From b50d4fee086e44b79789ee1faefabdb0b83f633c Mon Sep 17 00:00:00 2001 From: Arijit De Date: Wed, 10 Jun 2026 13:14:43 +0530 Subject: [PATCH 01/11] chore: update frontend dependencies and resolve build artifacts --- .gitignore | 3 +- GCP_Setup.md | 237 +- README.md | 95 +- backend/__init__.py | 1 + backend/auth.py | 130 + backend/config.py | 92 + backend/crew_runner.py | 255 + backend/database.py | 57 + backend/gcs.py | 159 + backend/main.py | 54 + backend/models.py | 112 + backend/requirements.txt | 16 + backend/routers/__init__.py | 1 + backend/routers/auth_router.py | 100 + backend/routers/github_router.py | 102 + backend/routers/job_router.py | 187 + backend/routers/resume_router.py | 165 + backend/schemas.py | 105 + frontend/.gitignore | 24 + frontend/README.md | 16 + frontend/eslint.config.js | 21 + frontend/index.html | 17 + frontend/package-lock.json | 4350 +++++++++++++++++ frontend/package.json | 33 + frontend/public/favicon.svg | 1 + frontend/public/icons.svg | 24 + frontend/src/App.jsx | 39 + frontend/src/api/client.js | 31 + frontend/src/assets/hero.png | Bin 0 -> 13057 bytes frontend/src/assets/react.svg | 1 + frontend/src/assets/vite.svg | 1 + frontend/src/components/GithubProfileAdd.jsx | 74 + frontend/src/components/LoginSection.jsx | 53 + frontend/src/components/Navbar.jsx | 41 + frontend/src/components/ProgressTracker.jsx | 98 + .../src/components/ResumePreviewModal.jsx | 55 + frontend/src/components/ResumeReadyModal.jsx | 59 + frontend/src/components/ResumeUpload.jsx | 113 + frontend/src/components/TailorResumeForm.jsx | 84 + .../src/components/TailoredResumeList.jsx | 76 + frontend/src/context/AuthContext.jsx | 52 + frontend/src/index.css | 450 ++ frontend/src/main.jsx | 16 + frontend/src/pages/HomePage.jsx | 102 + frontend/src/pages/InterviewMaterialsPage.jsx | 85 + frontend/src/pages/LandingPage.jsx | 181 + frontend/src/pages/RegisterPage.jsx | 99 + frontend/vite.config.js | 15 + github_repo_extractor.py | 18 +- package-lock.json | 6 + webapp_plan.md | 491 ++ 51 files changed, 8541 insertions(+), 56 deletions(-) create mode 100644 backend/__init__.py create mode 100644 backend/auth.py create mode 100644 backend/config.py create mode 100644 backend/crew_runner.py create mode 100644 backend/database.py create mode 100644 backend/gcs.py create mode 100644 backend/main.py create mode 100644 backend/models.py create mode 100644 backend/requirements.txt create mode 100644 backend/routers/__init__.py create mode 100644 backend/routers/auth_router.py create mode 100644 backend/routers/github_router.py create mode 100644 backend/routers/job_router.py create mode 100644 backend/routers/resume_router.py create mode 100644 backend/schemas.py create mode 100644 frontend/.gitignore create mode 100644 frontend/README.md create mode 100644 frontend/eslint.config.js create mode 100644 frontend/index.html create mode 100644 frontend/package-lock.json create mode 100644 frontend/package.json create mode 100644 frontend/public/favicon.svg create mode 100644 frontend/public/icons.svg create mode 100644 frontend/src/App.jsx create mode 100644 frontend/src/api/client.js create mode 100644 frontend/src/assets/hero.png create mode 100644 frontend/src/assets/react.svg create mode 100644 frontend/src/assets/vite.svg create mode 100644 frontend/src/components/GithubProfileAdd.jsx create mode 100644 frontend/src/components/LoginSection.jsx create mode 100644 frontend/src/components/Navbar.jsx create mode 100644 frontend/src/components/ProgressTracker.jsx create mode 100644 frontend/src/components/ResumePreviewModal.jsx create mode 100644 frontend/src/components/ResumeReadyModal.jsx create mode 100644 frontend/src/components/ResumeUpload.jsx create mode 100644 frontend/src/components/TailorResumeForm.jsx create mode 100644 frontend/src/components/TailoredResumeList.jsx create mode 100644 frontend/src/context/AuthContext.jsx create mode 100644 frontend/src/index.css create mode 100644 frontend/src/main.jsx create mode 100644 frontend/src/pages/HomePage.jsx create mode 100644 frontend/src/pages/InterviewMaterialsPage.jsx create mode 100644 frontend/src/pages/LandingPage.jsx create mode 100644 frontend/src/pages/RegisterPage.jsx create mode 100644 frontend/vite.config.js create mode 100644 package-lock.json create mode 100644 webapp_plan.md diff --git a/.gitignore b/.gitignore index ce4faee..c368141 100644 --- a/.gitignore +++ b/.gitignore @@ -163,4 +163,5 @@ cython_debug/ db/ crew_log.txt -project-69718baa-b6cc-44ec-9fb-17c2c8f0dd9c.json \ No newline at end of file +project-69718baa-b6cc-44ec-9fb-17c2c8f0dd9c.json +client_secret_720140080607-c0cuhvl4bvqtnk6tgicqds8136iqgrdv.apps.googleusercontent.com.json \ No newline at end of file diff --git a/GCP_Setup.md b/GCP_Setup.md index 7e22e49..7915807 100644 --- a/GCP_Setup.md +++ b/GCP_Setup.md @@ -1,86 +1,241 @@ # Google Cloud Platform (GCP) Setup Guide -This guide details the steps required to configure Google Cloud Platform (GCP) for the **Job Application Agent** project. The project relies on **Google BigQuery** as a vector database to store and search document embeddings and **Vertex AI** for generating semantic embeddings. +This guide details the steps required to configure Google Cloud Platform (GCP) for the **Job Application Agent** project. The project relies on: + +- **Google BigQuery** — vector database for GitHub repo embeddings +- **Vertex AI** — semantic embedding model (`text-embedding-005`) +- **Cloud SQL for MySQL** — relational database for user accounts, resumes, and job records +- **Cloud Storage (GCS)** — file storage for uploaded and tailored resumes --- ## 1. Prerequisites - A Google Cloud account. If you do not have one, sign up at [cloud.google.com](https://cloud.google.com/). - A billing account linked to your GCP account (BigQuery and Vertex AI require an active billing account, though they offer free tiers/credits). +- `gcloud` CLI installed and authenticated: `gcloud auth login` + +--- ## 2. Create a Google Cloud Project 1. Go to the [Google Cloud Console](https://console.cloud.google.com/). 2. Click on the project drop-down menu at the top of the page. 3. Click **New Project**. 4. Enter a project name (e.g., `job-application-agent`). -5. Note your **Project ID** (e.g., `inbound-byway-457408-c9` from the README, though yours will be unique). +5. Note your **Project ID** — you'll need this in your `.env` file. 6. Click **Create** and ensure your new project is selected in the console. +--- + ## 3. Enable Required APIs -You need to enable both the Vertex AI and BigQuery APIs for your project. +Navigate to **APIs & Services > Library** and enable all of the following: + +| API | Purpose | +|-----|---------| +| **Vertex AI API** | Semantic embeddings for GitHub repos | +| **BigQuery API** | Vector store for repo content | +| **Cloud SQL Admin API** | Managed MySQL database | +| **Cloud Storage API** | Resume file storage | +| **Cloud SQL Admin API** | SQL instance management | + +```bash +# Enable all required APIs via CLI +gcloud services enable aiplatform.googleapis.com \ + bigquery.googleapis.com \ + sqladmin.googleapis.com \ + storage.googleapis.com \ + --project=YOUR_PROJECT_ID +``` -1. Navigate to **APIs & Services > Library** in the left sidebar. -2. Search for **Agent Platform API** (formerly Vertex AI API). -3. Click on the result and click **Enable**. -4. Search for **BigQuery API**. -5. Click on the result and click **Enable**. +--- ## 4. Set Up BigQuery Dataset -The system will need a BigQuery dataset to store tables containing your GitHub repository data and their vector embeddings. +The system will store GitHub repo embeddings in BigQuery. Each user gets their own dataset (automatically named from their email). 1. Navigate to **BigQuery** from the GCP Console sidebar. 2. In the Explorer pane, click the three dots (`⋮`) next to your Project ID and select **Create dataset**. 3. Fill in the dataset details: - - **Dataset ID:** `job_applier_app` (or your preferred name, matching `GCP_DATASET_NAME` in `.env`). - - **Data location:** `asia-south2` (or your preferred region, matching `GCP_LOCATION` in `.env`). + - **Dataset ID:** `job_applier_app` (default dataset; user-specific ones are auto-created at runtime). + - **Data location:** `asia-south2` (must match `GCP_LOCATION` in `.env`). 4. Leave other settings as default and click **Create dataset**. -*(Note: The table `github_repo_data` will typically be created automatically by the LangChain/BigQuery vector store integration when you run the application, provided the dataset exists.)* -## 5. Create a Service Account and Generate JSON Key -To allow your local Python script to interact with GCP securely, you must use a Service Account. +> **Note:** User-specific datasets (e.g., `yourname_gmail_com_github_bq_db`) are created automatically at runtime by the web app. You only need to create the default dataset here. -1. Navigate to **IAM & Admin > Service Accounts** in the GCP Console. -2. Click **Create Service Account** at the top. -3. Provide a name (e.g., `job-agent-sa`) and a description. Click **Create and Continue**. -4. **Grant Access (Permissions):** You must assign the following roles to the service account so it can manage BigQuery data and call Vertex AI models: - - **BigQuery Admin** (or `BigQuery Data Editor` + `BigQuery Job User` for least privilege) - - **Vertex AI User** -5. Click **Continue** and then **Done**. -6. Find the newly created service account in the list, click the three dots (`⋮`) under the Actions column, and select **Manage keys**. -7. Click **Add Key > Create new key**. -8. Select **JSON** and click **Create**. -9. The JSON file will automatically download to your computer. Store this file securely and **DO NOT** commit it to version control (e.g., GitHub). +--- -## 6. Configure Environment Variables -Finally, update your local project to use the GCP resources and credentials you just created. +## 5. Create a Cloud SQL Instance (MySQL) -Create or update your `.env` file in the root of the `Job_Application_Agent` directory: +This is the relational database storing user accounts, resume metadata, and job records. + +### 5a. Create the instance + +```bash +gcloud sql instances create job-applier-mysql \ + --database-version=MYSQL_8_0 \ + --tier=db-f1-micro \ + --region=asia-south2 \ + --root-password=YOUR_SECURE_ROOT_PASSWORD \ + --storage-type=SSD \ + --storage-size=10GB \ + --project=YOUR_PROJECT_ID +``` + +> **Cost:** `db-f1-micro` costs approximately $7/month. + +### 5b. Create the application database + +```bash +gcloud sql databases create job_applier \ + --instance=job-applier-mysql \ + --project=YOUR_PROJECT_ID +``` + +### 5c. Authorize Local IP for Direct Connection + +Instead of running a proxy, authorize your local development machine's public IPv4 address to connect directly to the Cloud SQL instance: + +```bash +# Get your current public IPv4 address +curl -4 ifconfig.me + +# Authorize your IP in Cloud SQL +gcloud sql instances patch job-applier-mysql \ + --authorized-networks="YOUR_PUBLIC_IP/32" \ + --project=YOUR_PROJECT_ID +``` + +Set `MYSQL_HOST` in your `.env` to the Cloud SQL instance's public IP address (which can be retrieved using `gcloud sql instances list`). + +> **Note:** Database tables (`users`, `github_profiles`, `resumes`, `jobs`) are created automatically on first backend startup. + +--- + +## 6. Create a GCS Bucket + +This bucket stores uploaded and tailored resume files. + +```bash +# Create the bucket +gsutil mb \ + -l asia-south2 \ + -c STANDARD \ + gs://job-applier-YOUR_PROJECT_ID-resumes + +# Enable uniform bucket-level access (security best practice) +gsutil uniformbucketlevelaccess set on gs://job-applier-YOUR_PROJECT_ID-resumes +``` + +Replace `YOUR_PROJECT_ID` with your actual GCP project ID (e.g., `project-69718baa-b6cc-44ec-9fb`). + +--- + +## 7. Create a Service Account and Generate JSON Key + +```bash +# Create the service account +gcloud iam service-accounts create job-agent-sa \ + --description="Job Application Agent Service Account" \ + --display-name="Job Agent SA" \ + --project=YOUR_PROJECT_ID + +# Assign required roles +SA_EMAIL="job-agent-sa@YOUR_PROJECT_ID.iam.gserviceaccount.com" + +gcloud projects add-iam-policy-binding YOUR_PROJECT_ID \ + --member="serviceAccount:$SA_EMAIL" --role="roles/bigquery.admin" + +gcloud projects add-iam-policy-binding YOUR_PROJECT_ID \ + --member="serviceAccount:$SA_EMAIL" --role="roles/aiplatform.user" + +gcloud projects add-iam-policy-binding YOUR_PROJECT_ID \ + --member="serviceAccount:$SA_EMAIL" --role="roles/cloudsql.client" + +gcloud projects add-iam-policy-binding YOUR_PROJECT_ID \ + --member="serviceAccount:$SA_EMAIL" --role="roles/storage.objectAdmin" + +# Download the JSON key +gcloud iam service-accounts keys create ./service-account.json \ + --iam-account=$SA_EMAIL \ + --project=YOUR_PROJECT_ID +``` + +> **Security:** Store `service-account.json` securely and **never commit it to version control**. It is already listed in `.gitignore`. + +--- + +## 8. Set Up Google OAuth (Optional — for "Sign in with Google") + +1. Go to [GCP Console → APIs & Services → Credentials](https://console.cloud.google.com/apis/credentials). +2. Click **Create Credentials → OAuth 2.0 Client ID**. +3. If prompted, configure the **OAuth consent screen** first (External, fill in app name). +4. **Application type:** Web application. +5. **Authorized JavaScript origins:** `http://localhost:5173` (and your production domain). +6. **Authorized redirect URIs:** `http://localhost:5173`. +7. Click **Create** and copy the **Client ID** and **Client Secret**. + +--- + +## 9. Configure Environment Variables + +Update your `.env` file in the root of the project: ```env +# ── GCP Core ────────────────────────────────────────────────────── # Path to the downloaded Service Account JSON key -GOOGLE_APPLICATION_CREDENTIALS=/absolute/path/to/your/gcp-service-account.json +GOOGLE_APPLICATION_CREDENTIALS=/absolute/path/to/service-account.json # Your GCP Project ID GCP_PROJECT_ID=your-unique-project-id -# BigQuery Dataset Name -GCP_DATASET_NAME=job_applier_app - -# GCP Region (must match your dataset location) GCP_LOCATION=asia-south2 - -# BigQuery Table Name (will be created automatically if it doesn't exist) +GCP_DATASET_NAME=job_applier_app GCP_TABLE_NAME=github_repo_data -# Vertex AI Model for Embeddings -VERTEX_AI_MODEL=text-embedding-005 +# ── Cloud SQL MySQL ──────────────────────────────────────────────── +MYSQL_HOST=127.0.0.1 # Use 127.0.0.1 when Cloud SQL Auth Proxy is running +MYSQL_PORT=3306 +MYSQL_USER=root +MYSQL_PASSWORD=YOUR_SECURE_ROOT_PASSWORD +MYSQL_DATABASE=job_applier + +# ── Cloud Storage ────────────────────────────────────────────────── +GCS_BUCKET_NAME=job-applier-your-project-id-resumes + +# ── JWT Auth ─────────────────────────────────────────────────────── +# Generate a strong random key: python3 -c "import secrets; print(secrets.token_urlsafe(32))" +JWT_SECRET_KEY=CHANGE_ME_TO_A_RANDOM_SECRET +JWT_ALGORITHM=HS256 +JWT_EXPIRY_MINUTES=1440 + +# ── Google OAuth (optional) ──────────────────────────────────────── +GOOGLE_OAUTH_CLIENT_ID=your-client-id.apps.googleusercontent.com +GOOGLE_OAUTH_CLIENT_SECRET=your-client-secret + +# ── Existing API Keys ────────────────────────────────────────────── +BRIGHT_DATA_API_KEY=... +GITHUB_PERSONAL_ACCESS_TOKEN=... +GEMINI_API_KEY=... +SERPER_API_KEY=... ``` +--- + ## Summary Checklist + +### Existing (CLI pipeline) - [ ] GCP Project Created - [ ] Vertex AI API Enabled - [ ] BigQuery API Enabled -- [ ] BigQuery Dataset Created (`job_applier_app`) -- [ ] Service Account Created with roles (`BigQuery Admin`, `Vertex AI User`) -- [ ] Service Account JSON key downloaded -- [ ] `.env` file updated with valid GCP configuration +- [ ] BigQuery Dataset Created (`job_applier_app`, region `asia-south2`) +- [ ] Service Account created with roles: `BigQuery Admin`, `Vertex AI User` +- [ ] Service Account JSON key downloaded → path set in `GOOGLE_APPLICATION_CREDENTIALS` + +### New (Web App) +- [ ] Cloud SQL Admin API Enabled +- [ ] Cloud Storage API Enabled +- [ ] Cloud SQL instance created (`job-applier-mysql`, `db-f1-micro`, `asia-south2`) +- [ ] MySQL database created (`job_applier`) +- [ ] Local IPv4 authorized in Cloud SQL Instance settings +- [ ] GCS bucket created (`job-applier-YOUR_PROJECT_ID-resumes`) +- [ ] Service Account roles added: `Cloud SQL Client`, `Storage Object Admin` +- [ ] `.env` updated with `MYSQL_*`, `GCS_BUCKET_NAME`, `JWT_SECRET_KEY` +- [ ] Google OAuth credentials created (optional, for Google Sign-In) diff --git a/README.md b/README.md index 4eeb3a5..364946a 100644 --- a/README.md +++ b/README.md @@ -2,6 +2,8 @@ A powerful, multi-agent automation system for job applications, leveraging [Crew AI](https://www.crewai.com/) to orchestrate specialized agents that extract, analyze, and summarize job postings, tailor resumes, and prepare interview materials. The system integrates with Google BigQuery and Vertex AI for advanced document storage and semantic search, and features custom tools for LinkedIn and GitHub data extraction. +This project also includes a **Full-Stack Web App version** with a **FastAPI backend** (using Google Cloud SQL MySQL and GCS storage) and a **React + Vite frontend** that displays live agent execution progress via Server-Sent Events (SSE). + --- ## 🚀 Features @@ -56,23 +58,29 @@ A powerful, multi-agent automation system for job applications, leveraging [Crew ### 1. Clone the Repository +This is always the first step. Run: + ```bash git clone https://github.com/arijitde92/Job_Application_Agent.git cd Job_Application_Agent ``` -### 2. Install Dependencies +--- + +### Option A: Running the Standalone CLI Agent + +#### 1. Install Dependencies ```bash pip install -r requirements.txt ``` -### 3. Environment Variables +#### 2. Configure Environment Variables Create a `.env` file in the project root with the following variables: ```env -# GitHub API token (for higher rate limits/private repos) +# GitHub API token GITHUB_PERSONAL_ACCESS_TOKEN=your_github_token # Google Cloud Project details @@ -86,11 +94,7 @@ GCP_TABLE_NAME=github_repo_data VERTEX_AI_MODEL=text-embedding-005 ``` -**Note:** -- `GOOGLE_APPLICATION_CREDENTIALS` should point to your GCP service account JSON file with BigQuery and Vertex AI permissions. -- The default project, dataset, location, and table names are set in the code but can be overridden via environment variables. - -### 4. Run the Application +#### 3. Run the CLI Application ```bash python Job_Applier.py @@ -98,6 +102,79 @@ python Job_Applier.py --- +### Option B: Running the Full-Stack Web App + +#### 1. Setup Python Environment & Dependencies + +Create and activate a conda environment, then install the backend dependencies: + +```bash +conda create -n job_agent python=3.12 -y +conda activate job_agent +pip install -r requirements.txt +pip install -r backend/requirements.txt +``` + +#### 2. Install Node.js + +The frontend React application requires Node.js (v18+). If you do not have it installed, please download and install it from the official [Node.js Download Page](https://nodejs.org/en/download). + +#### 3. Setup Frontend Dependencies + +```bash +cd frontend +npm install +cd .. +``` + +#### 4. Configure Web App Environment Variables + +Add the following database and storage configurations to the bottom of your `.env` file: + +```env +# Cloud SQL MySQL (Direct Connection) +MYSQL_HOST=34.131.150.209 +MYSQL_PORT=3306 +MYSQL_USER=root +MYSQL_PASSWORD=JobAgent@2026 +MYSQL_DATABASE=job_applier + +# Google Cloud Storage (GCS) for Resumes +GCS_BUCKET_NAME=job-applier-project-69718baa-b6cc-44ec-9fb-resumes + +# JWT Authentication +# Generate using: python -c "import secrets; print(secrets.token_urlsafe(32))" +JWT_SECRET_KEY=drUVPzflrCADfaC7HjrydJYPPS_IpspPlXxjOELdfjI +JWT_ALGORITHM=HS256 +JWT_EXPIRY_MINUTES=1440 + +# Google OAuth (Optional — for Google Login) +GOOGLE_OAUTH_CLIENT_ID=your-client-id.apps.googleusercontent.com +GOOGLE_OAUTH_CLIENT_SECRET=your-client-secret +``` + +#### 5. Run the Web Application + +Start the backend API server and frontend development server in separate terminals: + +**Terminal 1 (Backend API):** + +```bash +conda activate job_agent +uvicorn backend.main:app --host 127.0.0.1 --port 8000 --reload +``` + +**Terminal 2 (Frontend React):** + +```bash +cd frontend +npm run dev +``` + +Open `http://localhost:5173` in your browser. + +--- + ## 🔑 API Keys & Cloud Setup - **GitHub:** [Create a Personal Access Token](https://github.com/settings/tokens) and set `GITHUB_PERSONAL_ACCESS_TOKEN` in your `.env` file. @@ -143,4 +220,4 @@ MIT License - [Google Cloud BigQuery](https://cloud.google.com/bigquery) - [Vertex AI](https://cloud.google.com/vertex-ai) - [LangChain](https://python.langchain.com/) -- [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/) \ No newline at end of file +- [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/) diff --git a/backend/__init__.py b/backend/__init__.py new file mode 100644 index 0000000..7f83169 --- /dev/null +++ b/backend/__init__.py @@ -0,0 +1 @@ +# Backend package diff --git a/backend/auth.py b/backend/auth.py new file mode 100644 index 0000000..e26252f --- /dev/null +++ b/backend/auth.py @@ -0,0 +1,130 @@ +""" +auth.py +------- +JWT token creation, password hashing (bcrypt), and authentication dependencies. +Includes Google OAuth ID token verification for sign-in with Google. +""" + +from datetime import datetime, timedelta, timezone +from typing import Optional + +from fastapi import Depends, HTTPException, status +from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials +from jose import JWTError, jwt +# ── Monkeypatch bcrypt/passlib compatibility issue ────────────────────────── +# bcrypt 5.0.0+ raises ValueError for passwords longer than 72 bytes. +# passlib's check_wrap_bug test passes a longer password, causing import failure. +try: + import bcrypt + _orig_hashpw = bcrypt.hashpw + def _patched_hashpw(password, salt): + if len(password) > 72: + password = password[:72] + return _orig_hashpw(password, salt) + bcrypt.hashpw = _patched_hashpw +except ImportError: + pass + +from passlib.context import CryptContext +from sqlalchemy import select +from sqlalchemy.ext.asyncio import AsyncSession + +from backend.config import get_settings +from backend.database import get_db +from backend.models import User + +settings = get_settings() + +# ── Password hashing ───────────────────────────────────────────────────────── +pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto") + +security = HTTPBearer() + + +def hash_password(plain: str) -> str: + """Hash a plaintext password with bcrypt.""" + return pwd_context.hash(plain) + + +def verify_password(plain: str, hashed: str) -> bool: + """Verify a plaintext password against a bcrypt hash.""" + return pwd_context.verify(plain, hashed) + + +# ── JWT tokens ──────────────────────────────────────────────────────────────── + +def create_access_token(user_id: int, email: str) -> str: + """Create a JWT access token with user_id and email in the payload.""" + expire = datetime.now(timezone.utc) + timedelta(minutes=settings.JWT_EXPIRY_MINUTES) + payload = { + "sub": str(user_id), + "email": email, + "exp": expire, + } + return jwt.encode(payload, settings.JWT_SECRET_KEY, algorithm=settings.JWT_ALGORITHM) + + +def decode_token(token: str) -> dict: + """Decode and validate a JWT token. Raises JWTError on failure.""" + return jwt.decode(token, settings.JWT_SECRET_KEY, algorithms=[settings.JWT_ALGORITHM]) + + +# ── FastAPI dependency: get current user ────────────────────────────────────── + +async def get_current_user( + credentials: HTTPAuthorizationCredentials = Depends(security), + db: AsyncSession = Depends(get_db), +) -> User: + """ + FastAPI dependency that extracts the JWT from the Authorization header, + validates it, and returns the corresponding User ORM object. + """ + token = credentials.credentials + credentials_exception = HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Invalid or expired authentication token", + headers={"WWW-Authenticate": "Bearer"}, + ) + + try: + payload = decode_token(token) + user_id: str = payload.get("sub") + if user_id is None: + raise credentials_exception + except JWTError: + raise credentials_exception + + result = await db.execute(select(User).where(User.id == int(user_id))) + user = result.scalar_one_or_none() + if user is None: + raise credentials_exception + + return user + + +# ── Google OAuth ID token verification ──────────────────────────────────────── + +async def verify_google_id_token(id_token_str: str) -> dict: + """ + Verify a Google OAuth ID token server-side using google-auth. + Returns the decoded token payload containing email, name, etc. + Raises HTTPException on invalid token. + """ + from google.oauth2 import id_token as google_id_token + from google.auth.transport import requests as google_requests + + try: + idinfo = google_id_token.verify_oauth2_token( + id_token_str, + google_requests.Request(), + settings.GOOGLE_OAUTH_CLIENT_ID, + ) + # Verify issuer + if idinfo["iss"] not in ("accounts.google.com", "https://accounts.google.com"): + raise ValueError("Invalid issuer") + return idinfo + except Exception as e: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail=f"Invalid Google ID token: {e}", + ) diff --git a/backend/config.py b/backend/config.py new file mode 100644 index 0000000..0842ce0 --- /dev/null +++ b/backend/config.py @@ -0,0 +1,92 @@ +""" +config.py +--------- +Application settings loaded from environment variables via Pydantic BaseSettings. +All existing env vars (GCP, Gemini, Bright Data, etc.) are preserved for +backward compatibility with the standalone CLI pipeline. +""" + +from pydantic_settings import BaseSettings +from pydantic import Field +from functools import lru_cache + + +class Settings(BaseSettings): + """Application configuration — sourced from .env or environment variables.""" + + # ── MySQL / Cloud SQL ───────────────────────────────────────────────── + MYSQL_HOST: str = Field(default="127.0.0.1", description="Cloud SQL MySQL host") + MYSQL_PORT: int = Field(default=3306, description="Cloud SQL MySQL port") + MYSQL_USER: str = Field(default="root", description="MySQL user") + MYSQL_PASSWORD: str = Field(default="", description="MySQL password") + MYSQL_DATABASE: str = Field(default="job_applier", description="MySQL database name") + + # ── GCS ──────────────────────────────────────────────────────────────── + GCS_BUCKET_NAME: str = Field( + default="job-applier-resumes", + description="GCS bucket for resume storage", + ) + + # ── JWT ──────────────────────────────────────────────────────────────── + JWT_SECRET_KEY: str = Field( + default="CHANGE_ME_IN_PRODUCTION", + description="Secret key for signing JWT tokens", + ) + JWT_ALGORITHM: str = Field(default="HS256") + JWT_EXPIRY_MINUTES: int = Field(default=1440, description="Token expiry in minutes (default 24h)") + + # ── Google OAuth ────────────────────────────────────────────────────── + GOOGLE_OAUTH_CLIENT_ID: str = Field(default="", description="Google OAuth 2.0 Client ID") + GOOGLE_OAUTH_CLIENT_SECRET: str = Field(default="", description="Google OAuth 2.0 Client Secret") + + # ── GCP (existing) ──────────────────────────────────────────────────── + GOOGLE_APPLICATION_CREDENTIALS: str = Field(default="", description="Path to GCP service account JSON") + GCP_PROJECT_ID: str = Field(default="", description="GCP project ID") + GCP_LOCATION: str = Field(default="asia-south2", description="GCP region") + GCP_DATASET_NAME: str = Field(default="job_applier_app", description="BigQuery dataset name") + GCP_TABLE_NAME: str = Field(default="github_repo_data", description="BigQuery table name") + + # ── API Keys (existing) ─────────────────────────────────────────────── + BRIGHT_DATA_API_KEY: str = Field(default="", description="Bright Data MCP API key") + GITHUB_PERSONAL_ACCESS_TOKEN: str = Field(default="", description="GitHub PAT") + GEMINI_API_KEY: str = Field(default="", description="Google Gemini API key") + SERPER_API_KEY: str = Field(default="", description="Serper search API key") + + # ── Resume Upload ───────────────────────────────────────────────────── + MAX_RESUME_SIZE_MB: int = Field(default=10, description="Max resume upload size in MB") + + @property + def mysql_url(self) -> str: + """Construct the async MySQL connection URL for SQLAlchemy.""" + import urllib.parse + encoded_password = urllib.parse.quote_plus(self.MYSQL_PASSWORD) + return ( + f"mysql+asyncmy://{self.MYSQL_USER}:{encoded_password}" + f"@{self.MYSQL_HOST}:{self.MYSQL_PORT}/{self.MYSQL_DATABASE}" + ) + + @property + def mysql_url_sync(self) -> str: + """Construct the sync MySQL connection URL for SQLAlchemy (used in migrations).""" + import urllib.parse + encoded_password = urllib.parse.quote_plus(self.MYSQL_PASSWORD) + return ( + f"mysql+pymysql://{self.MYSQL_USER}:{encoded_password}" + f"@{self.MYSQL_HOST}:{self.MYSQL_PORT}/{self.MYSQL_DATABASE}" + ) + + @property + def max_resume_bytes(self) -> int: + return self.MAX_RESUME_SIZE_MB * 1024 * 1024 + + model_config = { + "env_file": ".env", + "env_file_encoding": "utf-8", + "extra": "ignore", + } + + +@lru_cache() +def get_settings() -> Settings: + """Return a cached Settings instance (reads .env once).""" + return Settings() diff --git a/backend/crew_runner.py b/backend/crew_runner.py new file mode 100644 index 0000000..85b623f --- /dev/null +++ b/backend/crew_runner.py @@ -0,0 +1,255 @@ +""" +crew_runner.py +-------------- +Async wrapper for CrewAI crew execution with progress tracking via SSE. +Runs the existing agent pipeline in a thread pool to avoid blocking the async event loop. +""" + +import asyncio +import json +import os +import tempfile +from concurrent.futures import ThreadPoolExecutor +from datetime import datetime +from typing import Dict, Any + +from sqlalchemy import select, update +from sqlalchemy.ext.asyncio import AsyncSession + +from backend.config import get_settings +from backend.database import AsyncSessionLocal +from backend.gcs import download_file, upload_tailored_resume, upload_interview_materials +from logger import get_logger + +logger = get_logger(__name__) +settings = get_settings() + +# Thread pool for running synchronous crew code +_executor = ThreadPoolExecutor(max_workers=4) + +# In-memory progress store: {job_id: {"current_step": str, "error": str|None}} +progress_store: Dict[int, Dict[str, Any]] = {} + + +def _update_progress(job_id: int, step: str, error: str = None): + """Update the in-memory progress store for SSE broadcasting.""" + progress_store[job_id] = {"current_step": step, "error": error} + logger.info("crew_runner.py: Job %d → step: %s", job_id, step) + + +def _run_crew_sync( + job_id: int, user_id: int, user_email: str, + github_username: str, github_url: str, bq_dataset_name: str, + resume_content: str, job_url: str, +): + """ + Synchronous crew execution — runs in a thread. + This imports and uses the existing crew pipeline. + """ + from dotenv import load_dotenv + load_dotenv() + + # Step 1: Extract job details + _update_progress(job_id, "extracting_job_info") + try: + from webpage_extractor import extract_linkedin_job_details + job_details = extract_linkedin_job_details(job_url) + job_info = job_details.to_dict() + job_info.pop("about_company", None) + job_details_json = json.dumps(job_info, indent=2) + except Exception as e: + logger.error("crew_runner.py: Failed to extract job details for job %d: %s", job_id, e) + raise RuntimeError(f"Failed to extract job details: {e}") + + # Save extracted job details to DB + _save_job_details_sync(job_id, job_details) + + # Step 2: Write resume to temp file for crew to read + _update_progress(job_id, "searching_projects") + with tempfile.NamedTemporaryFile(mode="w", suffix=".md", delete=False, prefix="resume_") as f: + f.write(resume_content) + resume_path = f.name + + try: + # Step 3: Build crew and run + _update_progress(job_id, "building_profile") + + from crewai import Crew + from agents import ( + github_project_summarizer, profiler, resume_strategist, interview_preparer, + ) + from tasks import ( + github_summary_task, profile_task, resume_strategy_task, interview_preparation_task, + ) + + applicant_name = user_email.split("@")[0].replace(".", "_") + + # Build output paths for this job + resume_output = tempfile.mktemp(suffix="_resume.md", prefix=f"job{job_id}_") + interview_output = tempfile.mktemp(suffix="_interview.md", prefix=f"job{job_id}_") + + # Override output_file dynamically + resume_strategy_task.output_file = resume_output + interview_preparation_task.output_file = interview_output + + crew = Crew( + agents=[github_project_summarizer, profiler, resume_strategist, interview_preparer], + tasks=[github_summary_task, profile_task, resume_strategy_task, interview_preparation_task], + verbose=True, + ) + + _update_progress(job_id, "tailoring_resume") + + job_application_inputs = { + "applicant_name": applicant_name, + "job_posting_url": job_url, + "job_name": job_details.job_name, + "company_name": job_details.company_name, + "github_url": github_url, + "resume_path": resume_path, + "job_details_json": job_details_json, + "bq_dataset_name": bq_dataset_name, + } + + result = crew.kickoff(inputs=job_application_inputs) + logger.info("crew_runner.py: Crew execution completed for job %d", job_id) + + # Read output files + tailored_resume_content = "" + interview_content = "" + + if os.path.exists(resume_output): + with open(resume_output, "r") as f: + tailored_resume_content = f.read() + elif hasattr(result, "raw"): + tailored_resume_content = result.raw + + if os.path.exists(interview_output): + with open(interview_output, "r") as f: + interview_content = f.read() + + return { + "tailored_resume": tailored_resume_content, + "interview_materials": interview_content, + "job_details": job_details, + } + + finally: + # Clean up temp files + for path in [resume_path]: + if os.path.exists(path): + os.unlink(path) + + +def _save_job_details_sync(job_id: int, job_details): + """Save extracted job details to the database (sync, called from thread).""" + import asyncio + + async def _save(): + async with AsyncSessionLocal() as db: + await db.execute( + update( + __import__("backend.models", fromlist=["Job"]).Job + ).where( + __import__("backend.models", fromlist=["Job"]).Job.id == job_id + ).values( + job_name=job_details.job_name, + company_name=job_details.company_name, + location=job_details.location, + seniority_level=job_details.seniority_level, + employment_type=job_details.employment_type, + job_function=job_details.job_function, + industry=job_details.industry, + job_description=job_details.job_description, + requirements=job_details.requirements, + ) + ) + await db.commit() + + # Run async DB update from sync thread + loop = asyncio.new_event_loop() + try: + loop.run_until_complete(_save()) + finally: + loop.close() + + +async def run_crew_for_job( + job_id: int, user_id: int, user_email: str, + github_profile, resume_gcs_path: str, job_url: str, +): + """ + Async entry point for crew execution. Updates job status in DB. + Runs the synchronous crew pipeline in a thread pool executor. + """ + _update_progress(job_id, "pending") + + try: + # Download resume from GCS + resume_bytes = download_file(resume_gcs_path) + resume_content = resume_bytes.decode("utf-8") + + # Update status to processing + async with AsyncSessionLocal() as db: + from backend.models import Job + await db.execute( + update(Job).where(Job.id == job_id).values(status="processing") + ) + await db.commit() + + # Run crew in thread pool + loop = asyncio.get_event_loop() + result = await loop.run_in_executor( + _executor, _run_crew_sync, + job_id, user_id, user_email, + github_profile.github_username, github_profile.github_url, + github_profile.bq_dataset_name, resume_content, job_url, + ) + + # Upload results to GCS + _update_progress(job_id, "uploading_results") + + tailored_gcs = None + interview_gcs = None + + if result["tailored_resume"]: + tailored_gcs = upload_tailored_resume( + user_id, job_id, result["tailored_resume"], "resume.md" + ) + if result["interview_materials"]: + interview_gcs = upload_interview_materials( + user_id, job_id, result["interview_materials"], "interview.md" + ) + + # Update job record + async with AsyncSessionLocal() as db: + from backend.models import Job + await db.execute( + update(Job).where(Job.id == job_id).values( + status="completed", + tailored_resume_gcs_path=tailored_gcs, + interview_materials_gcs_path=interview_gcs, + completed_at=datetime.utcnow(), + ) + ) + await db.commit() + + _update_progress(job_id, "completed") + logger.info("crew_runner.py: Job %d completed successfully", job_id) + + except Exception as e: + logger.error("crew_runner.py: Job %d failed: %s", job_id, e, exc_info=True) + _update_progress(job_id, "failed", error=str(e)) + + # Update job status to failed + try: + async with AsyncSessionLocal() as db: + from backend.models import Job + await db.execute( + update(Job).where(Job.id == job_id).values( + status="failed", error_message=str(e)[:2000], + ) + ) + await db.commit() + except Exception as db_err: + logger.error("crew_runner.py: Failed to update job %d status: %s", job_id, db_err) diff --git a/backend/database.py b/backend/database.py new file mode 100644 index 0000000..61c8e19 --- /dev/null +++ b/backend/database.py @@ -0,0 +1,57 @@ +""" +database.py +----------- +SQLAlchemy async engine, session factory, and dependency injection for FastAPI. +Uses asyncmy driver for Cloud SQL MySQL connectivity. +""" + +from sqlalchemy.ext.asyncio import create_async_engine, async_sessionmaker, AsyncSession +from sqlalchemy.orm import DeclarativeBase + +from backend.config import get_settings + +# ── Monkeypatch SQLAlchemy asyncmy ping issue ────────────────────────────── +# In PyMySQL 1.2.0+, ping() defaults reconnect to False. +# SQLAlchemy's asyncmy adapter signature requires reconnect to be explicitly passed, +# but SQLAlchemy's pymysql base dialect calls ping() without args when _send_false_to_ping is False. +try: + from sqlalchemy.dialects.mysql.asyncmy import AsyncAdapt_asyncmy_connection + _orig_ping = AsyncAdapt_asyncmy_connection.ping + def _patched_ping(self, reconnect: bool = False) -> None: + return _orig_ping(self, reconnect) + AsyncAdapt_asyncmy_connection.ping = _patched_ping +except ImportError: + pass + +settings = get_settings() + +# ── Async engine ────────────────────────────────────────────────────────────── +engine = create_async_engine( + settings.mysql_url, + echo=False, + pool_pre_ping=True, + pool_size=10, + max_overflow=20, +) + +# ── Session factory ─────────────────────────────────────────────────────────── +AsyncSessionLocal = async_sessionmaker( + bind=engine, + class_=AsyncSession, + expire_on_commit=False, +) + + +# ── Declarative Base ────────────────────────────────────────────────────────── +class Base(DeclarativeBase): + pass + + +# ── Dependency for FastAPI routes ───────────────────────────────────────────── +async def get_db() -> AsyncSession: + """Yield an async DB session and ensure it is closed after use.""" + async with AsyncSessionLocal() as session: + try: + yield session + finally: + await session.close() diff --git a/backend/gcs.py b/backend/gcs.py new file mode 100644 index 0000000..a48dc41 --- /dev/null +++ b/backend/gcs.py @@ -0,0 +1,159 @@ +""" +gcs.py +------ +Google Cloud Storage helpers for uploading, downloading, and generating +signed URLs for resume and interview material files. + +Bucket structure: + gs:///uploads//.md — User-uploaded original resumes + gs:///tailored//_resume.md — Agent-generated tailored resumes + gs:///tailored//_interview.md — Agent-generated interview materials +""" + +import datetime +from google.cloud import storage + +from backend.config import get_settings + +settings = get_settings() + + +def _get_client() -> storage.Client: + """Return a GCS client.""" + return storage.Client(project=settings.GCP_PROJECT_ID) + + +def _get_bucket(): + """Return the configured GCS bucket.""" + client = _get_client() + return client.bucket(settings.GCS_BUCKET_NAME) + + +# ── Upload functions ────────────────────────────────────────────────────────── + +def upload_resume(user_id: int, file_bytes: bytes, filename: str) -> str: + """ + Upload a user's original resume to GCS. + + Args: + user_id: The user's database ID. + file_bytes: Raw file content. + filename: Original filename. + + Returns: + The GCS path (gs://bucket/uploads//). + + Raises: + ValueError: If file exceeds max size. + """ + if len(file_bytes) > settings.max_resume_bytes: + raise ValueError( + f"File too large. Maximum allowed size is {settings.MAX_RESUME_SIZE_MB} MB." + ) + + blob_path = f"uploads/{user_id}/{filename}" + bucket = _get_bucket() + blob = bucket.blob(blob_path) + blob.upload_from_string(file_bytes, content_type="text/markdown") + + gcs_path = f"gs://{settings.GCS_BUCKET_NAME}/{blob_path}" + return gcs_path + + +def upload_tailored_resume(user_id: int, job_id: int, content: str, filename: str) -> str: + """ + Upload an agent-generated tailored resume to GCS. + + Args: + user_id: The user's database ID. + job_id: The job record ID. + content: Markdown content of the tailored resume. + filename: Desired filename. + + Returns: + The GCS path. + """ + blob_path = f"tailored/{user_id}/{job_id}_{filename}" + bucket = _get_bucket() + blob = bucket.blob(blob_path) + blob.upload_from_string(content.encode("utf-8"), content_type="text/markdown") + + return f"gs://{settings.GCS_BUCKET_NAME}/{blob_path}" + + +def upload_interview_materials(user_id: int, job_id: int, content: str, filename: str) -> str: + """ + Upload agent-generated interview materials to GCS. + + Args: + user_id: The user's database ID. + job_id: The job record ID. + content: Markdown content of the interview materials. + filename: Desired filename. + + Returns: + The GCS path. + """ + blob_path = f"tailored/{user_id}/{job_id}_{filename}" + bucket = _get_bucket() + blob = bucket.blob(blob_path) + blob.upload_from_string(content.encode("utf-8"), content_type="text/markdown") + + return f"gs://{settings.GCS_BUCKET_NAME}/{blob_path}" + + +# ── Download functions ──────────────────────────────────────────────────────── + +def download_file(gcs_path: str) -> bytes: + """ + Download a file from GCS. + + Args: + gcs_path: Full GCS path (gs://bucket/path/to/file). + + Returns: + File content as bytes. + """ + # Parse gs://bucket/path + path = gcs_path.replace(f"gs://{settings.GCS_BUCKET_NAME}/", "") + bucket = _get_bucket() + blob = bucket.blob(path) + return blob.download_as_bytes() + + +def delete_file(gcs_path: str) -> None: + """ + Delete a file from GCS. + + Args: + gcs_path: Full GCS path (gs://bucket/path/to/file). + """ + path = gcs_path.replace(f"gs://{settings.GCS_BUCKET_NAME}/", "") + bucket = _get_bucket() + blob = bucket.blob(path) + blob.delete() + + +# ── Signed URL ──────────────────────────────────────────────────────────────── + +def generate_signed_url(gcs_path: str, expiry_minutes: int = 15) -> str: + """ + Generate a signed URL for temporary access to a GCS file. + + Args: + gcs_path: Full GCS path. + expiry_minutes: URL validity duration in minutes. + + Returns: + A signed URL string. + """ + path = gcs_path.replace(f"gs://{settings.GCS_BUCKET_NAME}/", "") + bucket = _get_bucket() + blob = bucket.blob(path) + + url = blob.generate_signed_url( + version="v4", + expiration=datetime.timedelta(minutes=expiry_minutes), + method="GET", + ) + return url diff --git a/backend/main.py b/backend/main.py new file mode 100644 index 0000000..822f447 --- /dev/null +++ b/backend/main.py @@ -0,0 +1,54 @@ +""" +main.py +------- +FastAPI application entry point with CORS, lifespan events, and router includes. +""" + +from contextlib import asynccontextmanager +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware + +from backend.database import engine, Base +from backend.routers import auth_router, github_router, resume_router, job_router + + +@asynccontextmanager +async def lifespan(app: FastAPI): + """Create database tables on startup.""" + async with engine.begin() as conn: + await conn.run_sync(Base.metadata.create_all) + yield + await engine.dispose() + + +app = FastAPI( + title="Job Application Agent API", + description="AI-powered resume tailoring and interview preparation", + version="1.0.0", + lifespan=lifespan, +) + +# CORS — allow frontend dev server and production origins +app.add_middleware( + CORSMiddleware, + allow_origins=[ + "http://localhost:5173", # Vite dev server + "http://localhost:3000", + "http://127.0.0.1:5173", + ], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +# Include routers +app.include_router(auth_router.router) +app.include_router(github_router.router) +app.include_router(resume_router.router) +app.include_router(job_router.router) + + +@app.get("/api/health") +async def health_check(): + """Health check endpoint.""" + return {"status": "healthy", "service": "job-application-agent"} diff --git a/backend/models.py b/backend/models.py new file mode 100644 index 0000000..ea3723e --- /dev/null +++ b/backend/models.py @@ -0,0 +1,112 @@ +""" +models.py +--------- +SQLAlchemy ORM models for the Job Application Agent web app. + +Tables: + - users: User accounts (email/password or OAuth) + - github_profiles: Linked GitHub accounts per user + - resumes: Uploaded resume files tracked in GCS + - jobs: Crew execution records with status tracking +""" + +from datetime import datetime +from sqlalchemy import ( + Column, Integer, String, Text, Enum, ForeignKey, DateTime, UniqueConstraint, Index +) +from sqlalchemy.orm import relationship +from backend.database import Base + + +class User(Base): + __tablename__ = "users" + + id = Column(Integer, primary_key=True, autoincrement=True) + first_name = Column(String(100), nullable=False) + last_name = Column(String(100), nullable=False) + email = Column(String(255), nullable=False, unique=True) + password_hash = Column(String(255), nullable=False, default="") # Empty for OAuth-only users + created_at = Column(DateTime, default=datetime.utcnow) + updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow) + + # Relationships + github_profiles = relationship("GithubProfile", back_populates="user", cascade="all, delete-orphan") + resumes = relationship("Resume", back_populates="user", cascade="all, delete-orphan") + jobs = relationship("Job", back_populates="user", cascade="all, delete-orphan") + + __table_args__ = ( + Index("idx_email", "email"), + ) + + +class GithubProfile(Base): + __tablename__ = "github_profiles" + + id = Column(Integer, primary_key=True, autoincrement=True) + user_id = Column(Integer, ForeignKey("users.id", ondelete="CASCADE"), nullable=False) + github_username = Column(String(100), nullable=False) + github_url = Column(String(500), nullable=False) + bq_dataset_name = Column(String(255), nullable=False) # e.g. "user_email_github_bq_db" + created_at = Column(DateTime, default=datetime.utcnow) + + # Relationships + user = relationship("User", back_populates="github_profiles") + jobs = relationship("Job", back_populates="github_profile") + + __table_args__ = ( + UniqueConstraint("user_id", "github_username", name="uq_user_github"), + ) + + +class Resume(Base): + __tablename__ = "resumes" + + id = Column(Integer, primary_key=True, autoincrement=True) + user_id = Column(Integer, ForeignKey("users.id", ondelete="CASCADE"), nullable=False) + original_filename = Column(String(255), nullable=False) + gcs_path = Column(String(500), nullable=False) # gs://bucket/uploads//.md + uploaded_at = Column(DateTime, default=datetime.utcnow) + + # Relationships + user = relationship("User", back_populates="resumes") + jobs = relationship("Job", back_populates="resume") + + +class Job(Base): + __tablename__ = "jobs" + + id = Column(Integer, primary_key=True, autoincrement=True) + user_id = Column(Integer, ForeignKey("users.id", ondelete="CASCADE"), nullable=False) + github_profile_id = Column(Integer, ForeignKey("github_profiles.id", ondelete="RESTRICT"), nullable=False) + resume_id = Column(Integer, ForeignKey("resumes.id", ondelete="RESTRICT"), nullable=False) + linkedin_job_url = Column(String(500), nullable=False) + + # Extracted job details + job_name = Column(String(255), nullable=True) + company_name = Column(String(255), nullable=True) + location = Column(String(255), nullable=True) + seniority_level = Column(String(100), nullable=True) + employment_type = Column(String(100), nullable=True) + job_function = Column(String(255), nullable=True) + industry = Column(String(255), nullable=True) + job_description = Column(Text, nullable=True) + requirements = Column(Text, nullable=True) + + # Output paths + tailored_resume_gcs_path = Column(String(500), nullable=True) + interview_materials_gcs_path = Column(String(500), nullable=True) + + # Status tracking + status = Column( + Enum("pending", "processing", "completed", "failed", name="job_status"), + default="pending", + nullable=False, + ) + error_message = Column(Text, nullable=True) + created_at = Column(DateTime, default=datetime.utcnow) + completed_at = Column(DateTime, nullable=True) + + # Relationships + user = relationship("User", back_populates="jobs") + github_profile = relationship("GithubProfile", back_populates="jobs") + resume = relationship("Resume", back_populates="jobs") diff --git a/backend/requirements.txt b/backend/requirements.txt new file mode 100644 index 0000000..c0f7acf --- /dev/null +++ b/backend/requirements.txt @@ -0,0 +1,16 @@ +# Backend dependencies +fastapi>=0.115.0 +uvicorn[standard]>=0.30.0 +sqlalchemy[asyncio]>=2.0 +asyncmy>=0.2.9 +pymysql>=1.1.0 +pydantic>=2.0 +pydantic-settings>=2.0 +python-jose[cryptography]>=3.3.0 +passlib[bcrypt]>=1.7.4 +python-multipart>=0.0.7 +google-cloud-storage>=2.14.0 +google-auth>=2.25.0 +python-dotenv>=1.0.0 +pydantic[email]>=2.0 +sse-starlette>=1.8.0 diff --git a/backend/routers/__init__.py b/backend/routers/__init__.py new file mode 100644 index 0000000..873f7bb --- /dev/null +++ b/backend/routers/__init__.py @@ -0,0 +1 @@ +# Routers package diff --git a/backend/routers/auth_router.py b/backend/routers/auth_router.py new file mode 100644 index 0000000..9e3afe9 --- /dev/null +++ b/backend/routers/auth_router.py @@ -0,0 +1,100 @@ +""" +auth_router.py +-------------- +Authentication endpoints: register, login, Google OAuth, and current user profile. +""" + +from fastapi import APIRouter, Depends, HTTPException, status +from sqlalchemy import select +from sqlalchemy.ext.asyncio import AsyncSession + +from backend.database import get_db +from backend.models import User +from backend.schemas import ( + RegisterRequest, LoginRequest, GoogleOAuthRequest, + TokenResponse, UserResponse, +) +from backend.auth import ( + hash_password, verify_password, create_access_token, + get_current_user, verify_google_id_token, +) + +router = APIRouter(prefix="/api/auth", tags=["auth"]) + + +@router.post("/register", response_model=TokenResponse, status_code=status.HTTP_201_CREATED) +async def register(req: RegisterRequest, db: AsyncSession = Depends(get_db)): + """Register a new user account with email and password.""" + # Check for duplicate email + existing = await db.execute(select(User).where(User.email == req.email)) + if existing.scalar_one_or_none(): + raise HTTPException( + status_code=status.HTTP_409_CONFLICT, + detail="An account with this email already exists.", + ) + + user = User( + first_name=req.first_name, + last_name=req.last_name, + email=req.email, + password_hash=hash_password(req.password), + ) + db.add(user) + await db.commit() + await db.refresh(user) + + token = create_access_token(user.id, user.email) + return TokenResponse(access_token=token) + + +@router.post("/login", response_model=TokenResponse) +async def login(req: LoginRequest, db: AsyncSession = Depends(get_db)): + """Login with email and password, returns a JWT token.""" + result = await db.execute(select(User).where(User.email == req.email)) + user = result.scalar_one_or_none() + + if not user or not user.password_hash or not verify_password(req.password, user.password_hash): + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Invalid email or password.", + ) + + token = create_access_token(user.id, user.email) + return TokenResponse(access_token=token) + + +@router.post("/google", response_model=TokenResponse) +async def google_oauth_login(req: GoogleOAuthRequest, db: AsyncSession = Depends(get_db)): + """ + Login or register via Google OAuth. + Verifies the Google ID token server-side, creates a user if first login. + """ + idinfo = await verify_google_id_token(req.id_token) + email = idinfo.get("email") + if not email: + raise HTTPException(status_code=400, detail="Google token missing email claim.") + + # Find or create user + result = await db.execute(select(User).where(User.email == email)) + user = result.scalar_one_or_none() + + if not user: + # First-time Google OAuth login — create account + user = User( + first_name=idinfo.get("given_name", ""), + last_name=idinfo.get("family_name", ""), + email=email, + password_hash="", # OAuth-only user, no password + ) + db.add(user) + await db.commit() + await db.refresh(user) + + token = create_access_token(user.id, user.email) + return TokenResponse(access_token=token) + + +@router.get("/me", response_model=UserResponse) +async def get_me(current_user: User = Depends(get_current_user)): + """Return the currently authenticated user's profile.""" + return current_user diff --git a/backend/routers/github_router.py b/backend/routers/github_router.py new file mode 100644 index 0000000..2d8e1c5 --- /dev/null +++ b/backend/routers/github_router.py @@ -0,0 +1,102 @@ +""" +github_router.py +---------------- +CRUD endpoints for managing GitHub profiles linked to a user account. +""" + +import re +from fastapi import APIRouter, Depends, HTTPException, status +from sqlalchemy import select +from sqlalchemy.ext.asyncio import AsyncSession + +from backend.database import get_db +from backend.models import User, GithubProfile +from backend.schemas import GithubProfileCreate, GithubProfileResponse +from backend.auth import get_current_user + +router = APIRouter(prefix="/api/github", tags=["github"]) + + +def _sanitize_dataset_name(email: str) -> str: + """ + Generate a BigQuery-safe dataset name from a user's email. + Replaces all non-alphanumeric characters with underscores. + Example: arijitde2050@gmail.com → arijitde2050_gmail_com_github_bq_db + """ + sanitized = re.sub(r"[^a-zA-Z0-9]", "_", email) + return f"{sanitized}_github_bq_db" + + +@router.post("", response_model=GithubProfileResponse, status_code=status.HTTP_201_CREATED) +async def add_github_profile( + req: GithubProfileCreate, + current_user: User = Depends(get_current_user), + db: AsyncSession = Depends(get_db), +): + """Add a GitHub profile (username) for the authenticated user.""" + # Check for duplicate + existing = await db.execute( + select(GithubProfile).where( + GithubProfile.user_id == current_user.id, + GithubProfile.github_username == req.github_username, + ) + ) + if existing.scalar_one_or_none(): + raise HTTPException( + status_code=status.HTTP_409_CONFLICT, + detail=f"GitHub username '{req.github_username}' is already linked to your account.", + ) + + profile = GithubProfile( + user_id=current_user.id, + github_username=req.github_username, + github_url=f"https://github.com/{req.github_username}", + bq_dataset_name=_sanitize_dataset_name(current_user.email), + ) + db.add(profile) + await db.commit() + await db.refresh(profile) + return profile + + +@router.get("", response_model=list[GithubProfileResponse]) +async def list_github_profiles( + current_user: User = Depends(get_current_user), + db: AsyncSession = Depends(get_db), +): + """List all GitHub profiles for the authenticated user.""" + result = await db.execute( + select(GithubProfile).where(GithubProfile.user_id == current_user.id) + ) + return result.scalars().all() + + +@router.delete("/{profile_id}", status_code=status.HTTP_204_NO_CONTENT) +async def delete_github_profile( + profile_id: int, + current_user: User = Depends(get_current_user), + db: AsyncSession = Depends(get_db), +): + """ + Delete a GitHub profile. Fails if the profile has been used in a job + (ON DELETE RESTRICT enforced at the DB level). + """ + result = await db.execute( + select(GithubProfile).where( + GithubProfile.id == profile_id, + GithubProfile.user_id == current_user.id, + ) + ) + profile = result.scalar_one_or_none() + if not profile: + raise HTTPException(status_code=404, detail="GitHub profile not found.") + + try: + await db.delete(profile) + await db.commit() + except Exception: + await db.rollback() + raise HTTPException( + status_code=status.HTTP_409_CONFLICT, + detail="Cannot delete this GitHub profile — it has been used in a job application.", + ) diff --git a/backend/routers/job_router.py b/backend/routers/job_router.py new file mode 100644 index 0000000..f326df2 --- /dev/null +++ b/backend/routers/job_router.py @@ -0,0 +1,187 @@ +""" +job_router.py +------------- +Endpoints for starting crew execution, listing jobs, checking status/progress, +downloading tailored resumes and interview materials. +""" + +import asyncio +from fastapi import APIRouter, Depends, HTTPException, status +from fastapi.responses import StreamingResponse, Response +from sqlalchemy import select +from sqlalchemy.ext.asyncio import AsyncSession + +from backend.database import get_db +from backend.models import User, Job, GithubProfile, Resume +from backend.schemas import TailorJobRequest, JobResponse, JobDetailResponse +from backend.auth import get_current_user +from backend.gcs import download_file +from backend.crew_runner import run_crew_for_job, progress_store + +router = APIRouter(prefix="/api/jobs", tags=["jobs"]) + + +@router.post("/tailor", response_model=JobResponse, status_code=status.HTTP_201_CREATED) +async def tailor_resume( + req: TailorJobRequest, + current_user: User = Depends(get_current_user), + db: AsyncSession = Depends(get_db), +): + """Start a crew execution to tailor a resume for a job posting.""" + gh_result = await db.execute( + select(GithubProfile).where( + GithubProfile.id == req.github_profile_id, + GithubProfile.user_id == current_user.id, + ) + ) + github_profile = gh_result.scalar_one_or_none() + if not github_profile: + raise HTTPException(status_code=404, detail="GitHub profile not found.") + + resume_result = await db.execute( + select(Resume).where(Resume.id == req.resume_id, Resume.user_id == current_user.id) + ) + resume = resume_result.scalar_one_or_none() + if not resume: + raise HTTPException(status_code=404, detail="Resume not found.") + + job = Job( + user_id=current_user.id, + github_profile_id=req.github_profile_id, + resume_id=req.resume_id, + linkedin_job_url=req.linkedin_job_url, + status="pending", + ) + db.add(job) + await db.commit() + await db.refresh(job) + + asyncio.create_task( + run_crew_for_job( + job_id=job.id, user_id=current_user.id, user_email=current_user.email, + github_profile=github_profile, resume_gcs_path=resume.gcs_path, + job_url=req.linkedin_job_url, + ) + ) + return job + + +@router.get("", response_model=list[JobResponse]) +async def list_jobs( + current_user: User = Depends(get_current_user), + db: AsyncSession = Depends(get_db), +): + """List all jobs for the authenticated user, newest first.""" + result = await db.execute( + select(Job).where(Job.user_id == current_user.id).order_by(Job.created_at.desc()) + ) + return result.scalars().all() + + +@router.get("/{job_id}", response_model=JobDetailResponse) +async def get_job_detail( + job_id: int, current_user: User = Depends(get_current_user), + db: AsyncSession = Depends(get_db), +): + """Get detailed information about a specific job.""" + result = await db.execute( + select(Job).where(Job.id == job_id, Job.user_id == current_user.id) + ) + job = result.scalar_one_or_none() + if not job: + raise HTTPException(status_code=404, detail="Job not found.") + return job + + +@router.get("/{job_id}/progress") +async def stream_job_progress( + job_id: int, current_user: User = Depends(get_current_user), + db: AsyncSession = Depends(get_db), +): + """SSE endpoint for real-time crew execution progress.""" + result = await db.execute( + select(Job).where(Job.id == job_id, Job.user_id == current_user.id) + ) + if not result.scalar_one_or_none(): + raise HTTPException(status_code=404, detail="Job not found.") + + async def event_stream(): + last_step = None + while True: + progress = progress_store.get(job_id, {}) + current_step = progress.get("current_step", "pending") + error = progress.get("error") + if current_step != last_step: + last_step = current_step + err_str = f'"{error}"' if error else "null" + yield f'data: {{"step": "{current_step}", "error": {err_str}}}\n\n' + if current_step in ("completed", "failed"): + break + await asyncio.sleep(1) + + return StreamingResponse( + event_stream(), media_type="text/event-stream", + headers={"Cache-Control": "no-cache", "Connection": "keep-alive", "X-Accel-Buffering": "no"}, + ) + + +@router.get("/{job_id}/resume/download") +async def download_tailored_resume( + job_id: int, current_user: User = Depends(get_current_user), + db: AsyncSession = Depends(get_db), +): + """Download the tailored resume .md file.""" + result = await db.execute( + select(Job).where(Job.id == job_id, Job.user_id == current_user.id) + ) + job = result.scalar_one_or_none() + if not job: + raise HTTPException(status_code=404, detail="Job not found.") + if not job.tailored_resume_gcs_path: + raise HTTPException(status_code=404, detail="Tailored resume not available yet.") + content = download_file(job.tailored_resume_gcs_path) + fname = f"{(job.company_name or 'tailored')}_{(job.job_name or 'resume')}_resume.md".replace(" ", "_") + return Response(content=content, media_type="text/markdown", + headers={"Content-Disposition": f'attachment; filename="{fname}"'}) + + +@router.get("/{job_id}/interview/download") +async def download_interview_materials( + job_id: int, current_user: User = Depends(get_current_user), + db: AsyncSession = Depends(get_db), +): + """Download the interview materials .md file.""" + result = await db.execute( + select(Job).where(Job.id == job_id, Job.user_id == current_user.id) + ) + job = result.scalar_one_or_none() + if not job: + raise HTTPException(status_code=404, detail="Job not found.") + if not job.interview_materials_gcs_path: + raise HTTPException(status_code=404, detail="Interview materials not available yet.") + content = download_file(job.interview_materials_gcs_path) + fname = f"{(job.company_name or 'interview')}_{(job.job_name or 'prep')}_interview.md".replace(" ", "_") + return Response(content=content, media_type="text/markdown", + headers={"Content-Disposition": f'attachment; filename="{fname}"'}) + + +@router.get("/{job_id}/interview/content") +async def get_interview_content( + job_id: int, current_user: User = Depends(get_current_user), + db: AsyncSession = Depends(get_db), +): + """Get interview materials markdown content for in-browser viewing.""" + result = await db.execute( + select(Job).where(Job.id == job_id, Job.user_id == current_user.id) + ) + job = result.scalar_one_or_none() + if not job: + raise HTTPException(status_code=404, detail="Job not found.") + if not job.interview_materials_gcs_path: + raise HTTPException(status_code=404, detail="Interview materials not available yet.") + content = download_file(job.interview_materials_gcs_path) + return { + "content": content.decode("utf-8"), "job_name": job.job_name, + "company_name": job.company_name, + "created_at": job.created_at.isoformat() if job.created_at else None, + } diff --git a/backend/routers/resume_router.py b/backend/routers/resume_router.py new file mode 100644 index 0000000..45c5556 --- /dev/null +++ b/backend/routers/resume_router.py @@ -0,0 +1,165 @@ +""" +resume_router.py +---------------- +Endpoints for uploading, listing, previewing, and deleting resumes. +""" + +from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, status +from sqlalchemy import select +from sqlalchemy.ext.asyncio import AsyncSession + +from backend.database import get_db +from backend.models import User, Resume +from backend.schemas import ResumeResponse +from backend.auth import get_current_user +from backend.gcs import upload_resume, generate_signed_url, download_file, delete_file +from backend.config import get_settings + +settings = get_settings() +router = APIRouter(prefix="/api/resumes", tags=["resumes"]) + + +@router.post("/upload", response_model=ResumeResponse, status_code=status.HTTP_201_CREATED) +async def upload_resume_file( + file: UploadFile = File(...), + current_user: User = Depends(get_current_user), + db: AsyncSession = Depends(get_db), +): + """Upload a .md resume file to GCS.""" + # Validate file extension + if not file.filename or not file.filename.endswith(".md"): + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail="Only Markdown (.md) files are accepted.", + ) + + # Read file content + file_bytes = await file.read() + + # Validate file size + if len(file_bytes) > settings.max_resume_bytes: + raise HTTPException( + status_code=status.HTTP_413_REQUEST_ENTITY_TOO_LARGE, + detail=f"File too large. Maximum allowed size is {settings.MAX_RESUME_SIZE_MB} MB.", + ) + + # Upload to GCS + try: + gcs_path = upload_resume(current_user.id, file_bytes, file.filename) + except Exception as e: + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail=f"Failed to upload resume: {e}", + ) + + # Create DB record + resume = Resume( + user_id=current_user.id, + original_filename=file.filename, + gcs_path=gcs_path, + ) + db.add(resume) + await db.commit() + await db.refresh(resume) + return resume + + +@router.get("", response_model=list[ResumeResponse]) +async def list_resumes( + current_user: User = Depends(get_current_user), + db: AsyncSession = Depends(get_db), +): + """List all resumes uploaded by the authenticated user.""" + result = await db.execute( + select(Resume).where(Resume.user_id == current_user.id).order_by(Resume.uploaded_at.desc()) + ) + return result.scalars().all() + + +@router.get("/{resume_id}/preview") +async def preview_resume( + resume_id: int, + current_user: User = Depends(get_current_user), + db: AsyncSession = Depends(get_db), +): + """Get a signed URL for previewing a resume.""" + result = await db.execute( + select(Resume).where( + Resume.id == resume_id, + Resume.user_id == current_user.id, + ) + ) + resume = result.scalar_one_or_none() + if not resume: + raise HTTPException(status_code=404, detail="Resume not found.") + + try: + signed_url = generate_signed_url(resume.gcs_path) + return {"signed_url": signed_url} + except Exception as e: + raise HTTPException( + status_code=500, + detail=f"Failed to generate preview URL: {e}", + ) + + +@router.get("/{resume_id}/content") +async def get_resume_content( + resume_id: int, + current_user: User = Depends(get_current_user), + db: AsyncSession = Depends(get_db), +): + """Get the raw markdown content of a resume for in-browser rendering.""" + result = await db.execute( + select(Resume).where( + Resume.id == resume_id, + Resume.user_id == current_user.id, + ) + ) + resume = result.scalar_one_or_none() + if not resume: + raise HTTPException(status_code=404, detail="Resume not found.") + + try: + content_bytes = download_file(resume.gcs_path) + return {"content": content_bytes.decode("utf-8"), "filename": resume.original_filename} + except Exception as e: + raise HTTPException(status_code=500, detail=f"Failed to read resume: {e}") + + +@router.delete("/{resume_id}", status_code=status.HTTP_204_NO_CONTENT) +async def delete_resume( + resume_id: int, + current_user: User = Depends(get_current_user), + db: AsyncSession = Depends(get_db), +): + """ + Delete a resume. Fails if the resume has been used in a job application + (ON DELETE RESTRICT enforced at the DB level). + """ + result = await db.execute( + select(Resume).where( + Resume.id == resume_id, + Resume.user_id == current_user.id, + ) + ) + resume = result.scalar_one_or_none() + if not resume: + raise HTTPException(status_code=404, detail="Resume not found.") + + try: + # Delete from GCS + try: + delete_file(resume.gcs_path) + except Exception: + pass # GCS file might not exist; still remove DB record + + # Delete DB record + await db.delete(resume) + await db.commit() + except Exception: + await db.rollback() + raise HTTPException( + status_code=status.HTTP_409_CONFLICT, + detail="Cannot delete this resume — it has been used in a job application.", + ) diff --git a/backend/schemas.py b/backend/schemas.py new file mode 100644 index 0000000..6e595a6 --- /dev/null +++ b/backend/schemas.py @@ -0,0 +1,105 @@ +""" +schemas.py +---------- +Pydantic request/response schemas for the FastAPI endpoints. +""" + +from datetime import datetime +from typing import Optional +from pydantic import BaseModel, EmailStr, Field + + +# ── Auth ────────────────────────────────────────────────────────────────────── + +class RegisterRequest(BaseModel): + first_name: str = Field(..., min_length=1, max_length=100) + last_name: str = Field(..., min_length=1, max_length=100) + email: EmailStr + password: str = Field(..., min_length=8) + + +class LoginRequest(BaseModel): + email: EmailStr + password: str + + +class GoogleOAuthRequest(BaseModel): + id_token: str = Field(..., description="Google OAuth ID token from frontend") + + +class TokenResponse(BaseModel): + access_token: str + token_type: str = "bearer" + + +class UserResponse(BaseModel): + id: int + first_name: str + last_name: str + email: str + created_at: datetime + + model_config = {"from_attributes": True} + + +# ── GitHub Profiles ─────────────────────────────────────────────────────────── + +class GithubProfileCreate(BaseModel): + github_username: str = Field(..., min_length=1, max_length=100) + + +class GithubProfileResponse(BaseModel): + id: int + github_username: str + github_url: str + bq_dataset_name: str + created_at: datetime + + model_config = {"from_attributes": True} + + +# ── Resumes ─────────────────────────────────────────────────────────────────── + +class ResumeResponse(BaseModel): + id: int + original_filename: str + gcs_path: str + uploaded_at: datetime + + model_config = {"from_attributes": True} + + +# ── Jobs ────────────────────────────────────────────────────────────────────── + +class TailorJobRequest(BaseModel): + linkedin_job_url: str = Field(..., description="LinkedIn job posting URL") + github_profile_id: int = Field(..., description="ID of the GitHub profile to use") + resume_id: int = Field(..., description="ID of the uploaded resume to use") + + +class JobResponse(BaseModel): + id: int + linkedin_job_url: str + job_name: Optional[str] = None + company_name: Optional[str] = None + location: Optional[str] = None + seniority_level: Optional[str] = None + employment_type: Optional[str] = None + job_function: Optional[str] = None + industry: Optional[str] = None + status: str + error_message: Optional[str] = None + tailored_resume_gcs_path: Optional[str] = None + interview_materials_gcs_path: Optional[str] = None + created_at: datetime + completed_at: Optional[datetime] = None + + model_config = {"from_attributes": True} + + +class JobDetailResponse(JobResponse): + """Extended job response including the full description and requirements.""" + job_description: Optional[str] = None + requirements: Optional[str] = None + + model_config = {"from_attributes": True} diff --git a/frontend/.gitignore b/frontend/.gitignore new file mode 100644 index 0000000..a547bf3 --- /dev/null +++ b/frontend/.gitignore @@ -0,0 +1,24 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? diff --git a/frontend/README.md b/frontend/README.md new file mode 100644 index 0000000..a36934d --- /dev/null +++ b/frontend/README.md @@ -0,0 +1,16 @@ +# React + Vite + +This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules. + +Currently, two official plugins are available: + +- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Oxc](https://oxc.rs) +- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/) + +## React Compiler + +The React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see [this documentation](https://react.dev/learn/react-compiler/installation). + +## Expanding the ESLint configuration + +If you are developing a production application, we recommend using TypeScript with type-aware lint rules enabled. Check out the [TS template](https://github.com/vitejs/vite/tree/main/packages/create-vite/template-react-ts) for information on how to integrate TypeScript and [`typescript-eslint`](https://typescript-eslint.io) in your project. diff --git a/frontend/eslint.config.js b/frontend/eslint.config.js new file mode 100644 index 0000000..ea36dd3 --- /dev/null +++ b/frontend/eslint.config.js @@ -0,0 +1,21 @@ +import js from '@eslint/js' +import globals from 'globals' +import reactHooks from 'eslint-plugin-react-hooks' +import reactRefresh from 'eslint-plugin-react-refresh' +import { defineConfig, globalIgnores } from 'eslint/config' + +export default defineConfig([ + globalIgnores(['dist']), + { + files: ['**/*.{js,jsx}'], + extends: [ + js.configs.recommended, + reactHooks.configs.flat.recommended, + reactRefresh.configs.vite, + ], + languageOptions: { + globals: globals.browser, + parserOptions: { ecmaFeatures: { jsx: true } }, + }, + }, +]) diff --git a/frontend/index.html b/frontend/index.html new file mode 100644 index 0000000..6f95f9c --- /dev/null +++ b/frontend/index.html @@ -0,0 +1,17 @@ + + + + + + + + + + + Job Application Agent — AI Resume Tailoring + + +
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"^10.0.1", + "@types/react": "^19.2.14", + "@types/react-dom": "^19.2.3", + "@vitejs/plugin-react": "^6.0.1", + "eslint": "^10.3.0", + "eslint-plugin-react-hooks": "^7.1.1", + "eslint-plugin-react-refresh": "^0.5.2", + "globals": "^17.6.0", + "vite": "^8.0.12" + } +} diff --git a/frontend/public/favicon.svg b/frontend/public/favicon.svg new file mode 100644 index 0000000..6893eb1 --- /dev/null +++ b/frontend/public/favicon.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/public/icons.svg b/frontend/public/icons.svg new file mode 100644 index 0000000..e952219 --- /dev/null +++ b/frontend/public/icons.svg @@ -0,0 +1,24 @@ + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/frontend/src/App.jsx b/frontend/src/App.jsx new file mode 100644 index 0000000..aff5b5e --- /dev/null +++ b/frontend/src/App.jsx @@ -0,0 +1,39 @@ +import { Routes, Route, Navigate } from 'react-router-dom'; +import { useAuth } from './context/AuthContext'; +import LandingPage from './pages/LandingPage'; +import RegisterPage from './pages/RegisterPage'; +import HomePage from './pages/HomePage'; +import InterviewMaterialsPage from './pages/InterviewMaterialsPage'; + +function ProtectedRoute({ children }) { + const { user, loading } = useAuth(); + if (loading) { + return ( +
+
+
+ ); + } + return user ? children : ; +} + +export default function App() { + const { user, loading } = useAuth(); + + if (loading) { + return ( +
+
+
+ ); + } + + return ( + + : } /> + : } /> + } /> + } /> + + ); +} diff --git a/frontend/src/api/client.js b/frontend/src/api/client.js new file mode 100644 index 0000000..a90b6a0 --- /dev/null +++ b/frontend/src/api/client.js @@ -0,0 +1,31 @@ +import axios from 'axios'; + +const api = axios.create({ + baseURL: '/api', + headers: { 'Content-Type': 'application/json' }, +}); + +// JWT interceptor — attach token to every request +api.interceptors.request.use((config) => { + const token = localStorage.getItem('token'); + if (token) { + config.headers.Authorization = `Bearer ${token}`; + } + return config; +}); + +// Response interceptor — handle 401 globally +api.interceptors.response.use( + (response) => response, + (error) => { + if (error.response?.status === 401) { + localStorage.removeItem('token'); + if (window.location.pathname !== '/') { + window.location.href = '/'; + } + } + return Promise.reject(error); + } +); + +export default api; diff --git a/frontend/src/assets/hero.png 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a/frontend/src/assets/react.svg b/frontend/src/assets/react.svg new file mode 100644 index 0000000..6c87de9 --- /dev/null +++ b/frontend/src/assets/react.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/src/assets/vite.svg b/frontend/src/assets/vite.svg new file mode 100644 index 0000000..5101b67 --- /dev/null +++ b/frontend/src/assets/vite.svg @@ -0,0 +1 @@ +Vite diff --git a/frontend/src/components/GithubProfileAdd.jsx b/frontend/src/components/GithubProfileAdd.jsx new file mode 100644 index 0000000..2d44baa --- /dev/null +++ b/frontend/src/components/GithubProfileAdd.jsx @@ -0,0 +1,74 @@ +import { useState } from 'react'; +import api from '../api/client'; +import { FiGithub, FiTrash2, FiPlus } from 'react-icons/fi'; + +export default function GithubProfileAdd({ profiles, onRefresh }) { + const [username, setUsername] = useState(''); + const [loading, setLoading] = useState(false); + const [error, setError] = useState(''); + + const handleAdd = async (e) => { + e.preventDefault(); + if (!username.trim()) return; + setError(''); + setLoading(true); + try { + await api.post('/github', { github_username: username.trim() }); + setUsername(''); + onRefresh(); + } catch (err) { + setError(err.response?.data?.detail || 'Failed to add GitHub profile.'); + } finally { + setLoading(false); + } + }; + + const handleDelete = async (id) => { + try { + await api.delete(`/github/${id}`); + onRefresh(); + } catch (err) { + setError(err.response?.data?.detail || 'Cannot delete profile.'); + } + }; + + return ( +
+

+ GitHub Profiles +

+ +
+ setUsername(e.target.value)} + style={{ flex: 1 }} id="github-username-input" + /> + +
+ + {error &&

{error}

} + + {profiles.length > 0 && ( +
+ {profiles.map((p) => ( +
+ + {p.github_username} + + +
+ ))} +
+ )} +
+ ); +} diff --git a/frontend/src/components/LoginSection.jsx b/frontend/src/components/LoginSection.jsx new file mode 100644 index 0000000..3aecd77 --- /dev/null +++ b/frontend/src/components/LoginSection.jsx @@ -0,0 +1,53 @@ +import { useState } from 'react'; +import api from '../api/client'; +import { useAuth } from '../context/AuthContext'; +import { FiMail, FiLock } from 'react-icons/fi'; + +export default function LoginSection() { + const { login } = useAuth(); + const [email, setEmail] = useState(''); + const [password, setPassword] = useState(''); + const [error, setError] = useState(''); + const [loading, setLoading] = useState(false); + + const handleSubmit = async (e) => { + e.preventDefault(); + setError(''); + setLoading(true); + try { + const res = await api.post('/auth/login', { email, password }); + login(res.data.access_token); + } catch (err) { + setError(err.response?.data?.detail || 'Login failed. Please try again.'); + } finally { + setLoading(false); + } + }; + + return ( +
+
+ +
+ + setEmail(e.target.value)} required + style={{ paddingLeft: 36 }} /> +
+
+
+ +
+ + setPassword(e.target.value)} required + style={{ paddingLeft: 36 }} /> +
+
+ {error &&

{error}

} + +
+ ); +} diff --git a/frontend/src/components/Navbar.jsx b/frontend/src/components/Navbar.jsx new file mode 100644 index 0000000..2cbdedb --- /dev/null +++ b/frontend/src/components/Navbar.jsx @@ -0,0 +1,41 @@ +import { useAuth } from '../context/AuthContext'; +import { useNavigate, Link } from 'react-router-dom'; +import { FiLogOut, FiZap } from 'react-icons/fi'; + +export default function Navbar() { + const { user, logout } = useAuth(); + const navigate = useNavigate(); + + const handleLogout = () => { + logout(); + navigate('/'); + }; + + return ( + + ); +} diff --git a/frontend/src/components/ProgressTracker.jsx b/frontend/src/components/ProgressTracker.jsx new file mode 100644 index 0000000..419a93a --- /dev/null +++ b/frontend/src/components/ProgressTracker.jsx @@ -0,0 +1,98 @@ +import { useEffect, useState } from 'react'; +import { FiCheck, FiLoader, FiClock, FiAlertTriangle } from 'react-icons/fi'; + +const STEPS = [ + { key: 'extracting_job_info', label: 'Extracting job information' }, + { key: 'searching_projects', label: 'Searching GitHub projects' }, + { key: 'building_profile', label: 'Building your profile' }, + { key: 'tailoring_resume', label: 'Tailoring your resume' }, + { key: 'uploading_results', label: 'Uploading results' }, +]; + +export default function ProgressTracker({ jobId, onComplete }) { + const [currentStep, setCurrentStep] = useState('pending'); + const [error, setError] = useState(null); + + useEffect(() => { + if (!jobId) return; + + const eventSource = new EventSource(`/api/jobs/${jobId}/progress`); + eventSource.onmessage = (event) => { + try { + const data = JSON.parse(event.data); + setCurrentStep(data.step); + if (data.error) setError(data.error); + if (data.step === 'completed' || data.step === 'failed') { + eventSource.close(); + if (data.step === 'completed') onComplete?.(); + } + } catch (e) { + console.error('SSE parse error:', e); + } + }; + eventSource.onerror = () => { + // Auto-reconnect is built into EventSource + }; + return () => eventSource.close(); + }, [jobId, onComplete]); + + const getStepIndex = () => STEPS.findIndex((s) => s.key === currentStep); + const activeIdx = getStepIndex(); + + return ( +
+

+ {currentStep === 'completed' ? '✅ Tailoring Complete!' : + currentStep === 'failed' ? '❌ Tailoring Failed' : + '⚡ Tailoring in progress...'} +

+ +
+ {STEPS.map((step, idx) => { + let status = 'pending'; + if (currentStep === 'completed') status = 'done'; + else if (currentStep === 'failed' && idx <= activeIdx) status = idx === activeIdx ? 'failed' : 'done'; + else if (idx < activeIdx) status = 'done'; + else if (idx === activeIdx) status = 'active'; + + return ( +
+
+ {status === 'done' && } + {status === 'active' && } + {status === 'failed' && } + {status === 'pending' && } +
+ + {step.label} + +
+ ); + })} +
+ + {error && ( +

+ {error} +

+ )} +
+ ); +} diff --git a/frontend/src/components/ResumePreviewModal.jsx b/frontend/src/components/ResumePreviewModal.jsx new file mode 100644 index 0000000..00f0029 --- /dev/null +++ b/frontend/src/components/ResumePreviewModal.jsx @@ -0,0 +1,55 @@ +import { useState, useEffect } from 'react'; +import ReactMarkdown from 'react-markdown'; +import remarkGfm from 'remark-gfm'; +import api from '../api/client'; +import { FiX } from 'react-icons/fi'; + +export default function ResumePreviewModal({ jobId, resumeId, onClose }) { + const [content, setContent] = useState(''); + const [loading, setLoading] = useState(true); + const [title, setTitle] = useState('Resume Preview'); + + useEffect(() => { + const fetchContent = async () => { + setLoading(true); + try { + if (jobId) { + // Preview tailored resume from a job + const res = await api.get(`/jobs/${jobId}/resume/download`, { responseType: 'text' }); + setContent(typeof res.data === 'string' ? res.data : new TextDecoder().decode(res.data)); + setTitle('Tailored Resume'); + } else if (resumeId) { + // Preview uploaded resume + const res = await api.get(`/resumes/${resumeId}/content`); + setContent(res.data.content); + setTitle(res.data.filename || 'Resume Preview'); + } + } catch (err) { + setContent('Failed to load resume content.'); + } finally { + setLoading(false); + } + }; + fetchContent(); + }, [jobId, resumeId]); + + return ( +
+
e.stopPropagation()}> +
+

{title}

+ +
+ {loading ? ( +
+
+
+ ) : ( +
+ {content} +
+ )} +
+
+ ); +} diff --git a/frontend/src/components/ResumeReadyModal.jsx b/frontend/src/components/ResumeReadyModal.jsx new file mode 100644 index 0000000..03246aa --- /dev/null +++ b/frontend/src/components/ResumeReadyModal.jsx @@ -0,0 +1,59 @@ +import { useNavigate } from 'react-router-dom'; +import { FiEye, FiDownload, FiBookOpen, FiX } from 'react-icons/fi'; +import api from '../api/client'; + +export default function ResumeReadyModal({ job, onClose, onPreviewResume }) { + const navigate = useNavigate(); + + const downloadFile = async (type) => { + try { + const url = type === 'resume' + ? `/jobs/${job.id}/resume/download` + : `/jobs/${job.id}/interview/download`; + const res = await api.get(url, { responseType: 'blob' }); + const blob = new Blob([res.data], { type: 'text/markdown' }); + const link = document.createElement('a'); + link.href = URL.createObjectURL(blob); + link.download = `${type}_${job.company_name || 'output'}.md`; + link.click(); + URL.revokeObjectURL(link.href); + } catch (err) { + console.error('Download failed:', err); + } + }; + + return ( +
+
e.stopPropagation()} style={{ textAlign: 'center', maxWidth: 500 }}> + + +
🎉
+

+ Your tailored resume is ready! +

+

+ {job.job_name} at {job.company_name} +

+ +
+ + +
+ + +
+
+
+
+ ); +} diff --git a/frontend/src/components/ResumeUpload.jsx b/frontend/src/components/ResumeUpload.jsx new file mode 100644 index 0000000..5fa8b93 --- /dev/null +++ b/frontend/src/components/ResumeUpload.jsx @@ -0,0 +1,113 @@ +import { useState, useRef } from 'react'; +import api from '../api/client'; +import { FiUploadCloud, FiFile, FiTrash2, FiExternalLink } from 'react-icons/fi'; + +export default function ResumeUpload({ resumes, onRefresh }) { + const [uploading, setUploading] = useState(false); + const [dragActive, setDragActive] = useState(false); + const [error, setError] = useState(''); + const fileInputRef = useRef(null); + + const handleUpload = async (file) => { + if (!file) return; + if (!file.name.endsWith('.md')) { + setError('Only Markdown (.md) files are accepted.'); + return; + } + setError(''); + setUploading(true); + try { + const formData = new FormData(); + formData.append('file', file); + await api.post('/resumes/upload', formData, { + headers: { 'Content-Type': 'multipart/form-data' }, + }); + onRefresh(); + } catch (err) { + setError(err.response?.data?.detail || 'Upload failed.'); + } finally { + setUploading(false); + } + }; + + const handleDelete = async (id) => { + try { + await api.delete(`/resumes/${id}`); + onRefresh(); + } catch (err) { + setError(err.response?.data?.detail || 'Cannot delete resume.'); + } + }; + + const handleDrop = (e) => { + e.preventDefault(); + setDragActive(false); + const file = e.dataTransfer.files[0]; + handleUpload(file); + }; + + return ( +
+

+ Resume Upload +

+ + {/* Drop zone */} +
{ e.preventDefault(); setDragActive(true); }} + onDragLeave={() => setDragActive(false)} + onDrop={handleDrop} + onClick={() => fileInputRef.current?.click()} + style={{ + border: `2px dashed ${dragActive ? 'var(--accent-primary)' : 'var(--border-medium)'}`, + borderRadius: 'var(--radius-md)', padding: 'var(--space-8)', + textAlign: 'center', cursor: 'pointer', + background: dragActive ? 'var(--accent-primary-glow)' : 'transparent', + transition: 'all var(--transition-base)', marginBottom: 'var(--space-4)', + }} + > + +

+ {uploading ? 'Uploading...' : 'Drag & drop or click to upload'} +

+

+ Only .md files accepted +

+ handleUpload(e.target.files[0])} /> +
+ + {/* Conversion links */} + + + {error &&

{error}

} + + {/* Resume list */} + {resumes.length > 0 && ( +
+ {resumes.map((r) => ( +
+ + {r.original_filename} + + +
+ ))} +
+ )} +
+ ); +} diff --git a/frontend/src/components/TailorResumeForm.jsx b/frontend/src/components/TailorResumeForm.jsx new file mode 100644 index 0000000..f409645 --- /dev/null +++ b/frontend/src/components/TailorResumeForm.jsx @@ -0,0 +1,84 @@ +import { useState } from 'react'; +import api from '../api/client'; +import { FiLink, FiSend, FiEye } from 'react-icons/fi'; + +export default function TailorResumeForm({ githubProfiles, resumes, onJobCreated, onPreviewUploadedResume }) { + const [jobUrl, setJobUrl] = useState(''); + const [githubId, setGithubId] = useState(''); + const [resumeId, setResumeId] = useState(''); + const [loading, setLoading] = useState(false); + const [error, setError] = useState(''); + + const handleSubmit = async (e) => { + e.preventDefault(); + if (!jobUrl || !githubId || !resumeId) { + setError('All fields are required.'); + return; + } + setError(''); + setLoading(true); + try { + const res = await api.post('/jobs/tailor', { + linkedin_job_url: jobUrl, + github_profile_id: parseInt(githubId), + resume_id: parseInt(resumeId), + }); + setJobUrl(''); + onJobCreated(res.data); + } catch (err) { + setError(err.response?.data?.detail || 'Failed to start tailoring.'); + } finally { + setLoading(false); + } + }; + + const canPreview = resumeId && resumes.length > 0; + + return ( +
+
+ +
+ + setJobUrl(e.target.value)} required + style={{ paddingLeft: 36 }} /> +
+
+ +
+
+ + +
+
+ +
+ + {canPreview && ( + + )} +
+
+
+ + {error &&

{error}

} + + +
+ ); +} diff --git a/frontend/src/components/TailoredResumeList.jsx b/frontend/src/components/TailoredResumeList.jsx new file mode 100644 index 0000000..cc3cd71 --- /dev/null +++ b/frontend/src/components/TailoredResumeList.jsx @@ -0,0 +1,76 @@ +import { useNavigate } from 'react-router-dom'; +import { FiEye, FiDownload, FiBookOpen } from 'react-icons/fi'; +import api from '../api/client'; + +export default function TailoredResumeList({ jobs, onPreviewResume }) { + const navigate = useNavigate(); + + const downloadFile = async (jobId, type) => { + try { + const url = type === 'resume' + ? `/jobs/${jobId}/resume/download` + : `/jobs/${jobId}/interview/download`; + const res = await api.get(url, { responseType: 'blob' }); + const blob = new Blob([res.data], { type: 'text/markdown' }); + const link = document.createElement('a'); + link.href = URL.createObjectURL(blob); + link.download = res.headers['content-disposition']?.split('filename=')[1]?.replace(/"/g, '') || `${type}.md`; + link.click(); + URL.revokeObjectURL(link.href); + } catch (err) { + console.error('Download failed:', err); + } + }; + + if (!jobs || jobs.length === 0) { + return ( +
+

No tailored resumes yet

+

Start by tailoring your first resume below!

+
+ ); + } + + return ( +
+ {jobs.map((job) => ( +
+
+
+ {job.job_name || 'Processing...'} +
+
+ {job.company_name || '—'} · {new Date(job.created_at).toLocaleDateString()} +
+
+ {job.status} + {job.status === 'completed' && ( +
+ + + + +
+ )} + {job.status === 'failed' && job.error_message && ( +

+ {job.error_message} +

+ )} +
+ ))} +
+ ); +} diff --git a/frontend/src/context/AuthContext.jsx b/frontend/src/context/AuthContext.jsx new file mode 100644 index 0000000..f6f09ec --- /dev/null +++ b/frontend/src/context/AuthContext.jsx @@ -0,0 +1,52 @@ +import { createContext, useContext, useState, useEffect, useCallback } from 'react'; +import api from '../api/client'; + +const AuthContext = createContext(null); + +export function AuthProvider({ children }) { + const [user, setUser] = useState(null); + const [loading, setLoading] = useState(true); + + const fetchUser = useCallback(async () => { + const token = localStorage.getItem('token'); + if (!token) { + setLoading(false); + return; + } + try { + const res = await api.get('/auth/me'); + setUser(res.data); + } catch { + localStorage.removeItem('token'); + setUser(null); + } finally { + setLoading(false); + } + }, []); + + useEffect(() => { + fetchUser(); + }, [fetchUser]); + + const login = (token) => { + localStorage.setItem('token', token); + fetchUser(); + }; + + const logout = () => { + localStorage.removeItem('token'); + setUser(null); + }; + + return ( + + {children} + + ); +} + +export function useAuth() { + const ctx = useContext(AuthContext); + if (!ctx) throw new Error('useAuth must be used within AuthProvider'); + return ctx; +} diff --git a/frontend/src/index.css b/frontend/src/index.css new file mode 100644 index 0000000..237a532 --- /dev/null +++ b/frontend/src/index.css @@ -0,0 +1,450 @@ +/* ══════════════════════════════════════════════════════════════════════ + DESIGN SYSTEM — Global Tokens & Base Styles + ══════════════════════════════════════════════════════════════════════ */ + +:root { + /* ── Color Palette ─────────────────────────────────────────────── */ + --bg-primary: #0a0a0f; + --bg-secondary: #13131a; + --bg-surface: #1a1a2e; + --bg-surface-hover: #22223a; + --bg-elevated: #252540; + + --accent-primary: #6c63ff; + --accent-primary-hover: #7d76ff; + --accent-primary-glow: rgba(108, 99, 255, 0.25); + --accent-secondary: #00d4aa; + --accent-secondary-glow: rgba(0, 212, 170, 0.2); + + --text-primary: #e8e8f0; + --text-secondary: #9898a8; + --text-muted: #6b6b7b; + --text-accent: #b4b0ff; + + --error: #ff4757; + --error-bg: rgba(255, 71, 87, 0.1); + --warning: #ffa726; + --success: #00d4aa; + --success-bg: rgba(0, 212, 170, 0.1); + --info: #6c63ff; + + --border-subtle: rgba(255, 255, 255, 0.06); + --border-medium: rgba(255, 255, 255, 0.1); + --border-accent: rgba(108, 99, 255, 0.3); + + /* ── Typography ────────────────────────────────────────────────── */ + --font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif; + --font-size-xs: 0.75rem; + --font-size-sm: 0.875rem; + --font-size-base: 1rem; + --font-size-lg: 1.125rem; + --font-size-xl: 1.25rem; + --font-size-2xl: 1.5rem; + --font-size-3xl: 2rem; + --font-size-4xl: 2.5rem; + --font-size-5xl: 3.5rem; + + /* ── Spacing ───────────────────────────────────────────────────── */ + --space-1: 0.25rem; + --space-2: 0.5rem; + --space-3: 0.75rem; + --space-4: 1rem; + --space-5: 1.25rem; + --space-6: 1.5rem; + --space-8: 2rem; + --space-10: 2.5rem; + --space-12: 3rem; + --space-16: 4rem; + --space-20: 5rem; + + /* ── Border Radius ─────────────────────────────────────────────── */ + --radius-sm: 6px; + --radius-md: 10px; + --radius-lg: 16px; + --radius-xl: 20px; + --radius-full: 9999px; + + /* ── Shadows ───────────────────────────────────────────────────── */ + --shadow-sm: 0 1px 3px rgba(0, 0, 0, 0.3); + --shadow-md: 0 4px 14px rgba(0, 0, 0, 0.35); + --shadow-lg: 0 10px 40px rgba(0, 0, 0, 0.4); + --shadow-glow: 0 0 30px var(--accent-primary-glow); + + /* ── Transitions ───────────────────────────────────────────────── */ + --transition-fast: 150ms cubic-bezier(0.4, 0, 0.2, 1); + --transition-base: 250ms cubic-bezier(0.4, 0, 0.2, 1); + --transition-slow: 400ms cubic-bezier(0.4, 0, 0.2, 1); + + /* ── Glassmorphism ─────────────────────────────────────────────── */ + --glass-bg: rgba(26, 26, 46, 0.6); + --glass-border: rgba(255, 255, 255, 0.08); + --glass-blur: blur(20px); +} + +/* ── Reset & Base ──────────────────────────────────────────────────── */ + +*, *::before, *::after { + margin: 0; + padding: 0; + box-sizing: border-box; +} + +html { + font-size: 16px; + scroll-behavior: smooth; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +body { + font-family: var(--font-family); + background-color: var(--bg-primary); + color: var(--text-primary); + line-height: 1.6; + min-height: 100vh; + overflow-x: hidden; +} + +a { + color: var(--accent-primary); + text-decoration: none; + transition: color var(--transition-fast); +} +a:hover { + color: var(--accent-primary-hover); +} + +h1, h2, h3, h4, h5, h6 { + line-height: 1.2; + font-weight: 700; +} + +img { max-width: 100%; } + +/* ── Utility Classes ───────────────────────────────────────────────── */ + +.glass-card { + background: var(--glass-bg); + backdrop-filter: var(--glass-blur); + -webkit-backdrop-filter: var(--glass-blur); + border: 1px solid var(--glass-border); + border-radius: var(--radius-lg); + transition: border-color var(--transition-base), box-shadow var(--transition-base); +} +.glass-card:hover { + border-color: var(--border-accent); + box-shadow: var(--shadow-glow); +} + +.container { + max-width: 1200px; + margin: 0 auto; + padding: 0 var(--space-6); +} + +/* ── Buttons ───────────────────────────────────────────────────────── */ + +.btn { + display: inline-flex; + align-items: center; + justify-content: center; + gap: var(--space-2); + font-family: var(--font-family); + font-size: var(--font-size-sm); + font-weight: 600; + padding: var(--space-3) var(--space-6); + border-radius: var(--radius-md); + border: none; + cursor: pointer; + transition: all var(--transition-base); + text-decoration: none; + white-space: nowrap; +} +.btn:disabled { + opacity: 0.5; + cursor: not-allowed; +} + +.btn-primary { + background: linear-gradient(135deg, var(--accent-primary), #8b7dff); + color: white; + box-shadow: 0 4px 15px var(--accent-primary-glow); +} +.btn-primary:hover:not(:disabled) { + transform: translateY(-2px); + box-shadow: 0 6px 25px var(--accent-primary-glow); +} + +.btn-secondary { + background: var(--bg-surface); + color: var(--text-primary); + border: 1px solid var(--border-medium); +} +.btn-secondary:hover:not(:disabled) { + background: var(--bg-surface-hover); + border-color: var(--border-accent); +} + +.btn-ghost { + background: transparent; + color: var(--text-secondary); + border: 1px solid transparent; +} +.btn-ghost:hover:not(:disabled) { + color: var(--text-primary); + background: rgba(255, 255, 255, 0.04); +} + +.btn-danger { + background: rgba(255, 71, 87, 0.15); + color: var(--error); + border: 1px solid rgba(255, 71, 87, 0.3); +} +.btn-danger:hover:not(:disabled) { + background: rgba(255, 71, 87, 0.25); +} + +.btn-success { + background: linear-gradient(135deg, var(--accent-secondary), #00e6b8); + color: #0a0a0f; + font-weight: 700; +} +.btn-success:hover:not(:disabled) { + transform: translateY(-2px); + box-shadow: 0 6px 25px var(--accent-secondary-glow); +} + +.btn-sm { padding: var(--space-2) var(--space-4); font-size: var(--font-size-xs); } +.btn-lg { padding: var(--space-4) var(--space-8); font-size: var(--font-size-base); } +.btn-icon { + width: 36px; + height: 36px; + padding: 0; + border-radius: var(--radius-sm); +} + +/* ── Form Inputs ───────────────────────────────────────────────────── */ + +.input-group { + display: flex; + flex-direction: column; + gap: var(--space-2); +} + +.input-group label { + font-size: var(--font-size-sm); + font-weight: 500; + color: var(--text-secondary); +} + +.input-field { + font-family: var(--font-family); + font-size: var(--font-size-base); + padding: var(--space-3) var(--space-4); + background: var(--bg-secondary); + border: 1px solid var(--border-subtle); + border-radius: var(--radius-md); + color: var(--text-primary); + transition: border-color var(--transition-fast), box-shadow var(--transition-fast); + outline: none; + width: 100%; +} +.input-field::placeholder { + color: var(--text-muted); +} +.input-field:focus { + border-color: var(--accent-primary); + box-shadow: 0 0 0 3px var(--accent-primary-glow); +} +.input-field.error { + border-color: var(--error); + box-shadow: 0 0 0 3px var(--error-bg); +} + +.input-error { + font-size: var(--font-size-xs); + color: var(--error); +} + +select.input-field { + appearance: none; + background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='12' height='12' viewBox='0 0 12 12'%3E%3Cpath fill='%239898a8' d='M6 8L1 3h10z'/%3E%3C/svg%3E"); + background-repeat: no-repeat; + background-position: right var(--space-4) center; + padding-right: var(--space-10); +} + +/* ── Toast Notifications ───────────────────────────────────────────── */ + +.toast-container { + position: fixed; + top: var(--space-6); + right: var(--space-6); + z-index: 9999; + display: flex; + flex-direction: column; + gap: var(--space-3); +} + +.toast { + padding: var(--space-4) var(--space-6); + border-radius: var(--radius-md); + font-size: var(--font-size-sm); + font-weight: 500; + backdrop-filter: var(--glass-blur); + animation: slideIn 0.3s ease-out; + max-width: 400px; +} +.toast-error { + background: var(--error-bg); + border: 1px solid rgba(255, 71, 87, 0.3); + color: var(--error); +} +.toast-success { + background: var(--success-bg); + border: 1px solid rgba(0, 212, 170, 0.3); + color: var(--success); +} + +/* ── Animations ────────────────────────────────────────────────────── */ + +@keyframes slideIn { + from { opacity: 0; transform: translateX(20px); } + to { opacity: 1; transform: translateX(0); } +} + +@keyframes fadeIn { + from { opacity: 0; } + to { opacity: 1; } +} + +@keyframes fadeInUp { + from { opacity: 0; transform: translateY(20px); } + to { opacity: 1; transform: translateY(0); } +} + +@keyframes pulse { + 0%, 100% { opacity: 1; } + 50% { opacity: 0.5; } +} + +@keyframes gradientShift { + 0% { background-position: 0% 50%; } + 50% { background-position: 100% 50%; } + 100% { background-position: 0% 50%; } +} + +@keyframes float { + 0%, 100% { transform: translateY(0px); } + 50% { transform: translateY(-10px); } +} + +@keyframes spin { + from { transform: rotate(0deg); } + to { transform: rotate(360deg); } +} + +.animate-fade-in { animation: fadeIn 0.5s ease-out; } +.animate-fade-in-up { animation: fadeInUp 0.6s ease-out; } +.animate-pulse { animation: pulse 2s infinite; } + +/* ── Scrollbar ─────────────────────────────────────────────────────── */ + +::-webkit-scrollbar { + width: 8px; +} +::-webkit-scrollbar-track { + background: var(--bg-primary); +} +::-webkit-scrollbar-thumb { + background: var(--bg-surface-hover); + border-radius: 4px; +} +::-webkit-scrollbar-thumb:hover { + background: var(--text-muted); +} + +/* ── Markdown Content Styling ──────────────────────────────────────── */ + +.markdown-content { + font-size: var(--font-size-base); + line-height: 1.8; + color: var(--text-primary); +} +.markdown-content h1 { font-size: var(--font-size-3xl); margin: var(--space-8) 0 var(--space-4); } +.markdown-content h2 { font-size: var(--font-size-2xl); margin: var(--space-6) 0 var(--space-3); color: var(--accent-primary); } +.markdown-content h3 { font-size: var(--font-size-xl); margin: var(--space-5) 0 var(--space-2); } +.markdown-content p { margin-bottom: var(--space-4); } +.markdown-content ul, .markdown-content ol { padding-left: var(--space-6); margin-bottom: var(--space-4); } +.markdown-content li { margin-bottom: var(--space-2); } +.markdown-content code { + font-family: 'JetBrains Mono', 'Fira Code', monospace; + background: var(--bg-secondary); + padding: 2px 6px; + border-radius: 4px; + font-size: 0.9em; +} +.markdown-content pre { + background: var(--bg-secondary); + padding: var(--space-4); + border-radius: var(--radius-md); + overflow-x: auto; + margin-bottom: var(--space-4); +} +.markdown-content pre code { background: none; padding: 0; } +.markdown-content strong { color: var(--text-primary); font-weight: 600; } +.markdown-content hr { border: none; border-top: 1px solid var(--border-subtle); margin: var(--space-8) 0; } + +/* ── Modal Overlay ─────────────────────────────────────────────────── */ + +.modal-overlay { + position: fixed; + inset: 0; + background: rgba(0, 0, 0, 0.7); + backdrop-filter: blur(4px); + display: flex; + align-items: center; + justify-content: center; + z-index: 1000; + animation: fadeIn 0.2s ease-out; +} + +.modal-content { + background: var(--bg-surface); + border: 1px solid var(--glass-border); + border-radius: var(--radius-xl); + max-width: 800px; + max-height: 85vh; + width: 90%; + overflow-y: auto; + padding: var(--space-8); + animation: fadeInUp 0.3s ease-out; +} + +/* ── Spinner ───────────────────────────────────────────────────────── */ + +.spinner { + width: 20px; + height: 20px; + border: 2px solid var(--border-medium); + border-top-color: var(--accent-primary); + border-radius: 50%; + animation: spin 0.6s linear infinite; +} + +/* ── Badge ─────────────────────────────────────────────────────────── */ + +.badge { + display: inline-flex; + align-items: center; + font-size: var(--font-size-xs); + font-weight: 600; + padding: 2px 8px; + border-radius: var(--radius-full); + text-transform: uppercase; + letter-spacing: 0.5px; +} +.badge-pending { background: rgba(255, 167, 38, 0.15); color: var(--warning); } +.badge-processing { background: var(--accent-primary-glow); color: var(--accent-primary); animation: pulse 2s infinite; } +.badge-completed { background: var(--success-bg); color: var(--success); } +.badge-failed { background: var(--error-bg); color: var(--error); } diff --git a/frontend/src/main.jsx b/frontend/src/main.jsx new file mode 100644 index 0000000..56a6aa2 --- /dev/null +++ b/frontend/src/main.jsx @@ -0,0 +1,16 @@ +import { StrictMode } from 'react' +import { createRoot } from 'react-dom/client' +import { BrowserRouter } from 'react-router-dom' +import { AuthProvider } from './context/AuthContext' +import App from './App' +import './index.css' + +createRoot(document.getElementById('root')).render( + + + + + + + +) diff --git a/frontend/src/pages/HomePage.jsx b/frontend/src/pages/HomePage.jsx new file mode 100644 index 0000000..dbb102c --- /dev/null +++ b/frontend/src/pages/HomePage.jsx @@ -0,0 +1,102 @@ +import { useState, useEffect, useCallback } from 'react'; +import { useAuth } from '../context/AuthContext'; +import api from '../api/client'; +import Navbar from '../components/Navbar'; +import ResumeUpload from '../components/ResumeUpload'; +import GithubProfileAdd from '../components/GithubProfileAdd'; +import TailoredResumeList from '../components/TailoredResumeList'; +import TailorResumeForm from '../components/TailorResumeForm'; +import ProgressTracker from '../components/ProgressTracker'; +import ResumePreviewModal from '../components/ResumePreviewModal'; +import ResumeReadyModal from '../components/ResumeReadyModal'; +import { FiActivity, FiSettings, FiSend } from 'react-icons/fi'; + +export default function HomePage() { + const { user } = useAuth(); + const [jobs, setJobs] = useState([]); + const [resumes, setResumes] = useState([]); + const [githubProfiles, setGithubProfiles] = useState([]); + const [activeJobId, setActiveJobId] = useState(null); + const [completedJob, setCompletedJob] = useState(null); + const [previewJobId, setPreviewJobId] = useState(null); + const [previewResumeId, setPreviewResumeId] = useState(null); + + const fetchData = useCallback(async () => { + try { + const [j, r, g] = await Promise.all([ + api.get('/jobs'), api.get('/resumes'), api.get('/github'), + ]); + setJobs(j.data); setResumes(r.data); setGithubProfiles(g.data); + } catch (err) { console.error('Fetch error:', err); } + }, []); + + useEffect(() => { fetchData(); }, [fetchData]); + + const handleJobCreated = (job) => { + setActiveJobId(job.id); + setJobs((prev) => [job, ...prev]); + }; + + const handleComplete = useCallback(async () => { + try { + const res = await api.get(`/jobs/${activeJobId}`); + setCompletedJob(res.data); + const jobsRes = await api.get('/jobs'); + setJobs(jobsRes.data); + } catch { setCompletedJob({ id: activeJobId }); } + setActiveJobId(null); + }, [activeJobId]); + + return ( +
+ +
+
+

+ Welcome back, {user?.first_name}! +

+

Manage your resumes and tailor them for your next opportunity.

+
+ +
+

+ Previously Tailored Resumes +

+ setPreviewJobId(id)} /> +
+ +
+

+ Manage Resources +

+
+ + +
+
+ +
+

+ Tailor Resume +

+
+ setPreviewResumeId(id)} /> +
+
+ + {activeJobId && ( +
+ +
+ )} +
+ + {previewJobId && setPreviewJobId(null)} />} + {previewResumeId && setPreviewResumeId(null)} />} + {completedJob && { setCompletedJob(null); fetchData(); }} + onPreviewResume={(id) => { setCompletedJob(null); setPreviewJobId(id); }} />} +
+ ); +} diff --git a/frontend/src/pages/InterviewMaterialsPage.jsx b/frontend/src/pages/InterviewMaterialsPage.jsx new file mode 100644 index 0000000..e585012 --- /dev/null +++ b/frontend/src/pages/InterviewMaterialsPage.jsx @@ -0,0 +1,85 @@ +import { useState, useEffect } from 'react'; +import { useParams, Link } from 'react-router-dom'; +import ReactMarkdown from 'react-markdown'; +import remarkGfm from 'remark-gfm'; +import api from '../api/client'; +import Navbar from '../components/Navbar'; +import { FiArrowLeft, FiDownload } from 'react-icons/fi'; + +export default function InterviewMaterialsPage() { + const { jobId } = useParams(); + const [data, setData] = useState(null); + const [loading, setLoading] = useState(true); + const [error, setError] = useState(''); + + useEffect(() => { + (async () => { + try { + const res = await api.get(`/jobs/${jobId}/interview/content`); + setData(res.data); + } catch (err) { + setError(err.response?.data?.detail || 'Failed to load interview materials.'); + } finally { + setLoading(false); + } + })(); + }, [jobId]); + + const handleDownload = async () => { + try { + const res = await api.get(`/jobs/${jobId}/interview/download`, { responseType: 'blob' }); + const blob = new Blob([res.data], { type: 'text/markdown' }); + const link = document.createElement('a'); + link.href = URL.createObjectURL(blob); + link.download = `interview_materials_${data?.company_name || 'prep'}.md`; + link.click(); + URL.revokeObjectURL(link.href); + } catch (err) { + console.error('Download failed:', err); + } + }; + + return ( +
+ +
+
+ + Back to Dashboard + + {data && ( + + )} +
+ + {loading ? ( +
+
+
+ ) : error ? ( +
+

{error}

+
+ ) : ( + <> +
+

+ Interview Preparation +

+

+ {data.job_name} at {data.company_name} · {data.created_at ? new Date(data.created_at).toLocaleDateString() : ''} +

+
+
+
+ {data.content} +
+
+ + )} +
+
+ ); +} diff --git a/frontend/src/pages/LandingPage.jsx b/frontend/src/pages/LandingPage.jsx new file mode 100644 index 0000000..9956289 --- /dev/null +++ b/frontend/src/pages/LandingPage.jsx @@ -0,0 +1,181 @@ +import { Link } from 'react-router-dom'; +import LoginSection from '../components/LoginSection'; +import { FiUpload, FiLink, FiCpu, FiTarget, FiGithub, FiSearch, FiZap, FiFileText } from 'react-icons/fi'; + +const PROBLEMS = [ + { icon: , text: 'Spending hours customizing resumes for each application?' }, + { icon: , text: 'Struggling to pass ATS keyword screening?' }, + { icon: , text: 'Not sure which projects to highlight for each role?' }, + { icon: , text: 'LinkedIn job postings are hard to decode quickly' }, +]; + +const STEPS = [ + { num: '01', icon: , title: 'Upload & Link', desc: 'Upload your resume and link your GitHub profile' }, + { num: '02', icon: , title: 'Paste Job URL', desc: 'Paste a LinkedIn job posting URL' }, + { num: '03', icon: , title: 'AI Tailoring', desc: 'AI agents analyze, match, and tailor your resume' }, +]; + +const FEATURES = [ + { icon: , title: 'ATS Optimization', desc: 'Embed the right keywords to pass automated screening systems' }, + { icon: , title: 'GitHub Analysis', desc: 'Automatically analyze your repos to highlight relevant projects' }, + { icon: , title: 'Smart Matching', desc: 'AI matches your experience with job requirements precisely' }, + { icon: , title: 'Instant Tailoring', desc: 'Get a perfectly tailored resume in minutes, not hours' }, +]; + +export default function LandingPage() { + return ( +
+ {/* ── Hero ──────────────────────────────────────────────────── */} +
+ {/* Gradient orbs */} +
+
+ +
+ + ✨ AI-Powered Resume Agent + +

+ Your Dream Job Deserves
a Perfect Resume +

+

+ AI agents analyze your GitHub projects, understand job requirements, + and tailor your resume for maximum impact. +

+ +
+
+ + {/* ── Problems ──────────────────────────────────────────────── */} +
+
+

+ Sound familiar? +

+
+ {PROBLEMS.map((p, i) => ( +
+
{p.icon}
+

{p.text}

+
+ ))} +
+
+
+ + {/* ── How It Works ──────────────────────────────────────────── */} +
+
+

+ How It Works +

+

+ Three simple steps to a perfectly tailored resume +

+
+ {STEPS.map((s, i) => ( +
+ + {s.icon} + + + STEP {s.num} + +

{s.title}

+

{s.desc}

+
+ ))} +
+
+
+ + {/* ── Features ──────────────────────────────────────────────── */} +
+
+

+ Powerful Features +

+
+ {FEATURES.map((f, i) => ( +
+
+ {f.icon} +
+

{f.title}

+

{f.desc}

+
+ ))} +
+
+
+ + {/* ── Auth Section ──────────────────────────────────────────── */} +
+
+
+ {/* Login side */} +
+

Welcome Back

+

+ Sign in to your account +

+ +
+ + {/* Register side */} +
+

New Here?

+

+ Create an account and start tailoring your resume with AI +

+ + Create Account + +
+
+
+
+ + {/* ── Footer ────────────────────────────────────────────────── */} +
+ Built with CrewAI, FastAPI & React · {new Date().getFullYear()} +
+
+ ); +} diff --git a/frontend/src/pages/RegisterPage.jsx b/frontend/src/pages/RegisterPage.jsx new file mode 100644 index 0000000..0d66cac --- /dev/null +++ b/frontend/src/pages/RegisterPage.jsx @@ -0,0 +1,99 @@ +import { useState } from 'react'; +import { Link, useNavigate } from 'react-router-dom'; +import api from '../api/client'; +import { useAuth } from '../context/AuthContext'; +import { FiUser, FiMail, FiLock, FiArrowLeft } from 'react-icons/fi'; + +export default function RegisterPage() { + const { login } = useAuth(); + const navigate = useNavigate(); + const [form, setForm] = useState({ first_name: '', last_name: '', email: '', password: '', confirmPassword: '' }); + const [errors, setErrors] = useState({}); + const [serverError, setServerError] = useState(''); + const [loading, setLoading] = useState(false); + + const validate = () => { + const errs = {}; + if (!form.first_name.trim()) errs.first_name = 'First name is required'; + if (!form.last_name.trim()) errs.last_name = 'Last name is required'; + if (!/^[^\s@]+@[^\s@]+\.[^\s@]+$/.test(form.email)) errs.email = 'Invalid email address'; + if (form.password.length < 8) errs.password = 'Password must be at least 8 characters'; + if (form.password !== form.confirmPassword) errs.confirmPassword = 'Passwords do not match'; + setErrors(errs); + return Object.keys(errs).length === 0; + }; + + const handleSubmit = async (e) => { + e.preventDefault(); + if (!validate()) return; + setServerError(''); + setLoading(true); + try { + const res = await api.post('/auth/register', { + first_name: form.first_name, last_name: form.last_name, + email: form.email, password: form.password, + }); + login(res.data.access_token); + navigate('/dashboard'); + } catch (err) { + setServerError(err.response?.data?.detail || 'Registration failed.'); + } finally { + setLoading(false); + } + }; + + const handleChange = (field) => (e) => { + setForm({ ...form, [field]: e.target.value }); + if (errors[field]) setErrors({ ...errors, [field]: '' }); + }; + + const fields = [ + { key: 'first_name', label: 'First Name', type: 'text', icon: , placeholder: 'John' }, + { key: 'last_name', label: 'Last Name', type: 'text', icon: , placeholder: 'Doe' }, + { key: 'email', label: 'Email', type: 'email', icon: , placeholder: 'you@example.com' }, + { key: 'password', label: 'Password', type: 'password', icon: , placeholder: 'At least 8 characters' }, + { key: 'confirmPassword', label: 'Confirm Password', type: 'password', icon: , placeholder: 'Re-enter your password' }, + ]; + + return ( +
+
+ + Back to home + +

Create Account

+

+ Join and start tailoring your resumes with AI +

+ +
+ {fields.map((f) => ( +
+ +
+ {f.icon} + +
+ {errors[f.key] && {errors[f.key]}} + {f.key === 'confirmPassword' && form.password && form.confirmPassword && form.password === form.confirmPassword && ( + + Passwords Matched + + )} +
+ ))} + {serverError &&

{serverError}

} + +
+ +

+ Already have an account? Sign in +

+
+
+ ); +} diff --git a/frontend/vite.config.js b/frontend/vite.config.js new file mode 100644 index 0000000..1406734 --- /dev/null +++ b/frontend/vite.config.js @@ -0,0 +1,15 @@ +import { defineConfig } from 'vite' +import react from '@vitejs/plugin-react' + +export default defineConfig({ + plugins: [react()], + server: { + port: 5173, + proxy: { + '/api': { + target: 'http://localhost:8000', + changeOrigin: true, + }, + }, + }, +}) diff --git a/github_repo_extractor.py b/github_repo_extractor.py index 399b966..07b3629 100644 --- a/github_repo_extractor.py +++ b/github_repo_extractor.py @@ -149,10 +149,14 @@ def extract_github_repo_contents(repo_url, save_to_disk=True): TABLE_NAME = os.environ.get("GCP_TABLE_NAME", "github_repo_data") # Function to process a GitHub repo and store embeddings in BigQuery -def process_github_repo_to_bq(repo_url, branch="main", file_filter=None, access_token=None): +def process_github_repo_to_bq(repo_url, branch="main", file_filter=None, access_token=None, dataset_name=None): """ Loads files from a GitHub repo, creates embeddings, and stores them in BigQuery. + + Args: + dataset_name: Optional override for the BQ dataset name. Defaults to GCP_DATASET_NAME env var. """ + effective_dataset = dataset_name or DATASET_NAME # Extract owner and repo first, and validate the URL try: owner, repo = parse_github_url(repo_url) @@ -166,7 +170,7 @@ def process_github_repo_to_bq(repo_url, branch="main", file_filter=None, access_ # Query to check for existing data query = f""" SELECT COUNT(*) as count - FROM `{PROJECT_ID}.{DATASET_NAME}.{TABLE_NAME}` + FROM `{PROJECT_ID}.{effective_dataset}.{TABLE_NAME}` WHERE repo_username = '{owner}' AND repo_name = '{repo}' """ @@ -221,7 +225,7 @@ def process_github_repo_to_bq(repo_url, branch="main", file_filter=None, access_ bq_store = BigQueryVectorStore( project_id=PROJECT_ID, location=LOCATION, - dataset_name=DATASET_NAME, + dataset_name=effective_dataset, table_name=TABLE_NAME, embedding=embedding_model, ) @@ -243,11 +247,15 @@ def process_github_repo_to_bq(repo_url, branch="main", file_filter=None, access_ logger.info("github_repo_extractor.py: Added %d documents to BigQuery vector store.", len(doc_ids)) -def query_github_vector_store(query, top_k=5): +def query_github_vector_store(query, top_k=5, dataset_name=None): """ Query the BigQuery vector store for relevant document chunks using a natural language query. Returns a list of (content, metadata) tuples. + + Args: + dataset_name: Optional override for the BQ dataset name. Defaults to GCP_DATASET_NAME env var. """ + effective_dataset = dataset_name or DATASET_NAME # 1. Create Vertex AI Embeddings (same as used for ingestion) embedding_model = GoogleGenerativeAIEmbeddings( model="text-embedding-005", project=PROJECT_ID, vertexai=True @@ -257,7 +265,7 @@ def query_github_vector_store(query, top_k=5): bq_store = BigQueryVectorStore( project_id=PROJECT_ID, location=LOCATION, - dataset_name=DATASET_NAME, + dataset_name=effective_dataset, table_name=TABLE_NAME, embedding=embedding_model, ) diff --git a/package-lock.json b/package-lock.json new file mode 100644 index 0000000..8591261 --- /dev/null +++ b/package-lock.json @@ -0,0 +1,6 @@ +{ + "name": "Job_Application_Agent", + "lockfileVersion": 3, + "requires": true, + "packages": {} +} diff --git a/webapp_plan.md b/webapp_plan.md new file mode 100644 index 0000000..50b0a55 --- /dev/null +++ b/webapp_plan.md @@ -0,0 +1,491 @@ +# Job Application Agent — Full-Stack Web Application Plan + +Build a production-grade web interface for the existing CrewAI-based Job Application Agent using **FastAPI** (backend), **React + Vite** (frontend), **Google Cloud SQL for MySQL** (user data), and **Google Cloud Storage** (resume files). + +--- + +## Resolved Decisions + +| Decision | Resolution | +|----------|------------| +| **GCP Region** | `asia-south2` for all resources (Cloud SQL, GCS, BigQuery) | +| **Cloud SQL Tier** | `db-f1-micro` (cheapest, ~$7/mo) | +| **Authentication** | JWT + OAuth (Google Sign-In) | +| **BQ Dataset Naming** | Sanitized: `arijitde2050_gmail_com_github_bq_db` | +| **Interview Materials** | Stored in GCS + tracked in `jobs` table | +| **GCS Bucket Name** | `job-applier--resumes` | +| **Max Resume Upload** | 10 MB | +| **Concurrency** | Concurrent crew executions (asyncio + thread pool) | +| **Deployment** | Dockerized → GCP Cloud Run | + +--- + +## Current Architecture Understanding + +### Existing Agent Pipeline + +```mermaid +graph LR + A["Job_Applier.py
(Entrypoint)"] -->|"extract job details
via Bright Data MCP"| B["webpage_extractor.py
extract_linkedin_job_details()"] + A -->|"kickoff crew"| C["CrewAI Crew"] + C --> D["Agent 1: GitHub Summarizer
Tools: github_repos_extractor,
repo_content_searcher"] + C --> E["Agent 2: Profiler
Tools: FileRead, MDXSearch"] + C --> F["Agent 3: Resume Strategist
Tools: Scrape, Search,
FileRead, MDXSearch"] + C --> G["Agent 4: Interview Preparer
Tools: Scrape, Search,
FileRead, MDXSearch"] + D -->|"index repos in
BigQuery vector store"| H["github_repo_extractor.py
process_github_repo_to_bq()"] + D --> E --> F --> G + F -->|"output"| I["Tailored Resume .md"] + G -->|"output"| J["interview_materials.md"] +``` + +**Key observations:** +- `Job_Applier.py` pre-fetches job details via `extract_linkedin_job_details()` then passes JSON to all crew tasks +- `github_repo_extractor.py` uses `GithubFileLoader` → Vertex AI embeddings → BigQuery vector store +- BigQuery dataset/table are configured via env vars (`GCP_DATASET_NAME`, `GCP_TABLE_NAME`) +- The crew outputs a tailored resume as `{applicant_name}_{company_name}_{job_name}_resume.md` locally +- LLM: Gemini 2.5 Flash via `gemini/gemini-2.5-flash` + +--- + +## Proposed Changes + +### New Directory Structure + +``` +Job_Application_Agent/ +├── backend/ # FastAPI application +│ ├── __init__.py +│ ├── main.py # FastAPI app entry point, CORS, lifespan +│ ├── config.py # Settings from env vars (Pydantic BaseSettings) +│ ├── database.py # SQLAlchemy engine, session, Base +│ ├── models.py # SQLAlchemy ORM models (users, github, resumes, jobs) +│ ├── schemas.py # Pydantic request/response schemas +│ ├── auth.py # JWT token creation, password hashing, auth deps +│ ├── gcs.py # GCS upload/download helpers +│ ├── crew_runner.py # Crew execution wrapper with progress callbacks +│ ├── requirements.txt # Backend-specific dependencies +│ └── routers/ +│ ├── __init__.py +│ ├── auth_router.py # POST /register, POST /login +│ ├── github_router.py # POST /github, GET /github, DELETE /github +│ ├── resume_router.py # POST /resume/upload, GET /resumes, DELETE /resume +│ └── job_router.py # POST /job/tailor, GET /jobs, GET /job/{id}/status +├── frontend/ # React + Vite application +│ ├── index.html +│ ├── package.json +│ ├── vite.config.js +│ ├── public/ +│ └── src/ +│ ├── main.jsx +│ ├── App.jsx +│ ├── App.css +│ ├── index.css # Global design tokens +│ ├── api/ +│ │ └── client.js # Axios instance with JWT interceptor +│ ├── context/ +│ │ └── AuthContext.jsx # Auth state management +│ ├── pages/ +│ │ ├── LandingPage.jsx # USP, hero, features, sign-up/login +│ │ ├── RegisterPage.jsx # User registration form +│ │ ├── HomePage.jsx # Dashboard after login +│ │ └── InterviewMaterialsPage.jsx # View interview materials (markdown) +│ └── components/ +│ ├── Navbar.jsx +│ ├── LoginSection.jsx +│ ├── ResumeUpload.jsx +│ ├── GithubProfileAdd.jsx +│ ├── TailoredResumeList.jsx +│ ├── TailorResumeForm.jsx +│ ├── ProgressTracker.jsx +│ ├── ResumePreviewModal.jsx +│ └── ResumeReadyModal.jsx +├── agents.py # MODIFIED — accept dynamic DB name +├── tasks.py # MODIFIED — dynamic output paths +├── github_repo_extractor.py # MODIFIED — accept DB name parameter +├── webpage_extractor.py # Unchanged +├── logger.py # Unchanged +├── Job_Applier.py # Kept for standalone CLI usage +└── requirements.txt # Existing (unchanged) +``` + +--- + +### Component 1: Google Cloud Infrastructure + +#### GCS Bucket Setup + +Create a single GCS bucket with the following folder structure: +``` +gs:/// +├── uploads/ +│ └── / # User-uploaded original resumes +│ └── .md +└── tailored/ + └── / # Agent-generated tailored resumes + └── ___resume.md +``` + +**Setup command:** +```bash +gsutil mb -l asia-south2 -c STANDARD gs://job-applier--resumes +gsutil uniformbucketlevelaccess set on gs://job-applier--resumes +``` + +#### Cloud SQL for MySQL Setup + +- Instance name: `job-applier-mysql` +- MySQL version: 8.0 +- Region: `asia-south2` +- Add the service account and app's IP to authorized networks + +--- + +### Component 2: Database Schema (Cloud SQL MySQL) + +#### [NEW] [models.py](file:///home/arijit/Documents/github/Job_Application_Agent/backend/models.py) + +```sql +-- users table +CREATE TABLE users ( + id INT AUTO_INCREMENT PRIMARY KEY, + first_name VARCHAR(100) NOT NULL, + last_name VARCHAR(100) NOT NULL, + email VARCHAR(255) NOT NULL UNIQUE, + password_hash VARCHAR(255) NOT NULL, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP, + INDEX idx_email (email) +); + +-- github_profiles table +CREATE TABLE github_profiles ( + id INT AUTO_INCREMENT PRIMARY KEY, + user_id INT NOT NULL, + github_username VARCHAR(100) NOT NULL, + github_url VARCHAR(500) NOT NULL, + bq_dataset_name VARCHAR(255) NOT NULL, -- e.g. "user_email_github_bq_db" + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + FOREIGN KEY (user_id) REFERENCES users(id) ON DELETE CASCADE, + UNIQUE KEY uq_user_github (user_id, github_username) +); + +-- resumes table +CREATE TABLE resumes ( + id INT AUTO_INCREMENT PRIMARY KEY, + user_id INT NOT NULL, + original_filename VARCHAR(255) NOT NULL, + gcs_path VARCHAR(500) NOT NULL, -- gs://bucket/uploads//.md + uploaded_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + FOREIGN KEY (user_id) REFERENCES users(id) ON DELETE CASCADE +); + +-- jobs table +CREATE TABLE jobs ( + id INT AUTO_INCREMENT PRIMARY KEY, + user_id INT NOT NULL, + github_profile_id INT NOT NULL, + resume_id INT NOT NULL, + linkedin_job_url VARCHAR(500) NOT NULL, + job_name VARCHAR(255), + company_name VARCHAR(255), + location VARCHAR(255), + seniority_level VARCHAR(100), + employment_type VARCHAR(100), + job_function VARCHAR(255), + industry VARCHAR(255), + job_description TEXT, + requirements TEXT, + tailored_resume_gcs_path VARCHAR(500), -- gs://bucket/tailored//_resume.md + interview_materials_gcs_path VARCHAR(500), -- gs://bucket/tailored//_interview.md + status ENUM('pending','processing','completed','failed') DEFAULT 'pending', + error_message TEXT, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + completed_at TIMESTAMP NULL, + FOREIGN KEY (user_id) REFERENCES users(id) ON DELETE CASCADE, + FOREIGN KEY (github_profile_id) REFERENCES github_profiles(id) ON DELETE RESTRICT, + FOREIGN KEY (resume_id) REFERENCES resumes(id) ON DELETE RESTRICT +); +``` + +**Key design decisions:** +- `github_profiles` and `resumes` use `ON DELETE CASCADE` — deleting a user removes their profiles/resumes +- `jobs` uses `ON DELETE RESTRICT` for `github_profile_id` and `resume_id` — can't delete a github profile or resume that has been used in a job application +- `jobs.status` tracks crew execution state for progress UI +- `bq_dataset_name` stored per github profile so the system knows which BQ dataset to query + +--- + +### Component 3: FastAPI Backend + +#### [NEW] [config.py](file:///home/arijit/Documents/github/Job_Application_Agent/backend/config.py) + +Pydantic `BaseSettings` loading from `.env`: +- `MYSQL_HOST`, `MYSQL_PORT`, `MYSQL_USER`, `MYSQL_PASSWORD`, `MYSQL_DATABASE` +- `GCS_BUCKET_NAME` +- `JWT_SECRET_KEY`, `JWT_ALGORITHM=HS256`, `JWT_EXPIRY_MINUTES=1440` +- `GOOGLE_OAUTH_CLIENT_ID`, `GOOGLE_OAUTH_CLIENT_SECRET` +- All existing env vars (GCP project, Gemini key, Bright Data key, etc.) + +#### [NEW] [database.py](file:///home/arijit/Documents/github/Job_Application_Agent/backend/database.py) + +- SQLAlchemy async engine with `asyncmy` driver connecting to Cloud SQL +- Session factory, dependency injection via `get_db()` +- `Base.metadata.create_all()` for table creation on startup + +#### [NEW] [auth.py](file:///home/arijit/Documents/github/Job_Application_Agent/backend/auth.py) + +- `hash_password(plain)` → bcrypt hash +- `verify_password(plain, hashed)` → bool +- `create_access_token(user_id, email)` → JWT string +- `get_current_user(token)` → FastAPI dependency that extracts & validates JWT +- `google_oauth_login(id_token)` → Verify Google OAuth ID token, create/find user, return JWT +- Uses `google-auth` library to verify Google ID tokens server-side +- On first Google OAuth login, creates a user record (password_hash set to empty — OAuth-only users) + +#### [NEW] [gcs.py](file:///home/arijit/Documents/github/Job_Application_Agent/backend/gcs.py) + +- `upload_resume(user_id, file_bytes, filename)` → returns `gcs_path` (max 10MB enforced) +- `upload_tailored_resume(user_id, job_id, content, filename)` → returns `gcs_path` +- `upload_interview_materials(user_id, job_id, content, filename)` → returns `gcs_path` +- `download_file(gcs_path)` → returns file bytes +- `generate_signed_url(gcs_path, expiry_minutes=15)` → returns presigned URL for preview + +#### [NEW] [crew_runner.py](file:///home/arijit/Documents/github/Job_Application_Agent/backend/crew_runner.py) + +Wraps the existing crew execution with progress tracking: + +```python +async def run_crew_for_job(job_id, user, github_profile, resume_gcs_path, job_url): + """ + 1. Update job status to 'processing' + 2. Extract job details via extract_linkedin_job_details() + 3. Store extracted details in jobs table + 4. Download user's resume from GCS to temp file + 5. Build crew inputs (dynamic BQ dataset name from github_profile) + 6. Kick off crew with progress callbacks + 7. Upload tailored resume to GCS + 8. Update job record with tailored_resume_gcs_path, status='completed' + """ +``` + +**Concurrency**: Each crew execution runs in a `ThreadPoolExecutor` thread via `asyncio.run_in_executor()`, allowing multiple users to tailor resumes simultaneously. + +**Progress tracking via SSE (Server-Sent Events):** +- Each crew step updates a shared dict `progress_store[job_id]` +- Steps: `extracting_job_info` → `indexing_github_repos` → `searching_projects` → `building_profile` → `tailoring_resume` → `generating_interview_prep` → `uploading_results` → `completed` +- Frontend connects via `GET /api/jobs/{id}/progress` (SSE endpoint) + +#### API Endpoints Summary + +| Method | Endpoint | Description | +|--------|----------|-------------| +| `POST` | `/api/auth/register` | Create new user account | +| `POST` | `/api/auth/login` | Login, returns JWT token | +| `POST` | `/api/auth/google` | Google OAuth login/register | +| `GET` | `/api/auth/me` | Get current user profile | +| `POST` | `/api/github` | Add a GitHub username | +| `GET` | `/api/github` | List user's GitHub profiles | +| `DELETE` | `/api/github/{id}` | Remove a GitHub profile | +| `POST` | `/api/resumes/upload` | Upload a `.md` resume to GCS | +| `GET` | `/api/resumes` | List user's uploaded resumes | +| `GET` | `/api/resumes/{id}/preview` | Get signed URL for resume preview | +| `DELETE` | `/api/resumes/{id}` | Delete a resume (if not used in a job) | +| `POST` | `/api/jobs/tailor` | Start crew execution for a job | +| `GET` | `/api/jobs` | List user's tailored jobs | +| `GET` | `/api/jobs/{id}` | Get job details + tailored resume URL | +| `GET` | `/api/jobs/{id}/progress` | SSE stream for crew progress | +| `GET` | `/api/jobs/{id}/resume/download` | Download tailored resume .md file | +| `GET` | `/api/jobs/{id}/interview/download` | Download interview materials .md file | +| `GET` | `/api/jobs/{id}/interview/content` | Get interview materials markdown content for in-browser viewing | + +--- + +### Component 4: Existing Code Modifications + +#### [MODIFY] [github_repo_extractor.py](file:///home/arijit/Documents/github/Job_Application_Agent/github_repo_extractor.py) + +- `process_github_repo_to_bq()`: Add optional `dataset_name` parameter (defaults to env var for backward compatibility). When called from the web app, pass the user-specific dataset name. +- `query_github_vector_store()`: Same — add optional `dataset_name` parameter. + +#### [MODIFY] [agents.py](file:///home/arijit/Documents/github/Job_Application_Agent/agents.py) + +- Refactor `extract_github_repos_tool` and `repo_content_searcher` to accept `dataset_name` as a parameter (passed via crew input variables). +- The tools will use the user-specific BQ dataset when called from the web app. + +#### [MODIFY] [tasks.py](file:///home/arijit/Documents/github/Job_Application_Agent/tasks.py) + +- `resume_strategy_task`: Change `output_file` to a temp path that the web app can read and upload to GCS. +- Add `{bq_dataset_name}` as a crew input variable passed to tasks that use BQ. + +--- + +### Component 5: React Frontend + +#### Design System + +- **Font**: Inter (Google Fonts) +- **Color Palette**: Dark mode primary with vibrant accents + - Background: `#0a0a0f` (deep navy-black) + - Surface: `#13131a` / `#1a1a2e` + - Primary accent: `#6c63ff` (vibrant indigo) + - Secondary accent: `#00d4aa` (teal-green for success) + - Text: `#e8e8f0` / `#9898a8` (muted) + - Error: `#ff4757` +- **Effects**: Glassmorphism cards, subtle gradient borders, smooth transitions + +#### Page: Landing Page (`LandingPage.jsx`) + +**Sections:** +1. **Hero Section**: Full-width gradient background with animated particles or subtle mesh gradient. Large headline: *"Your Dream Job Deserves a Perfect Resume"*, sub-headline addressing the pain points. CTA buttons for Sign Up / Learn More. + +2. **Problem Section** (with icons/illustrations): + - "Spending hours customizing resumes for each application?" + - "Struggling to pass ATS keyword screening?" + - "Not sure which projects to highlight for each role?" + - "LinkedIn job postings are hard to decode quickly" + +3. **Solution/How It Works** (3-step visual flow): + - Step 1: Upload your resume & link your GitHub + - Step 2: Paste a LinkedIn job URL + - Step 3: AI agents analyze, match, and tailor your resume + +4. **Features Grid** (glassmorphism cards): + - AI-Powered ATS Optimization + - GitHub Project Analysis + - Smart Keyword Matching + - Instant Resume Tailoring + +5. **Login / Sign Up Section**: A split card at the bottom — + - **Left side**: Login form (email + password + Login button) + "Sign in with Google" OAuth button + - **Right side**: Sign Up prompt with a "Create Account" button that navigates to `/register` + +#### Page: Register Page (`RegisterPage.jsx`) + +- Clean form: First Name, Last Name, Email, Password, Confirm Password +- "Sign up with Google" OAuth button as alternative +- Client-side validation (email format, password min length 8, password match) +- On success → redirect to login or auto-login + +#### Page: Home Page (`HomePage.jsx`) — Protected Route + +**Layout: Single-page dashboard with stacked sections** + +**i) Welcome Header** +- "Welcome back, {first_name}!" with user avatar placeholder +- Logout button in navbar + +**ii) Previously Tailored Resumes Section** +- Table/card list showing: Job Title, Company, Date, Status (completed/processing/failed) +- Each completed row has: + - "View Resume" button → opens markdown preview modal + - "View Interview Materials" button → navigates to `/interview/{job_id}` page + - "⬇ Resume" download button → triggers `.md` file download + - "⬇ Interview" download button → triggers `.md` file download +- Empty state: "No tailored resumes yet. Start by tailoring your first resume below!" + +**iii) Manage Resources Section** (two side-by-side cards) + +**Resume Upload Card:** +- Drag-and-drop zone or file picker (accepts `.md` only) +- Info banner: "Only Markdown (.md) files accepted" with conversion links: + - [PDF → Markdown](https://pdf2md.morethan.io/) + - [Word → Markdown](https://word2md.com/) +- List of uploaded resumes with delete option +- Upload triggers `POST /api/resumes/upload` + +**GitHub Profile Card:** +- Text input for GitHub username + "Add" button +- List of added GitHub usernames with delete option + +**iv) Tailor Resume Section** +- **Job URL input**: LinkedIn job posting URL text field +- **GitHub Profile dropdown**: Select from user's added profiles +- **Resume dropdown**: Select from user's uploaded resumes +- **"Preview Resume" button**: Opens a modal showing the selected resume content (fetched via signed URL, rendered as markdown) +- **"Tailor My Resume" button**: Triggers `POST /api/jobs/tailor` + +**v) Progress Tracker** (appears after clicking "Tailor My Resume") +- Vertical stepper / progress bar with labeled steps: + 1. ⏳ Extracting job information + 2. ⏳ Searching GitHub projects + 3. ⏳ Ingesting GitHub project info + 4. ⏳ Creating user profile + 5. ⏳ Understanding job requirements + 6. ⏳ Tailoring your resume +- Each step shows: pending (grey) → in-progress (pulsing accent) → completed (green check) +- Connects to SSE endpoint `GET /api/jobs/{id}/progress` + +**vi) Resume Ready Modal** +- Triggered when SSE reports `completed` +- "🎉 Your tailored resume is ready!" +- Buttons: + - "View Resume" → opens markdown preview modal + - "View Interview Materials" → navigates to `/interview/{job_id}` + - "⬇ Download Resume" → downloads tailored resume `.md` file + - "⬇ Download Interview Materials" → downloads interview materials `.md` file + - "Close" +- On close, the new entry appears in the "Previously Tailored Resumes" section + +#### Page: Interview Materials Page (`InterviewMaterialsPage.jsx`) — Route: `/interview/:jobId` + +- Fetches interview materials markdown content via `GET /api/jobs/{id}/interview/content` +- Renders the markdown beautifully in-browser using `react-markdown` with syntax highlighting +- Header shows: Job Title, Company Name, Date created +- "⬇ Download" button in the top-right corner +- "← Back to Dashboard" navigation link +- Styled consistently with the dark-mode glassmorphism design system + +--- + +### Component 6: Error Handling + +| Layer | Error | Handling | +|-------|-------|----------| +| **Backend** | Invalid/expired JWT | Return 401, frontend redirects to login | +| **Backend** | Duplicate email on register | Return 409 Conflict | +| **Backend** | Resume not `.md` | Return 400 with message | +| **Backend** | Resume too large (>5MB) | Return 413 | +| **Backend** | GCS upload failure | Return 500, log error | +| **Backend** | Crew execution failure | Set job status to `failed`, store `error_message` | +| **Backend** | LinkedIn scraping failure | Return error in job details, set status `failed` | +| **Backend** | BQ dataset creation failure | Return 500, log error | +| **Frontend** | Network errors | Toast notifications with retry option | +| **Frontend** | Form validation | Inline error messages under fields | +| **Frontend** | SSE connection lost | Auto-reconnect with exponential backoff | +| **Frontend** | Upload wrong file type | Block upload, show warning banner | + +--- + +## Execution Order + +```mermaid +graph TD + A["Phase 1: Infrastructure
Cloud SQL + GCS Bucket"] --> B["Phase 2: Backend Foundation
config, database, models, auth"] + B --> C["Phase 3: Backend APIs
auth, github, resume, job routers"] + C --> D["Phase 4: Agent Modifications
Dynamic BQ dataset, crew_runner"] + D --> E["Phase 5: Frontend Foundation
Vite setup, design system, routing"] + E --> F["Phase 6: Frontend Pages
Landing, Register, Home"] + F --> G["Phase 7: Integration
SSE progress, file preview, modals"] + G --> H["Phase 8: Testing & Polish
Error handling, animations, UX"] +``` + +--- + +## Verification Plan + +### Automated Tests +- **Backend unit tests**: pytest for auth (register, login, JWT), CRUD operations on all tables, GCS mock upload/download +- **API integration tests**: httpx async client against FastAPI test server +- **Frontend**: Manual browser testing via browser tool — verify all pages render, forms submit, progress tracker works + +### Manual Verification +1. Register a new user → verify record in Cloud SQL +2. Upload a `.md` resume → verify file in GCS bucket + DB record +3. Add a GitHub username → verify DB record +4. Submit a job URL with selected github + resume → verify: + - Job details extracted and stored in `jobs` table + - Progress SSE stream shows correct steps + - Tailored resume uploaded to GCS + - Job status updated to `completed` + - Entry appears in "Previously Tailored Resumes" list +5. Test error cases: wrong file type, duplicate email, invalid job URL, expired JWT From c18a188a96e6836fd242e0ce1a2c63ed7f3765fb Mon Sep 17 00:00:00 2001 From: Arijit De Date: Tue, 16 Jun 2026 18:16:10 +0530 Subject: [PATCH 02/11] refactor: configure agent execution limits, add log directory, and remove resume file and redundant DB sync --- .gitignore | 3 +- ...Artificial Intelligence Engineer_resume.md | 90 ------------------- agents.py | 20 +++-- backend/config.py | 13 ++- backend/crew_runner.py | 66 +++++++------- example_env.txt | 26 +++++- frontend/package-lock.json | 10 +-- 7 files changed, 90 insertions(+), 138 deletions(-) delete mode 100644 Arijit_De_hoichoi_Artificial Intelligence Engineer_resume.md diff --git a/.gitignore b/.gitignore index c368141..931bff6 100644 --- a/.gitignore +++ b/.gitignore @@ -164,4 +164,5 @@ cython_debug/ db/ crew_log.txt project-69718baa-b6cc-44ec-9fb-17c2c8f0dd9c.json -client_secret_720140080607-c0cuhvl4bvqtnk6tgicqds8136iqgrdv.apps.googleusercontent.com.json \ No newline at end of file +client_secret_720140080607-c0cuhvl4bvqtnk6tgicqds8136iqgrdv.apps.googleusercontent.com.json +logs/ \ No newline at end of file diff --git a/Arijit_De_hoichoi_Artificial Intelligence Engineer_resume.md b/Arijit_De_hoichoi_Artificial Intelligence Engineer_resume.md deleted file mode 100644 index fb1fc2b..0000000 --- a/Arijit_De_hoichoi_Artificial Intelligence Engineer_resume.md +++ /dev/null @@ -1,90 +0,0 @@ -# Arijit De -*Machine Learning Engineer | AI/ML Systems | Generative AI | MLOps* - -Kolkata, India | [LinkedIn Profile](https://www.linkedin.com/in/arijit-de/) | [GitHub Profile](https://github.com/arijitde) - -## Summary - -Highly accomplished Machine Learning Engineer with 2-6 years of hands-on experience in designing, training, and deploying robust AI/ML systems in production environments. Specializes in Deep Learning, Computer Vision, and Natural Language Processing, with a proven track record in building `Platform Intelligence` and `Creative AI` solutions. Expert in `Generative AI`, `LLMs`, `prompt engineering`, and `RAG architectures`, leveraging frameworks like PyTorch and cloud platforms (GCP). Passionate about developing scalable `MLOps foundations`, driving `model performance`, and championing `responsible AI practices` to transform complex data into actionable insights and innovative products. Adept at translating ambiguous creative problems into well-scoped AI features for diverse stakeholders. - -## Work Experience - -**Machine Learning Engineer** | mVizn Pte. Ltd. | Singapore -*Jan 2022 – Present* - -* Led the end-to-end development and deployment of a Deep Learning pipeline for 3D point cloud semantic segmentation, enhancing `model performance` by 30% and improving system robustness for critical applications. -* Directed backend development and deployed `AI models` on `GCP` (VMs, Cloud Storage) with Python and `MySQL` integration, leading to a 60% improvement in `system performance` and `scalability` for a unified intelligence layer. -* Implemented `GCP IAM` for secure role-based access control and resolved critical post-deployment issues, collaborating with 5+ stakeholders to align technical solutions with business needs and ensure continuous `model monitoring` and iteration. -* Developed `APIs` and `model-serving infrastructure` to enable direct consumption of `AI-driven insights` by various teams, streamlining data access and decision-making. - -**Machine Learning Engineer** | Mercedes-Benz Research and Development India | Bengaluru, India -*Aug 2020 – Jan 2022* - -* Developed and deployed an `AI system` for Vulnerable Road User (VRU) detection using a `Yolo v3 model`, significantly improving `Level 3 ADAS capabilities` by 30% in real-world scenarios. -* Created manual quality control tools with `PyQt` for validating annotated images, increasing `annotation accuracy` by 25% and ensuring high-quality training data. -* Automated quality checks and `data analysis` using `Python scripts`, boosting `annotation throughput` by 20% and optimizing data processing pipelines. - -**Systems Engineer** | Tata Consultancy Services | Bengaluru, India -*Sep 2018 – Aug 2020* - -* Maintained and enhanced a large-scale internal website, implementing numerous feature requests across frontend (`HTML`, `Bootstrap CSS`, `JavaScript`) and backend (`Java`, `MySQL`). -* Collaborated with cross-functional teams to streamline release cycles and deliver high-quality software solutions, demonstrating strong teamwork in complex system management. - -## Projects - -**Job Application Agent** (Ongoing) -* Developed an `AI agent` using `LangChain`, `Crew AI`, `Google Cloud BigQuery`, and `Vertex AI` for automated job application tasks, demonstrating practical application of `GenAI workflows` and `MLOps tools`. -* Implemented `RAG architectures` to enhance agent's knowledge retrieval and contextual understanding for tailored applications. - -**Spiritual Chatbot** -* Utilized `OpenAI GPT-3.5 turbo` and `LangChain` for `prompt engineering` to develop a conversational `LLM` capable of providing spiritual guidance, showcasing expertise in `fine-tuning` and `evaluating model outputs`. - -**Github Code Analysis Tool** -* Employed `OpenAI GPT-3.5 turbo`, `LangChain`, and `DeepLake` `vector database` to build a tool for code analysis, demonstrating experience with `RAG systems` and efficient data retrieval for `LLM` applications. - -**3D Brain Analysis and Visualization App** -* Developed a `Flask` application for 3D brain MRI segmentation and visualization, highlighting expertise in `computer vision` and `REST API development` for medical imaging solutions. - -## Skills - -**Programming & Frameworks:** -* **Python:** `PyTorch`, `TensorFlow`, `Scikit-Learn`, `Numpy`, `Pandas`, `OpenCV`, `Flask` -* **SQL:** `MySQL`, `Google Cloud BigQuery` -* **Other:** C++, HTML, CSS, JavaScript, Java - -**AI/ML & Generative AI:** -* `Deep Learning`, `Computer Vision`, `Natural Language Processing` -* `Generative AI (GenAI)`, `Large Language Models (LLMs)` -* `Prompt Engineering`, `Fine-tuning`, `RAG Architectures` -* `Semantic Segmentation`, `Object Detection`, `Audience Segmentation`, `Content Performance Prediction` -* `Model Evaluation`, `A/B Testing`, `Bias Audits`, `Explainability` - -**Cloud & MLOps:** -* `Google Cloud Platform (GCP)`: VMs, Cloud Storage, IAM, `Vertex AI` -* `MLOps Tools`: `Model Registry`, `Experiment Tracking`, `Feature Store`, `Deployment Pipelines` -* `Data Pipeline Tools`: `Data Ingestion`, `ETL`, (equivalent experience to `Airflow`/`dbt` through BigQuery and pipeline ownership) - -**Databases & Tools:** -* `SQL Databases`, `Vector Databases` (`DeepLake`) -* `Git`, `PyQt`, `LangChain`, `Crew AI` - -## Education - -**Ph.D. in Computer Science & Engineering** | Jadavpur University | Kolkata, India -*Specialization: Medical Image Analysis (Brain Segmentation, Tumor Classification)* -*Jan 2015 – Dec 2021* - -**Master of Engineering in Computer Science & Engineering** | Jadavpur University | Kolkata, India -*Aug 2013 – Jul 2015* - -**Bachelor of Engineering in Computer Science & Engineering** | Jadavpur University | Kolkata, India -*Aug 2009 – Jul 2013* - -## Publications & Certifications - -**Selected Publications:** -* Numerous publications in top-tier conferences and journals (`ICPR`, `IATMSI`, `ICIP`, `EMBC`, `Expert Syst. Appl.`) focusing on medical image analysis, deep graph cuts, and neurological disorder detection. - -**Certifications:** -* **Deep Learning Specialization** by Deeplearning.ai -* **TensorFlow in Practice Specialization** by Deeplearning.ai \ No newline at end of file diff --git a/agents.py b/agents.py index b319755..0dfd525 100644 --- a/agents.py +++ b/agents.py @@ -158,6 +158,9 @@ def repo_content_searcher(query: str, job_description: str = None, top_k: int = tools=[extract_github_repos_tool, repo_content_searcher], llm=gemini_llm, verbose=True, + max_iter=8, + max_rpm=10, + respect_context_window=True, backstory=( "You are an expert in analyzing GitHub repositories and summarizing project experience for job applications. " "You first ensure the user's repositories are indexed in the vector store using the github_repos_extractor tool. " @@ -171,9 +174,12 @@ def repo_content_searcher(query: str, job_description: str = None, top_k: int = profiler = Agent( role="Personal Profiler for Engineers", goal="Do incredible analytical research on job applicants to help them stand out in the job market", - tools = [read_resume, semantic_search_resume], + tools=[read_resume, semantic_search_resume], llm=gemini_llm, verbose=True, + max_iter=8, + max_rpm=10, + respect_context_window=True, backstory=( "Equipped with analytical prowess, you dissect and synthesize information " "from diverse sources to craft comprehensive personal and professional profiles," @@ -185,10 +191,12 @@ def repo_content_searcher(query: str, job_description: str = None, top_k: int = resume_strategist = Agent( role="Resume Strategist for Engineers", goal="Find all the best ways to make a resume stand out in the job market.", - tools = [scrape_tool, search_tool, - read_resume, semantic_search_resume], + tools=[scrape_tool, search_tool, read_resume, semantic_search_resume], llm=gemini_llm, verbose=True, + max_iter=8, + max_rpm=10, + respect_context_window=True, backstory=( "With a strategic mind and an eye for detail, you " "excel at refining resumes to highlight the most " @@ -202,10 +210,12 @@ def repo_content_searcher(query: str, job_description: str = None, top_k: int = role="Engineering Interview Preparer", goal="Create interview questions and talking points " "based on the resume and job requirements", - tools = [scrape_tool, search_tool, - read_resume, semantic_search_resume], + tools=[scrape_tool, search_tool, read_resume, semantic_search_resume], llm=gemini_llm, verbose=True, + max_iter=8, + max_rpm=10, + respect_context_window=True, backstory=( "Your role is crucial in anticipating the dynamics of " "interviews. With your ability to formulate key questions " diff --git a/backend/config.py b/backend/config.py index 0842ce0..a8b0703 100644 --- a/backend/config.py +++ b/backend/config.py @@ -52,6 +52,10 @@ class Settings(BaseSettings): GEMINI_API_KEY: str = Field(default="", description="Google Gemini API key") SERPER_API_KEY: str = Field(default="", description="Serper search API key") + # ── Crew AI ─────────────────────────────────────────────────────────── + CREWAI_TOOLS_ALLOW_UNSAFE_PATHS: bool = Field(default=True, description="Allow CrewAI tools to access unsafe paths") + CREWAI_TRACING_ENABLED: bool = Field(default=True, description="Enable CrewAI tracing") + # ── Resume Upload ───────────────────────────────────────────────────── MAX_RESUME_SIZE_MB: int = Field(default=10, description="Max resume upload size in MB") @@ -89,4 +93,11 @@ def max_resume_bytes(self) -> int: @lru_cache() def get_settings() -> Settings: """Return a cached Settings instance (reads .env once).""" - return Settings() + settings = Settings() + import os + if settings.GOOGLE_APPLICATION_CREDENTIALS: + os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = settings.GOOGLE_APPLICATION_CREDENTIALS + # Propagate CrewAI variables to os.environ for libraries that read them directly + os.environ["CREWAI_TOOLS_ALLOW_UNSAFE_PATHS"] = str(settings.CREWAI_TOOLS_ALLOW_UNSAFE_PATHS).lower() + os.environ["CREWAI_TRACING_ENABLED"] = str(settings.CREWAI_TRACING_ENABLED).lower() + return settings diff --git a/backend/crew_runner.py b/backend/crew_runner.py index 85b623f..1be1393 100644 --- a/backend/crew_runner.py +++ b/backend/crew_runner.py @@ -11,6 +11,7 @@ import tempfile from concurrent.futures import ThreadPoolExecutor from datetime import datetime +from pathlib import Path from typing import Dict, Any from sqlalchemy import select, update @@ -30,6 +31,9 @@ # In-memory progress store: {job_id: {"current_step": str, "error": str|None}} progress_store: Dict[int, Dict[str, Any]] = {} +# ── Crew verbose log path ───────────────────────────────────────────────────── +_LOG_DIR = Path(__file__).parent.parent / "logs" + def _update_progress(job_id: int, step: str, error: str = None): """Update the in-memory progress store for SSE broadcasting.""" @@ -61,8 +65,6 @@ def _run_crew_sync( logger.error("crew_runner.py: Failed to extract job details for job %d: %s", job_id, e) raise RuntimeError(f"Failed to extract job details: {e}") - # Save extracted job details to DB - _save_job_details_sync(job_id, job_details) # Step 2: Write resume to temp file for crew to read _update_progress(job_id, "searching_projects") @@ -92,11 +94,17 @@ def _run_crew_sync( resume_strategy_task.output_file = resume_output interview_preparation_task.output_file = interview_output + _LOG_DIR.mkdir(parents=True, exist_ok=True) + ts = datetime.now().strftime("%Y%m%d_%H%M%S") + crew_log_path = str(_LOG_DIR / f"{ts}_crew.log") + crew = Crew( agents=[github_project_summarizer, profiler, resume_strategist, interview_preparer], tasks=[github_summary_task, profile_task, resume_strategy_task, interview_preparation_task], verbose=True, + output_log_file=crew_log_path, ) + logger.info("crew_runner.py: Crew verbose log → %s", crew_log_path) _update_progress(job_id, "tailoring_resume") @@ -112,7 +120,7 @@ def _run_crew_sync( } result = crew.kickoff(inputs=job_application_inputs) - logger.info("crew_runner.py: Crew execution completed for job %d", job_id) + logger.info("crew_runner.py: Crew execution completed for job %d. Log saved to %s", job_id, crew_log_path) # Read output files tailored_resume_content = "" @@ -141,38 +149,6 @@ def _run_crew_sync( os.unlink(path) -def _save_job_details_sync(job_id: int, job_details): - """Save extracted job details to the database (sync, called from thread).""" - import asyncio - - async def _save(): - async with AsyncSessionLocal() as db: - await db.execute( - update( - __import__("backend.models", fromlist=["Job"]).Job - ).where( - __import__("backend.models", fromlist=["Job"]).Job.id == job_id - ).values( - job_name=job_details.job_name, - company_name=job_details.company_name, - location=job_details.location, - seniority_level=job_details.seniority_level, - employment_type=job_details.employment_type, - job_function=job_details.job_function, - industry=job_details.industry, - job_description=job_details.job_description, - requirements=job_details.requirements, - ) - ) - await db.commit() - - # Run async DB update from sync thread - loop = asyncio.new_event_loop() - try: - loop.run_until_complete(_save()) - finally: - loop.close() - async def run_crew_for_job( job_id: int, user_id: int, user_email: str, @@ -206,6 +182,26 @@ async def run_crew_for_job( github_profile.bq_dataset_name, resume_content, job_url, ) + # Save extracted job details to DB on the correct async event loop + job_details = result["job_details"] + async with AsyncSessionLocal() as db: + from backend.models import Job + await db.execute( + update(Job).where(Job.id == job_id).values( + job_name=job_details.job_name, + company_name=job_details.company_name, + location=job_details.location, + seniority_level=job_details.seniority_level, + employment_type=job_details.employment_type, + job_function=job_details.job_function, + industry=job_details.industry, + job_description=job_details.job_description, + requirements=job_details.requirements, + ) + ) + await db.commit() + logger.info("crew_runner.py: Job details saved for job %d", job_id) + # Upload results to GCS _update_progress(job_id, "uploading_results") diff --git a/example_env.txt b/example_env.txt index 7de9932..81eb081 100644 --- a/example_env.txt +++ b/example_env.txt @@ -22,4 +22,28 @@ BRIGHT_DATA_API_KEY= # Logging Configuration # Console log level: DEBUG | INFO | WARNING | ERROR | CRITICAL (default: INFO) -LOG_LEVEL=INFO \ No newline at end of file +LOG_LEVEL=INFO + +# ── Web App Configuration ────────────────────────────────────────── +# Cloud SQL MySQL +MYSQL_HOST=127.0.0.1 +MYSQL_PORT=3306 +MYSQL_USER= +MYSQL_PASSWORD= +MYSQL_DATABASE=job_applier + +# GCS Bucket for resumes +GCS_BUCKET_NAME= + +# JWT Authentication +JWT_SECRET_KEY= +JWT_ALGORITHM=HS256 +JWT_EXPIRY_MINUTES=1440 + +# Google OAuth (optional — set up in GCP Console) +GOOGLE_OAUTH_CLIENT_ID= +GOOGLE_OAUTH_CLIENT_SECRET= + +# Crew AI +CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true +CREWAI_TRACING_ENABLED=true \ No newline at end of file diff --git a/frontend/package-lock.json b/frontend/package-lock.json index 65a901e..57ad64a 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -1743,16 +1743,16 @@ } }, "node_modules/form-data": { - "version": "4.0.5", - "resolved": "https://registry.npmjs.org/form-data/-/form-data-4.0.5.tgz", - "integrity": "sha512-8RipRLol37bNs2bhoV67fiTEvdTrbMUYcFTiy3+wuuOnUog2QBHCZWXDRijWQfAkhBj2Uf5UnVaiWwA5vdd82w==", + "version": "4.0.6", + "resolved": "https://registry.npmjs.org/form-data/-/form-data-4.0.6.tgz", + "integrity": "sha512-vKatAh4SlVfgbv+YtmhiRjhEMJsYpsG1Y2rMQtR+SVSbytsSD1YGzDIcrAJmdFec88u/+VoGmxnl+80gL1tRCQ==", "license": "MIT", "dependencies": { "asynckit": "^0.4.0", "combined-stream": "^1.0.8", "es-set-tostringtag": "^2.1.0", - "hasown": "^2.0.2", - "mime-types": "^2.1.12" + "hasown": "^2.0.4", + "mime-types": "^2.1.35" }, "engines": { "node": ">= 6" From 4da8f4d745f469392c7b14bbfbf06ff4006bbfe8 Mon Sep 17 00:00:00 2001 From: Arijit De Date: Wed, 17 Jun 2026 13:29:24 +0530 Subject: [PATCH 03/11] docs: add Cloud SQL Auth Proxy setup instructions and update crew log file naming convention --- README.md | 37 +++++++++++++++++++++++++++++++++---- backend/crew_runner.py | 2 +- 2 files changed, 34 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 364946a..3857cbe 100644 --- a/README.md +++ b/README.md @@ -132,8 +132,10 @@ cd .. Add the following database and storage configurations to the bottom of your `.env` file: ```env -# Cloud SQL MySQL (Direct Connection) -MYSQL_HOST=34.131.150.209 +# Cloud SQL MySQL Connection +# Use 127.0.0.1 when running Cloud SQL Auth Proxy (Recommended) +# Use 34.131.150.209 for direct connection (requires authorizing your public IP in GCP console) +MYSQL_HOST=127.0.0.1 MYSQL_PORT=3306 MYSQL_USER=root MYSQL_PASSWORD=JobAgent@2026 @@ -153,9 +155,36 @@ GOOGLE_OAUTH_CLIENT_ID=your-client-id.apps.googleusercontent.com GOOGLE_OAUTH_CLIENT_SECRET=your-client-secret ``` -#### 5. Run the Web Application +#### 5. Setup & Run Cloud SQL Auth Proxy (Recommended) -Start the backend API server and frontend development server in separate terminals: +Since dynamic public IP addresses change frequently, running the Cloud SQL Auth Proxy is the recommended way to securely connect to the Cloud SQL database from your local development environment without having to configure authorized IP networks in the GCP Console. + +##### A. Install the Proxy Client (Linux) +```bash +# Download the binary (for 64-bit Linux) +curl -o cloud-sql-proxy https://storage.googleapis.com/cloud-sql-connectors/cloud-sql-proxy/v2.11.0/cloud-sql-proxy.linux.amd64 + +# Make it executable +chmod +x cloud-sql-proxy + +# Move it to a directory in your PATH (optional, but recommended) +sudo mv cloud-sql-proxy /usr/local/bin/ +``` + +##### B. Run the Proxy +Start the proxy in a separate terminal using your GCP Service Account JSON key: + +```bash +cloud-sql-proxy \ + --credentials-file ./project-69718baa-b6cc-44ec-9fb-17c2c8f0dd9c.json \ + project-69718baa-b6cc-44ec-9fb:asia-south2:job-applier-mysql +``` + +*Note: If port `3306` is already in use by a local MySQL server on your machine, run the proxy on port `3307` using the `--port 3307` flag and update `MYSQL_PORT=3307` in your `.env` file.* + +#### 6. Run the Web Application + +Start the backend API server and frontend development server in separate terminals (ensure the Cloud SQL Auth Proxy is running if `MYSQL_HOST` is set to `127.0.0.1`): **Terminal 1 (Backend API):** diff --git a/backend/crew_runner.py b/backend/crew_runner.py index 1be1393..1af48ac 100644 --- a/backend/crew_runner.py +++ b/backend/crew_runner.py @@ -96,7 +96,7 @@ def _run_crew_sync( _LOG_DIR.mkdir(parents=True, exist_ok=True) ts = datetime.now().strftime("%Y%m%d_%H%M%S") - crew_log_path = str(_LOG_DIR / f"{ts}_crew.log") + crew_log_path = str(_LOG_DIR / f"{ts}_crew") crew = Crew( agents=[github_project_summarizer, profiler, resume_strategist, interview_preparer], From d27615fe29c2a3a69a8dcb3e43991ffb9072e733 Mon Sep 17 00:00:00 2001 From: Arijit De Date: Thu, 18 Jun 2026 11:13:27 +0000 Subject: [PATCH 04/11] Refactored the project to a production grade project sturcutre. Added uv as the backend package manager. Updated README.md --- backend/__init__.py | 1 - .../auth_router.py => app/api/v1/auth.py} | 0 .../github_router.py => app/api/v1/github.py} | 0 .../job_router.py => app/api/v1/jobs.py} | 0 .../api/v1/resumes.py} | 0 backend/{ => app/core}/config.py | 0 backend/{ => app/core}/database.py | 0 logger.py => backend/app/core/logging.py | 0 backend/{auth.py => app/core/security.py} | 0 backend/{ => app}/main.py | 0 backend/{models.py => app/models/__init__.py} | 0 .../{schemas.py => app/schemas/__init__.py} | 0 .../app/services/crew/agents.py | 0 .../app/services/crew/crew.py | 0 .../app/services/crew/tasks.py | 0 backend/{ => app/services}/crew_runner.py | 0 .../services/extractors/github_extractor.py | 0 .../services/extractors/linkedin_extractor.py | 0 .../{gcs.py => app/services/gcs_service.py} | 0 GCP_Setup.md => backend/docs/GCP_Setup.md | 0 webapp_plan.md => backend/docs/webapp_plan.md | 0 backend/requirements.txt | 16 ------ backend/routers/__init__.py | 1 - .../sample_data/Arijit_De_Resume.md | 0 .../sample_data/interview_materials.md | 0 example_env.txt | 49 ------------------- package-lock.json | 6 --- requirements.txt | 14 ------ 28 files changed, 87 deletions(-) delete mode 100644 backend/__init__.py rename backend/{routers/auth_router.py => app/api/v1/auth.py} (100%) rename backend/{routers/github_router.py => app/api/v1/github.py} (100%) rename backend/{routers/job_router.py => app/api/v1/jobs.py} (100%) rename backend/{routers/resume_router.py => app/api/v1/resumes.py} (100%) rename backend/{ => app/core}/config.py (100%) rename backend/{ => app/core}/database.py (100%) rename logger.py => backend/app/core/logging.py (100%) rename backend/{auth.py => app/core/security.py} (100%) rename backend/{ => app}/main.py (100%) rename backend/{models.py => app/models/__init__.py} (100%) rename backend/{schemas.py => app/schemas/__init__.py} (100%) rename agents.py => backend/app/services/crew/agents.py (100%) rename Job_Applier.py => backend/app/services/crew/crew.py (100%) rename tasks.py => backend/app/services/crew/tasks.py (100%) rename backend/{ => app/services}/crew_runner.py (100%) rename github_repo_extractor.py => backend/app/services/extractors/github_extractor.py (100%) rename webpage_extractor.py => backend/app/services/extractors/linkedin_extractor.py (100%) rename backend/{gcs.py => app/services/gcs_service.py} (100%) rename GCP_Setup.md => backend/docs/GCP_Setup.md (100%) rename webapp_plan.md => backend/docs/webapp_plan.md (100%) delete mode 100644 backend/requirements.txt delete mode 100644 backend/routers/__init__.py rename Arijit_De_Resume.md => backend/sample_data/Arijit_De_Resume.md (100%) rename interview_materials.md => backend/sample_data/interview_materials.md (100%) delete mode 100644 example_env.txt delete mode 100644 package-lock.json delete mode 100644 requirements.txt diff --git a/backend/__init__.py b/backend/__init__.py deleted file mode 100644 index 7f83169..0000000 --- a/backend/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# Backend package diff --git a/backend/routers/auth_router.py b/backend/app/api/v1/auth.py similarity index 100% rename from backend/routers/auth_router.py rename to backend/app/api/v1/auth.py diff --git a/backend/routers/github_router.py b/backend/app/api/v1/github.py similarity index 100% rename from backend/routers/github_router.py rename to backend/app/api/v1/github.py diff --git a/backend/routers/job_router.py b/backend/app/api/v1/jobs.py similarity index 100% rename from backend/routers/job_router.py rename to backend/app/api/v1/jobs.py diff --git a/backend/routers/resume_router.py b/backend/app/api/v1/resumes.py similarity index 100% rename from backend/routers/resume_router.py rename to backend/app/api/v1/resumes.py diff --git a/backend/config.py b/backend/app/core/config.py similarity index 100% rename from backend/config.py rename to backend/app/core/config.py diff --git a/backend/database.py b/backend/app/core/database.py similarity index 100% rename from backend/database.py rename to backend/app/core/database.py diff --git a/logger.py b/backend/app/core/logging.py similarity index 100% rename from logger.py rename to backend/app/core/logging.py diff --git a/backend/auth.py b/backend/app/core/security.py similarity index 100% rename from backend/auth.py rename to backend/app/core/security.py diff --git a/backend/main.py b/backend/app/main.py similarity index 100% rename from backend/main.py rename to backend/app/main.py diff --git a/backend/models.py b/backend/app/models/__init__.py similarity index 100% rename from backend/models.py rename to backend/app/models/__init__.py diff --git a/backend/schemas.py b/backend/app/schemas/__init__.py similarity index 100% rename from backend/schemas.py rename to backend/app/schemas/__init__.py diff --git a/agents.py b/backend/app/services/crew/agents.py similarity index 100% rename from agents.py rename to backend/app/services/crew/agents.py diff --git a/Job_Applier.py b/backend/app/services/crew/crew.py similarity index 100% rename from Job_Applier.py rename to backend/app/services/crew/crew.py diff --git a/tasks.py b/backend/app/services/crew/tasks.py similarity index 100% rename from tasks.py rename to backend/app/services/crew/tasks.py diff --git a/backend/crew_runner.py b/backend/app/services/crew_runner.py similarity index 100% rename from backend/crew_runner.py rename to backend/app/services/crew_runner.py diff --git a/github_repo_extractor.py b/backend/app/services/extractors/github_extractor.py similarity index 100% rename from github_repo_extractor.py rename to backend/app/services/extractors/github_extractor.py diff --git a/webpage_extractor.py b/backend/app/services/extractors/linkedin_extractor.py similarity index 100% rename from webpage_extractor.py rename to backend/app/services/extractors/linkedin_extractor.py diff --git a/backend/gcs.py b/backend/app/services/gcs_service.py similarity index 100% rename from backend/gcs.py rename to backend/app/services/gcs_service.py diff --git a/GCP_Setup.md b/backend/docs/GCP_Setup.md similarity index 100% rename from GCP_Setup.md rename to backend/docs/GCP_Setup.md diff --git a/webapp_plan.md b/backend/docs/webapp_plan.md similarity index 100% rename from webapp_plan.md rename to backend/docs/webapp_plan.md diff --git a/backend/requirements.txt b/backend/requirements.txt deleted file mode 100644 index c0f7acf..0000000 --- a/backend/requirements.txt +++ /dev/null @@ -1,16 +0,0 @@ -# Backend dependencies -fastapi>=0.115.0 -uvicorn[standard]>=0.30.0 -sqlalchemy[asyncio]>=2.0 -asyncmy>=0.2.9 -pymysql>=1.1.0 -pydantic>=2.0 -pydantic-settings>=2.0 -python-jose[cryptography]>=3.3.0 -passlib[bcrypt]>=1.7.4 -python-multipart>=0.0.7 -google-cloud-storage>=2.14.0 -google-auth>=2.25.0 -python-dotenv>=1.0.0 -pydantic[email]>=2.0 -sse-starlette>=1.8.0 diff --git a/backend/routers/__init__.py b/backend/routers/__init__.py deleted file mode 100644 index 873f7bb..0000000 --- a/backend/routers/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# Routers package diff --git a/Arijit_De_Resume.md b/backend/sample_data/Arijit_De_Resume.md similarity index 100% rename from Arijit_De_Resume.md rename to backend/sample_data/Arijit_De_Resume.md diff --git a/interview_materials.md b/backend/sample_data/interview_materials.md similarity index 100% rename from interview_materials.md rename to backend/sample_data/interview_materials.md diff --git a/example_env.txt b/example_env.txt deleted file mode 100644 index 81eb081..0000000 --- a/example_env.txt +++ /dev/null @@ -1,49 +0,0 @@ -# Path to the downloaded Service Account JSON key -GOOGLE_APPLICATION_CREDENTIALS= - -# Your GCP Project ID -GCP_PROJECT_ID= - -GCP_LOCATION= -GCP_DATASET_NAME= -GCP_TABLE_NAME= - -GITHUB_PERSONAL_ACCESS_TOKEN= - -OPENAI_API_KEY= - -SERPER_API_KEY= - -ANTHROPIC_API_KEY= - -GEMINI_API_KEY= - -BRIGHT_DATA_API_KEY= - -# Logging Configuration -# Console log level: DEBUG | INFO | WARNING | ERROR | CRITICAL (default: INFO) -LOG_LEVEL=INFO - -# ── Web App Configuration ────────────────────────────────────────── -# Cloud SQL MySQL -MYSQL_HOST=127.0.0.1 -MYSQL_PORT=3306 -MYSQL_USER= -MYSQL_PASSWORD= -MYSQL_DATABASE=job_applier - -# GCS Bucket for resumes -GCS_BUCKET_NAME= - -# JWT Authentication -JWT_SECRET_KEY= -JWT_ALGORITHM=HS256 -JWT_EXPIRY_MINUTES=1440 - -# Google OAuth (optional — set up in GCP Console) -GOOGLE_OAUTH_CLIENT_ID= -GOOGLE_OAUTH_CLIENT_SECRET= - -# Crew AI -CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true -CREWAI_TRACING_ENABLED=true \ No newline at end of file diff --git a/package-lock.json b/package-lock.json deleted file mode 100644 index 8591261..0000000 --- a/package-lock.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "name": "Job_Application_Agent", - "lockfileVersion": 3, - "requires": true, - "packages": {} -} diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 1573186..0000000 --- a/requirements.txt +++ /dev/null @@ -1,14 +0,0 @@ -crewai[anthropic] -crewai-tools -beautifulsoup4 -google-cloud-bigquery -langchain-community -langchain-google-genai -langchain-google-community[featurestore] -langchain-text-splitters -openpyxl -pandas -pypdf -python-dotenv -requests -python-docx \ No newline at end of file From 40de1a30969e693797a11d060513bf672bfedd1e Mon Sep 17 00:00:00 2001 From: Arijit De Date: Thu, 18 Jun 2026 11:16:17 +0000 Subject: [PATCH 05/11] refactor: Refactored the project to a production grade project sturcutre. Added uv as the backend package manager. Updated README.md --- .github/workflows/deploy.yml | 0 .gitignore | 22 +- README.md | 295 +- backend/.env.example | 53 + backend/Dockerfile | 0 backend/README.md | 104 + backend/app/__init__.py | 0 backend/app/api/__init__.py | 0 backend/app/api/deps.py | 15 + backend/app/api/v1/__init__.py | 0 backend/app/api/v1/auth.py | 8 +- backend/app/api/v1/github.py | 8 +- backend/app/api/v1/jobs.py | 12 +- backend/app/api/v1/resumes.py | 12 +- backend/app/cli.py | 251 + backend/app/core/__init__.py | 0 backend/app/core/config.py | 11 + backend/app/core/database.py | 2 +- backend/app/core/logging.py | 9 +- backend/app/core/security.py | 6 +- backend/app/main.py | 34 +- backend/app/models/__init__.py | 2 +- backend/app/services/__init__.py | 0 backend/app/services/crew/__init__.py | 0 backend/app/services/crew/__main__.py | 68 + backend/app/services/crew/agents.py | 24 +- backend/app/services/crew/crew.py | 112 +- backend/app/services/crew/tasks.py | 2 +- backend/app/services/crew_runner.py | 59 +- backend/app/services/extractors/__init__.py | 0 .../services/extractors/github_extractor.py | 26 +- .../services/extractors/linkedin_extractor.py | 14 +- backend/app/services/gcs_service.py | 2 +- backend/pyproject.toml | 76 + backend/tests/__init__.py | 0 backend/tests/test_health.py | 13 + backend/uv.lock | 5428 +++++++++++++++++ docker-compose.yml | 0 frontend/.env.example | 11 + frontend/Dockerfile | 0 frontend/README.md | 49 +- frontend/src/components/GithubProfileAdd.jsx | 2 +- frontend/src/components/LoginSection.jsx | 2 +- .../src/components/ResumePreviewModal.jsx | 2 +- frontend/src/components/ResumeReadyModal.jsx | 2 +- frontend/src/components/ResumeUpload.jsx | 2 +- frontend/src/components/TailorResumeForm.jsx | 2 +- .../src/components/TailoredResumeList.jsx | 2 +- frontend/src/context/AuthContext.jsx | 2 +- frontend/src/hooks/.gitkeep | 0 frontend/src/pages/HomePage.jsx | 2 +- frontend/src/pages/InterviewMaterialsPage.jsx | 2 +- frontend/src/pages/RegisterPage.jsx | 2 +- .../src/{api/client.js => services/api.js} | 0 54 files changed, 6405 insertions(+), 345 deletions(-) create mode 100644 .github/workflows/deploy.yml create mode 100644 backend/.env.example create mode 100644 backend/Dockerfile create mode 100644 backend/README.md create mode 100644 backend/app/__init__.py create mode 100644 backend/app/api/__init__.py create mode 100644 backend/app/api/deps.py create mode 100644 backend/app/api/v1/__init__.py create mode 100644 backend/app/cli.py create mode 100644 backend/app/core/__init__.py create mode 100644 backend/app/services/__init__.py create mode 100644 backend/app/services/crew/__init__.py create mode 100644 backend/app/services/crew/__main__.py create mode 100644 backend/app/services/extractors/__init__.py create mode 100644 backend/pyproject.toml create mode 100644 backend/tests/__init__.py create mode 100644 backend/tests/test_health.py create mode 100644 backend/uv.lock create mode 100644 docker-compose.yml create mode 100644 frontend/.env.example create mode 100644 frontend/Dockerfile create mode 100644 frontend/src/hooks/.gitkeep rename frontend/src/{api/client.js => services/api.js} (100%) diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml new file mode 100644 index 0000000..e69de29 diff --git a/.gitignore b/.gitignore index 931bff6..1701bca 100644 --- a/.gitignore +++ b/.gitignore @@ -123,6 +123,11 @@ celerybeat.pid # Environments .env +.env.* +!.env.example +**/.env +**/.env.* +!**/.env.example .venv env/ venv/ @@ -130,6 +135,11 @@ ENV/ env.bak/ venv.bak/ +# Node +node_modules/ +**/node_modules/ +frontend/dist/ + # Spyder project settings .spyderproject .spyproject @@ -163,6 +173,12 @@ cython_debug/ db/ crew_log.txt -project-69718baa-b6cc-44ec-9fb-17c2c8f0dd9c.json -client_secret_720140080607-c0cuhvl4bvqtnk6tgicqds8136iqgrdv.apps.googleusercontent.com.json -logs/ \ No newline at end of file +logs/ +**/logs/ + +# Credentials / service-account keys (never commit) +**/credentials/ +client_secret_*.json +*-service-account*.json +project-*.json +backend/credentials/ \ No newline at end of file diff --git a/README.md b/README.md index 3857cbe..22e270d 100644 --- a/README.md +++ b/README.md @@ -6,6 +6,101 @@ This project also includes a **Full-Stack Web App version** with a **FastAPI bac --- +## 📦 Project Structure + +``` +Job_Application_Agent/ +├── backend/ # FastAPI backend + CrewAI pipeline (uv-managed) +│ ├── app/ +│ │ ├── api/ # Route handlers (api/v1) + shared deps +│ │ ├── core/ # config, security, database, logging +│ │ ├── services/ # GCS, crew runner, CrewAI pipeline, extractors +│ │ ├── models/ # SQLAlchemy ORM models +│ │ ├── schemas/ # Pydantic schemas +│ │ └── main.py # App init + CORS + routers +│ ├── credentials/ # GCP keys (gitignored) +│ ├── pyproject.toml # Dependencies (uv) +│ ├── Dockerfile # (empty for now) +│ └── README.md # Backend setup & run instructions +├── frontend/ # React + Vite (JavaScript) frontend +│ ├── src/ +│ │ ├── components/ pages/ context/ hooks/ services/ assets/ +│ │ └── main.jsx App.jsx +│ ├── Dockerfile # (empty for now) +│ └── package.json +├── .github/workflows/ # CI/CD (deploy.yml — empty for now) +├── docker-compose.yml # (empty for now) +└── README.md # This file +``` + +## ✅ Prerequisites + +| Requirement | Version / Notes | +| ----------- | --------------- | +| **Python** | 3.11+ (3.12 recommended). You don't need to install it manually — `uv` will fetch a matching interpreter. | +| **[uv](https://docs.astral.sh/uv/)** | The Python package & project manager used here. Install: `curl -LsSf https://astral.sh/uv/install.sh \| sh` (then restart your shell so `~/.local/bin` is on `PATH`). | +| **Node.js** | 18+ (with `npm`), for the React frontend. [Download](https://nodejs.org/en/download). | +| **Google Cloud account** | A GCP project with **BigQuery**, **Vertex AI**, **Cloud SQL Admin**, and **Cloud Storage** APIs enabled, plus a **service-account JSON key**. See [backend/docs/GCP_Setup.md](backend/docs/GCP_Setup.md). | +| **Cloud SQL (MySQL)** | A Cloud SQL MySQL instance (the web app stores users/jobs there). | +| **[Cloud SQL Auth Proxy](https://cloud.google.com/sql/docs/mysql/sql-proxy)** | `cloud-sql-proxy` binary on `PATH` (the `uv run db-proxy` / `uv run dev` commands invoke it). See [Installation](https://docs.cloud.google.com/sql/docs/mysql/connect-instance-auth-proxy) to install| +| **API keys** | GitHub PAT, Bright Data, Gemini, and (optionally) Serper. See [API Keys & Cloud Setup](#-api-keys--cloud-setup). | + +> **All `uv run` commands are run from the `backend/` directory** — the project +> and its custom commands are defined in `backend/pyproject.toml`. There is no +> root-level `pyproject.toml`, so `uv run dev` from the repo root will not work. + +--- + +## ⚡ Quick Start + +```bash +git clone https://github.com/arijitde92/Job_Application_Agent.git +cd Job_Application_Agent +``` + +### 1. Backend (uv) + +```bash +cd backend +cp .env.example .env # then fill in secrets (see below) +uv sync # creates backend/.venv and installs everything +``` + +Place your GCP service-account JSON key under `backend/credentials/` and point +`GOOGLE_APPLICATION_CREDENTIALS` in `backend/.env` at it (an absolute path is +safest). + +### 2. Frontend + +```bash +cd ../frontend +cp .env.example .env +npm install +``` + +### 3. Run everything (from `backend/`) + +```bash +cd ../backend +uv run dev # Cloud SQL proxy + API + frontend, together +``` + +Then open **http://localhost:5173**. The API is at http://localhost:8000 +(docs at http://localhost:8000/docs). + +Prefer separate terminals? Run them individually: + +```bash +uv run db-proxy # Cloud SQL Auth Proxy → 127.0.0.1:3306 +uv run api # FastAPI dev server → http://localhost:8000 +uv run frontend # Vite dev server → http://localhost:5173 +``` + +See **[backend/README.md](backend/README.md)** for the full command list, +env-var overrides, and the standalone crew CLI. + +--- + ## 🚀 Features - **Multi-Agent Orchestration:** Uses Crew AI to coordinate agents for research, profiling, resume tailoring, and interview preparation. @@ -22,7 +117,6 @@ This project also includes a **Full-Stack Web App version** with a **FastAPI bac - **Crew AI** ([Homepage](https://www.crewai.com/)): The backbone of the system, enabling modular, collaborative agent workflows. - **Agents:** - - **Researcher:** Extracts and analyzes job requirements from LinkedIn. - **GitHub Project Summarizer:** Summarizes your most relevant GitHub projects. - **Profiler:** Compiles a comprehensive personal/professional profile. - **Resume Strategist:** Tailors your resume for each job. @@ -45,184 +139,101 @@ This project also includes a **Full-Stack Web App version** with a **FastAPI bac ## 🏗️ Architecture -- **Python** (see `requirements.txt` for dependencies) +- **Python 3.11+** with **[uv](https://docs.astral.sh/uv/)** for dependency & + environment management (see `backend/pyproject.toml`) +- **FastAPI** backend (async SQLAlchemy + Cloud SQL MySQL, JWT/Google OAuth auth) +- **React + Vite** frontend with live progress via Server-Sent Events (SSE) - **Crew AI** for agent orchestration - **Google BigQuery** as a vector store for document embeddings - **Vertex AI** for generating and querying semantic embeddings -- **BeautifulSoup, Requests** for web scraping -- **LangChain** for document loading and chunking - ---- - -## ⚙️ Setup Instructions - -### 1. Clone the Repository - -This is always the first step. Run: - -```bash -git clone https://github.com/arijitde92/Job_Application_Agent.git -cd Job_Application_Agent -``` +- **Google Cloud Storage** for resume & generated-output storage +- **Bright Data MCP** for LinkedIn job scraping; **LangChain** for document + loading and chunking --- -### Option A: Running the Standalone CLI Agent +## ⚙️ Configuration -#### 1. Install Dependencies - -```bash -pip install -r requirements.txt -``` - -#### 2. Configure Environment Variables - -Create a `.env` file in the project root with the following variables: +All backend configuration lives in **`backend/.env`** (copy it from +`backend/.env.example`). Key groups: ```env -# GitHub API token -GITHUB_PERSONAL_ACCESS_TOKEN=your_github_token - -# Google Cloud Project details -GOOGLE_APPLICATION_CREDENTIALS=/path/to/your/gcp-service-account.json -GCP_PROJECT_ID=inbound-byway-457408-c9 -GCP_DATASET_NAME=job_applier_app +# ── GCP ────────────────────────────────────────────────────────── +GOOGLE_APPLICATION_CREDENTIALS=/abs/path/to/backend/credentials/service-account.json +GCP_PROJECT_ID=your-project-id GCP_LOCATION=asia-south2 +GCP_DATASET_NAME=job_applier_app GCP_TABLE_NAME=github_repo_data -# (Optional) Vertex AI Model -VERTEX_AI_MODEL=text-embedding-005 -``` - -#### 3. Run the CLI Application - -```bash -python Job_Applier.py -``` - ---- - -### Option B: Running the Full-Stack Web App - -#### 1. Setup Python Environment & Dependencies - -Create and activate a conda environment, then install the backend dependencies: - -```bash -conda create -n job_agent python=3.12 -y -conda activate job_agent -pip install -r requirements.txt -pip install -r backend/requirements.txt -``` - -#### 2. Install Node.js +# ── API keys ───────────────────────────────────────────────────── +GITHUB_PERSONAL_ACCESS_TOKEN=... +BRIGHT_DATA_API_KEY=... # LinkedIn scraping (Bright Data MCP) +GEMINI_API_KEY=... # Crew LLM + Vertex embeddings +SERPER_API_KEY=... # optional web search -The frontend React application requires Node.js (v18+). If you do not have it installed, please download and install it from the official [Node.js Download Page](https://nodejs.org/en/download). - -#### 3. Setup Frontend Dependencies - -```bash -cd frontend -npm install -cd .. -``` - -#### 4. Configure Web App Environment Variables - -Add the following database and storage configurations to the bottom of your `.env` file: - -```env -# Cloud SQL MySQL Connection -# Use 127.0.0.1 when running Cloud SQL Auth Proxy (Recommended) -# Use 34.131.150.209 for direct connection (requires authorizing your public IP in GCP console) -MYSQL_HOST=127.0.0.1 +# ── Cloud SQL (MySQL) ──────────────────────────────────────────── +MYSQL_HOST=127.0.0.1 # 127.0.0.1 when using the Cloud SQL Auth Proxy MYSQL_PORT=3306 -MYSQL_USER=root -MYSQL_PASSWORD=JobAgent@2026 +MYSQL_USER=... +MYSQL_PASSWORD=... MYSQL_DATABASE=job_applier -# Google Cloud Storage (GCS) for Resumes -GCS_BUCKET_NAME=job-applier-project-69718baa-b6cc-44ec-9fb-resumes - -# JWT Authentication -# Generate using: python -c "import secrets; print(secrets.token_urlsafe(32))" -JWT_SECRET_KEY=drUVPzflrCADfaC7HjrydJYPPS_IpspPlXxjOELdfjI -JWT_ALGORITHM=HS256 -JWT_EXPIRY_MINUTES=1440 - -# Google OAuth (Optional — for Google Login) -GOOGLE_OAUTH_CLIENT_ID=your-client-id.apps.googleusercontent.com -GOOGLE_OAUTH_CLIENT_SECRET=your-client-secret +# ── GCS / Auth ─────────────────────────────────────────────────── +GCS_BUCKET_NAME=your-resumes-bucket +JWT_SECRET_KEY= # generate: python -c "import secrets; print(secrets.token_urlsafe(32))" +GOOGLE_OAUTH_CLIENT_ID= # optional, for "Sign in with Google" +GOOGLE_OAUTH_CLIENT_SECRET= ``` -#### 5. Setup & Run Cloud SQL Auth Proxy (Recommended) - -Since dynamic public IP addresses change frequently, running the Cloud SQL Auth Proxy is the recommended way to securely connect to the Cloud SQL database from your local development environment without having to configure authorized IP networks in the GCP Console. +The frontend has its own `frontend/.env` (copy from `frontend/.env.example`) for +`VITE_*` variables. -##### A. Install the Proxy Client (Linux) -```bash -# Download the binary (for 64-bit Linux) -curl -o cloud-sql-proxy https://storage.googleapis.com/cloud-sql-connectors/cloud-sql-proxy/v2.11.0/cloud-sql-proxy.linux.amd64 - -# Make it executable -chmod +x cloud-sql-proxy +> **Never commit `.env` or service-account keys.** They are gitignored. The +> `db-proxy` connection name is derived from `GCP_PROJECT_ID`, `GCP_LOCATION`, +> and instance `job-applier-mysql` by default — override with `CLOUD_SQL_INSTANCE` +> if yours differs. -# Move it to a directory in your PATH (optional, but recommended) -sudo mv cloud-sql-proxy /usr/local/bin/ -``` +For step-by-step GCP provisioning (project, BigQuery dataset, Cloud SQL instance, +GCS bucket, service account), see **[backend/docs/GCP_Setup.md](backend/docs/GCP_Setup.md)**. -##### B. Run the Proxy -Start the proxy in a separate terminal using your GCP Service Account JSON key: - -```bash -cloud-sql-proxy \ - --credentials-file ./project-69718baa-b6cc-44ec-9fb-17c2c8f0dd9c.json \ - project-69718baa-b6cc-44ec-9fb:asia-south2:job-applier-mysql -``` - -*Note: If port `3306` is already in use by a local MySQL server on your machine, run the proxy on port `3307` using the `--port 3307` flag and update `MYSQL_PORT=3307` in your `.env` file.* - -#### 6. Run the Web Application - -Start the backend API server and frontend development server in separate terminals (ensure the Cloud SQL Auth Proxy is running if `MYSQL_HOST` is set to `127.0.0.1`): +--- -**Terminal 1 (Backend API):** +## 🖥️ Standalone Crew CLI (no web app) -```bash -conda activate job_agent -uvicorn backend.main:app --host 127.0.0.1 --port 8000 --reload -``` - -**Terminal 2 (Frontend React):** +You can run the agent pipeline directly, without the frontend/DB, from `backend/`: ```bash -cd frontend -npm run dev +cd backend +uv run crew \ + --url https://www.linkedin.com/jobs/view// \ + --github https://github.com/ \ + --resume sample_data/Arijit_De_Resume.md \ + --name "Your Name" ``` -Open `http://localhost:5173` in your browser. +This extracts the job, indexes the GitHub repos into BigQuery, tailors the +resume, and writes interview materials — same pipeline the web app drives. --- ## 🔑 API Keys & Cloud Setup -- **GitHub:** [Create a Personal Access Token](https://github.com/settings/tokens) and set `GITHUB_PERSONAL_ACCESS_TOKEN` in your `.env` file. -- **Google Cloud:** - - Enable BigQuery and Vertex AI APIs. - - Create a service account with the necessary permissions. - - Download the JSON key and set `GOOGLE_APPLICATION_CREDENTIALS` in your `.env` file. -- **OpenAI:** - - [Get an OpenAI API Key](https://platform.openai.com/account/api-keys) - - Add `OPENAI_API_KEY=your_openai_api_key` to your `.env` file. -- **Serper:** - - [Get a Serper API Key](https://serper.dev/) - - Add `SERPER_API_KEY=your_serper_api_key` to your `.env` file. +All keys go in `backend/.env`. + +- **GitHub:** [Create a Personal Access Token](https://github.com/settings/tokens) → `GITHUB_PERSONAL_ACCESS_TOKEN`. +- **Google Cloud:** + - Enable **BigQuery**, **Vertex AI**, **Cloud SQL Admin**, and **Cloud Storage** APIs. + - Create a service account with the relevant roles (BigQuery, Vertex AI User, Cloud SQL Client, Storage Object Admin). + - Download the JSON key into `backend/credentials/` and point `GOOGLE_APPLICATION_CREDENTIALS` at it. +- **Gemini:** [Get a Gemini API key](https://aistudio.google.com/app/apikey) → `GEMINI_API_KEY` (the crew's LLM + embeddings). +- **Bright Data:** [Get an API key](https://brightdata.com/) for the MCP scraper → `BRIGHT_DATA_API_KEY` (LinkedIn job extraction). +- **Serper (optional):** [Get a Serper API key](https://serper.dev/) → `SERPER_API_KEY` (web search tool). --- ## 📚 How it Works -1. **Job Research:** The Researcher agent scrapes LinkedIn for job details. +1. **Job Extraction:** The job posting is scraped once via the Bright Data MCP LinkedIn extractor and parsed into structured `JobDetails`, which are injected into every downstream task. 2. **GitHub Analysis:** The GitHub Project Summarizer indexes and summarizes your public repos, storing embeddings in BigQuery using Vertex AI. 3. **Profile Compilation:** The Profiler agent creates a comprehensive profile using your resume, GitHub summaries, and job requirements. 4. **Resume Tailoring:** The Resume Strategist aligns your resume with the job description. diff --git a/backend/.env.example b/backend/.env.example new file mode 100644 index 0000000..e4a875a --- /dev/null +++ b/backend/.env.example @@ -0,0 +1,53 @@ +# ────────────────────────────────────────────────────────────────────────── +# Backend environment — copy to `backend/.env` and fill in real values. +# The app reads this file (BaseSettings, model_config env_file=".env") when +# started from the `backend/` directory. +# ────────────────────────────────────────────────────────────────────────── + +# ── GCP ──────────────────────────────────────────────────────────────────── +# Absolute path to the downloaded service-account JSON key. +GOOGLE_APPLICATION_CREDENTIALS=./credentials/service-account.json +GCP_PROJECT_ID= +GCP_LOCATION=asia-south2 +GCP_DATASET_NAME=job_applier_app +GCP_TABLE_NAME=github_repo_data + +# ── API keys ─────────────────────────────────────────────────────────────── +GITHUB_PERSONAL_ACCESS_TOKEN= +SERPER_API_KEY= +ANTHROPIC_API_KEY= +GEMINI_API_KEY= +BRIGHT_DATA_API_KEY= + +# ── Logging ──────────────────────────────────────────────────────────────── +# Console log level: DEBUG | INFO | WARNING | ERROR | CRITICAL +LOG_LEVEL=INFO + +# ── Cloud SQL (MySQL) ────────────────────────────────────────────────────── +MYSQL_HOST=127.0.0.1 +MYSQL_PORT=3306 +MYSQL_USER= +MYSQL_PASSWORD= +MYSQL_DATABASE=job_applier + +# ── GCS bucket for resumes ───────────────────────────────────────────────── +GCS_BUCKET_NAME= + +# ── JWT authentication ───────────────────────────────────────────────────── +JWT_SECRET_KEY=CHANGE_ME_IN_PRODUCTION +JWT_ALGORITHM=HS256 +JWT_EXPIRY_MINUTES=1440 + +# ── Google OAuth (optional) ──────────────────────────────────────────────── +GOOGLE_OAUTH_CLIENT_ID= +GOOGLE_OAUTH_CLIENT_SECRET= + +# ── CrewAI ───────────────────────────────────────────────────────────────── +CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true +CREWAI_TRACING_ENABLED=true + +# ── CORS (comma-separated frontend origins) ──────────────────────────────── +CORS_ORIGINS=http://localhost:5173,http://localhost:3000,http://127.0.0.1:5173 + +# ── Resume upload limit ──────────────────────────────────────────────────── +MAX_RESUME_SIZE_MB=10 diff --git a/backend/Dockerfile b/backend/Dockerfile new file mode 100644 index 0000000..e69de29 diff --git a/backend/README.md b/backend/README.md new file mode 100644 index 0000000..5f372b7 --- /dev/null +++ b/backend/README.md @@ -0,0 +1,104 @@ +# Job Application Agent — Backend + +FastAPI backend plus the CrewAI multi-agent pipeline that tailors resumes and +generates interview materials from a LinkedIn job posting and a GitHub profile. + +## Layout + +``` +backend/ +├── app/ +│ ├── api/ +│ │ ├── v1/ # Versioned route handlers +│ │ │ ├── auth.py # /api/auth — register, login, Google OAuth +│ │ │ ├── github.py # /api/github — GitHub profile CRUD +│ │ │ ├── resumes.py # /api/resumes — upload / preview / delete +│ │ │ └── jobs.py # /api/jobs — tailor, status (SSE), downloads +│ │ └── deps.py # Shared dependencies (get_db, get_current_user) +│ ├── core/ +│ │ ├── config.py # Pydantic BaseSettings (.env) +│ │ ├── security.py # JWT, password hashing, Google OAuth verify +│ │ ├── database.py # Async SQLAlchemy engine / session / Base +│ │ └── logging.py # Centralised logging +│ ├── services/ +│ │ ├── gcs_service.py # Google Cloud Storage helpers +│ │ ├── crew_runner.py # Async wrapper that drives the crew per job +│ │ ├── crew/ # CrewAI pipeline +│ │ │ ├── agents.py # Agent definitions + tools +│ │ │ ├── tasks.py # Task definitions +│ │ │ ├── crew.py # build_crew() factory +│ │ │ └── __main__.py # CLI: python -m app.services.crew +│ │ └── extractors/ +│ │ ├── linkedin_extractor.py # Bright Data MCP → JobDetails +│ │ └── github_extractor.py # GitHub → BigQuery vector store (RAG) +│ ├── models/ # SQLAlchemy ORM models +│ ├── schemas/ # Pydantic request/response schemas +│ └── main.py # FastAPI app init, CORS, router registration +├── credentials/ # GCP service-account / OAuth JSON (gitignored) +├── sample_data/ # Example resume & interview output +├── tests/ # Pytest suite +├── pyproject.toml # Dependencies (managed by uv) +├── Dockerfile # (intentionally empty for now) +└── .env.example # Copy to .env and fill in +``` + +## Setup (uv) + +This project uses [uv](https://docs.astral.sh/uv/). + +```bash +cd backend +cp .env.example .env # fill in secrets +uv sync # create venv + install from pyproject.toml +``` + +## Custom commands + +Defined as `[project.scripts]` in `pyproject.toml` — run from `backend/`: + +| Command | What it does | +| ------------------ | -------------------------------------------------------- | +| `uv run api` | FastAPI dev server with autoreload (127.0.0.1:8000) | +| `uv run api-prod` | FastAPI production server (no reload, `API_WORKERS` proc) | +| `uv run frontend` | Vite dev server via `npm run dev` (../frontend) | +| `uv run dev` | Runs **db-proxy + api + frontend** together; Ctrl-C stops all | +| `uv run db-proxy` | Cloud SQL Auth Proxy → `127.0.0.1:3306` (standalone) | +| `uv run crew ...` | The CrewAI pipeline CLI (see below) | + +Override host/port/workers with env vars: `API_HOST`, `API_PORT`, `API_WORKERS`. + +`db-proxy` derives the instance connection name from `.env` +(`::job-applier-mysql`) and binds the port from +`MYSQL_PORT`. Override with `CLOUD_SQL_INSTANCE` (full `project:region:instance` +name), `CLOUD_SQL_INSTANCE_NAME`, or `CLOUD_SQL_PROXY_BIN`. It authenticates with +`GOOGLE_APPLICATION_CREDENTIALS`, so that must point at a valid service-account +key with the **Cloud SQL Client** role. + +```bash +cd backend +uv run api # → http://localhost:8000/docs +uv run dev # db-proxy + api + frontend (needs `npm install` in ../frontend first) +``` + +`dev` starts the Cloud SQL proxy first and waits until `127.0.0.1:` +accepts connections before launching the API, so the app's startup DB connection +succeeds. Skip the proxy with `DEV_SKIP_PROXY=1` (e.g. when `MYSQL_HOST` is a +direct public IP). Ctrl-C stops all three processes (and their children). + +## Run the crew pipeline standalone (CLI) + +```bash +cd backend +uv run crew \ + --url https://www.linkedin.com/jobs/view// \ + --github https://github.com/ \ + --resume sample_data/Arijit_De_Resume.md \ + --name "Your Name" +``` + +## Tests + +```bash +cd backend +uv run pytest +``` diff --git a/backend/app/__init__.py b/backend/app/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/app/api/__init__.py b/backend/app/api/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/app/api/deps.py b/backend/app/api/deps.py new file mode 100644 index 0000000..293502f --- /dev/null +++ b/backend/app/api/deps.py @@ -0,0 +1,15 @@ +""" +app.api.deps +------------ +Common API dependencies shared across endpoints (database session, auth). + +Import these in routers rather than reaching into ``app.core`` directly, so the +dependency surface stays in one place: + + from app.api.deps import get_db, get_current_user +""" + +from app.core.database import get_db +from app.core.security import get_current_user + +__all__ = ["get_db", "get_current_user"] diff --git a/backend/app/api/v1/__init__.py b/backend/app/api/v1/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/app/api/v1/auth.py b/backend/app/api/v1/auth.py index 9e3afe9..374e925 100644 --- a/backend/app/api/v1/auth.py +++ b/backend/app/api/v1/auth.py @@ -8,13 +8,13 @@ from sqlalchemy import select from sqlalchemy.ext.asyncio import AsyncSession -from backend.database import get_db -from backend.models import User -from backend.schemas import ( +from app.api.deps import get_db +from app.models import User +from app.schemas import ( RegisterRequest, LoginRequest, GoogleOAuthRequest, TokenResponse, UserResponse, ) -from backend.auth import ( +from app.core.security import ( hash_password, verify_password, create_access_token, get_current_user, verify_google_id_token, ) diff --git a/backend/app/api/v1/github.py b/backend/app/api/v1/github.py index 2d8e1c5..6169aa0 100644 --- a/backend/app/api/v1/github.py +++ b/backend/app/api/v1/github.py @@ -9,10 +9,10 @@ from sqlalchemy import select from sqlalchemy.ext.asyncio import AsyncSession -from backend.database import get_db -from backend.models import User, GithubProfile -from backend.schemas import GithubProfileCreate, GithubProfileResponse -from backend.auth import get_current_user +from app.api.deps import get_db +from app.models import User, GithubProfile +from app.schemas import GithubProfileCreate, GithubProfileResponse +from app.api.deps import get_current_user router = APIRouter(prefix="/api/github", tags=["github"]) diff --git a/backend/app/api/v1/jobs.py b/backend/app/api/v1/jobs.py index f326df2..49adece 100644 --- a/backend/app/api/v1/jobs.py +++ b/backend/app/api/v1/jobs.py @@ -11,12 +11,12 @@ from sqlalchemy import select from sqlalchemy.ext.asyncio import AsyncSession -from backend.database import get_db -from backend.models import User, Job, GithubProfile, Resume -from backend.schemas import TailorJobRequest, JobResponse, JobDetailResponse -from backend.auth import get_current_user -from backend.gcs import download_file -from backend.crew_runner import run_crew_for_job, progress_store +from app.api.deps import get_db +from app.models import User, Job, GithubProfile, Resume +from app.schemas import TailorJobRequest, JobResponse, JobDetailResponse +from app.api.deps import get_current_user +from app.services.gcs_service import download_file +from app.services.crew_runner import run_crew_for_job, progress_store router = APIRouter(prefix="/api/jobs", tags=["jobs"]) diff --git a/backend/app/api/v1/resumes.py b/backend/app/api/v1/resumes.py index 45c5556..c6a0616 100644 --- a/backend/app/api/v1/resumes.py +++ b/backend/app/api/v1/resumes.py @@ -8,12 +8,12 @@ from sqlalchemy import select from sqlalchemy.ext.asyncio import AsyncSession -from backend.database import get_db -from backend.models import User, Resume -from backend.schemas import ResumeResponse -from backend.auth import get_current_user -from backend.gcs import upload_resume, generate_signed_url, download_file, delete_file -from backend.config import get_settings +from app.api.deps import get_db +from app.models import User, Resume +from app.schemas import ResumeResponse +from app.api.deps import get_current_user +from app.services.gcs_service import upload_resume, generate_signed_url, download_file, delete_file +from app.core.config import get_settings settings = get_settings() router = APIRouter(prefix="/api/resumes", tags=["resumes"]) diff --git a/backend/app/cli.py b/backend/app/cli.py new file mode 100644 index 0000000..3786d0e --- /dev/null +++ b/backend/app/cli.py @@ -0,0 +1,251 @@ +""" +app.cli +------- +Console entrypoints exposed via ``[project.scripts]`` in pyproject.toml. + +Run from the ``backend/`` directory: + + uv run api # start the FastAPI server (reload) + uv run api-prod # start the FastAPI server (no reload, multiple workers) + uv run frontend # start the Vite dev server (npm) + uv run dev # run db-proxy + api + frontend together + uv run db-proxy # start the Cloud SQL Auth Proxy (127.0.0.1:3306) + uv run crew --url ... # run the CrewAI pipeline CLI + +These are thin wrappers around uvicorn / npm / cloud-sql-proxy so the long +invocations live in one place. +""" + +import os +import subprocess +import sys +from pathlib import Path + +# backend/app/cli.py → parents[1] == backend/, parents[2] == repo root +BACKEND_DIR = Path(__file__).resolve().parents[1] +REPO_ROOT = Path(__file__).resolve().parents[2] +FRONTEND_DIR = REPO_ROOT / "frontend" + +DEFAULT_API_HOST = os.environ.get("API_HOST", "127.0.0.1") +DEFAULT_API_PORT = os.environ.get("API_PORT", "8000") + + +def crew() -> None: + """Run the CrewAI pipeline CLI (delegates to app.services.crew.__main__).""" + from app.services.crew.__main__ import main + + main() + + +def _cloud_sql_proxy_cmd() -> tuple[list[str], str, str]: + """ + Build the Cloud SQL Auth Proxy command from settings/env. + + The instance connection name is ``::``. It is + taken from CLOUD_SQL_INSTANCE if set, otherwise built from GCP_PROJECT_ID, + GCP_LOCATION and CLOUD_SQL_INSTANCE_NAME (default: job-applier-mysql). + + Tunables (env vars): + CLOUD_SQL_INSTANCE full connection name (overrides the parts below) + CLOUD_SQL_INSTANCE_NAME instance id (default: job-applier-mysql) + CLOUD_SQL_PROXY_BIN proxy binary (default: cloud-sql-proxy) + MYSQL_PORT local port to bind (default: 3306) + + Returns: + (cmd, instance_connection_name, port) + + Raises: + SystemExit: if the instance connection name cannot be determined. + """ + from app.core.config import get_settings + + settings = get_settings() + + instance = os.environ.get("CLOUD_SQL_INSTANCE") + if not instance: + project = settings.GCP_PROJECT_ID + region = settings.GCP_LOCATION + name = os.environ.get("CLOUD_SQL_INSTANCE_NAME", "job-applier-mysql") + if not project or not region: + print( + "Cannot build the Cloud SQL instance connection name: set " + "GCP_PROJECT_ID and GCP_LOCATION in .env, or set CLOUD_SQL_INSTANCE " + "to '::'.", + file=sys.stderr, + ) + sys.exit(1) + instance = f"{project}:{region}:{name}" + + proxy_bin = os.environ.get("CLOUD_SQL_PROXY_BIN", "cloud-sql-proxy") + port = str(settings.MYSQL_PORT) + cmd = [proxy_bin, "--address=127.0.0.1", f"--port={port}", instance] + return cmd, instance, port + + +def db_proxy() -> None: + """ + Start the Cloud SQL Auth Proxy so the app can reach the MySQL instance on + 127.0.0.1:3306 (matching MYSQL_HOST/MYSQL_PORT in .env). + """ + cmd, instance, port = _cloud_sql_proxy_cmd() + print(f"Starting Cloud SQL Auth Proxy → 127.0.0.1:{port} for {instance}") + try: + sys.exit(subprocess.call(cmd)) + except FileNotFoundError: + print( + f"'{cmd[0]}' not found. Install the Cloud SQL Auth Proxy or set " + "CLOUD_SQL_PROXY_BIN to its path.", + file=sys.stderr, + ) + sys.exit(1) + except KeyboardInterrupt: + pass + + +def api() -> None: + """Start the FastAPI app with autoreload (development).""" + import uvicorn + + uvicorn.run( + "app.main:app", + host=DEFAULT_API_HOST, + port=int(DEFAULT_API_PORT), + reload=True, + ) + + +def api_prod() -> None: + """Start the FastAPI app without reload (production-style).""" + import uvicorn + + workers = int(os.environ.get("API_WORKERS", "4")) + uvicorn.run( + "app.main:app", + host=os.environ.get("API_HOST", "0.0.0.0"), + port=int(DEFAULT_API_PORT), + workers=workers, + ) + + +def frontend() -> None: + """Start the Vite dev server (npm run dev).""" + if not (FRONTEND_DIR / "node_modules").exists(): + print("frontend/node_modules missing — run `npm install` in frontend/ first.", file=sys.stderr) + sys.exit(1) + sys.exit(subprocess.call(["npm", "run", "dev"], cwd=FRONTEND_DIR)) + + +def _wait_for_port(host: str, port: int, timeout: float = 30.0) -> bool: + """Block until ``host:port`` accepts a TCP connection, or timeout elapses.""" + import socket + import time + + deadline = time.monotonic() + timeout + while time.monotonic() < deadline: + try: + with socket.create_connection((host, port), timeout=1): + return True + except OSError: + time.sleep(0.5) + return False + + +def _spawn(cmd: list[str], cwd: Path) -> subprocess.Popen: + """ + Start a child process in its own session/process group so that on teardown + we can signal the whole group — npm spawns Vite, and uvicorn --reload spawns + a worker, both of which would otherwise orphan and keep their ports bound. + """ + return subprocess.Popen(cmd, cwd=cwd, start_new_session=True) + + +def _terminate(procs: list[subprocess.Popen]) -> None: + """Stop each process and its whole group; escalate to SIGKILL if needed.""" + import signal as _signal + import time + + for p in procs: + if p.poll() is not None: + continue + try: + os.killpg(os.getpgid(p.pid), _signal.SIGTERM) + except (ProcessLookupError, PermissionError): + p.terminate() + for p in procs: + try: + p.wait(timeout=10) + except subprocess.TimeoutExpired: + try: + os.killpg(os.getpgid(p.pid), _signal.SIGKILL) + except (ProcessLookupError, PermissionError): + p.kill() + + +def dev() -> None: + """ + Run the Cloud SQL Auth Proxy, the API, and the frontend dev server together; + Ctrl-C stops all three. + + The proxy starts first; the API only launches once 127.0.0.1: is + accepting connections, so its startup DB connection succeeds. Set + DEV_SKIP_PROXY=1 to skip the proxy (e.g. when MYSQL_HOST is a direct IP). + """ + import time + + if not (FRONTEND_DIR / "node_modules").exists(): + print("frontend/node_modules missing — run `npm install` in frontend/ first.", file=sys.stderr) + sys.exit(1) + + skip_proxy = os.environ.get("DEV_SKIP_PROXY") in ("1", "true", "True") + db_port = 3306 + + procs: list[subprocess.Popen] = [] + try: + # 1. Cloud SQL Auth Proxy (first, so the DB is reachable before the API). + if not skip_proxy: + proxy_cmd, instance, port_str = _cloud_sql_proxy_cmd() + db_port = int(port_str) + try: + procs.append(_spawn(proxy_cmd, BACKEND_DIR)) + except FileNotFoundError: + print( + f"'{proxy_cmd[0]}' not found. Install the Cloud SQL Auth Proxy, set " + "CLOUD_SQL_PROXY_BIN, or run with DEV_SKIP_PROXY=1.", + file=sys.stderr, + ) + sys.exit(1) + print(f" DB proxy → 127.0.0.1:{db_port} for {instance} (waiting…)") + if not _wait_for_port("127.0.0.1", db_port): + print(f"Cloud SQL proxy did not open 127.0.0.1:{db_port} in time.", file=sys.stderr) + raise SystemExit(1) + + # 2. API: run via uvicorn in a subprocess so it sits alongside the frontend. + procs.append( + _spawn( + [ + sys.executable, "-m", "uvicorn", "app.main:app", + "--host", DEFAULT_API_HOST, "--port", DEFAULT_API_PORT, "--reload", + ], + BACKEND_DIR, + ) + ) + # 3. Frontend dev server. + procs.append(_spawn(["npm", "run", "dev"], FRONTEND_DIR)) + + if not skip_proxy: + print(f"\n DB proxy → 127.0.0.1:{db_port}") + print(f" API → http://{DEFAULT_API_HOST}:{DEFAULT_API_PORT}") + print(" Frontend → http://localhost:5173") + print(" Press Ctrl-C to stop all.\n") + + # Exit if any process dies. + while True: + for p in procs: + code = p.poll() + if code is not None: + raise SystemExit(code) + time.sleep(0.5) + except (KeyboardInterrupt, SystemExit): + pass + finally: + _terminate(procs) diff --git a/backend/app/core/__init__.py b/backend/app/core/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/app/core/config.py b/backend/app/core/config.py index a8b0703..38550d5 100644 --- a/backend/app/core/config.py +++ b/backend/app/core/config.py @@ -59,6 +59,17 @@ class Settings(BaseSettings): # ── Resume Upload ───────────────────────────────────────────────────── MAX_RESUME_SIZE_MB: int = Field(default=10, description="Max resume upload size in MB") + # ── CORS ────────────────────────────────────────────────────────────── + CORS_ORIGINS: str = Field( + default="http://localhost:5173,http://localhost:3000,http://127.0.0.1:5173", + description="Comma-separated list of allowed CORS origins", + ) + + @property + def cors_origins(self) -> list[str]: + """Parse CORS_ORIGINS into a list of origin strings.""" + return [o.strip() for o in self.CORS_ORIGINS.split(",") if o.strip()] + @property def mysql_url(self) -> str: """Construct the async MySQL connection URL for SQLAlchemy.""" diff --git a/backend/app/core/database.py b/backend/app/core/database.py index 61c8e19..a80cade 100644 --- a/backend/app/core/database.py +++ b/backend/app/core/database.py @@ -8,7 +8,7 @@ from sqlalchemy.ext.asyncio import create_async_engine, async_sessionmaker, AsyncSession from sqlalchemy.orm import DeclarativeBase -from backend.config import get_settings +from app.core.config import get_settings # ── Monkeypatch SQLAlchemy asyncmy ping issue ────────────────────────────── # In PyMySQL 1.2.0+, ping() defaults reconnect to False. diff --git a/backend/app/core/logging.py b/backend/app/core/logging.py index 4678868..20f849d 100644 --- a/backend/app/core/logging.py +++ b/backend/app/core/logging.py @@ -1,10 +1,10 @@ """ -logger.py ---------- +app.core.logging +---------------- Centralized logging configuration for the Job Application Agent. Usage: - from logger import get_logger + from app.core.logging import get_logger logger = get_logger(__name__) logger.info("Something happened") @@ -28,7 +28,8 @@ from pathlib import Path # ── Constants ───────────────────────────────────────────────────────────────── -LOG_DIR = Path(__file__).parent / "logs" +# app/core/logging.py → parents[2] resolves to the `backend/` directory. +LOG_DIR = Path(__file__).resolve().parents[2] / "logs" timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") LOG_FILE = LOG_DIR / f"job_agent_{timestamp}.log" LOG_LEVEL_ENV = os.environ.get("LOG_LEVEL", "INFO").upper() # Override via .env diff --git a/backend/app/core/security.py b/backend/app/core/security.py index e26252f..1260986 100644 --- a/backend/app/core/security.py +++ b/backend/app/core/security.py @@ -29,9 +29,9 @@ def _patched_hashpw(password, salt): from sqlalchemy import select from sqlalchemy.ext.asyncio import AsyncSession -from backend.config import get_settings -from backend.database import get_db -from backend.models import User +from app.core.config import get_settings +from app.core.database import get_db +from app.models import User settings = get_settings() diff --git a/backend/app/main.py b/backend/app/main.py index 822f447..085a75e 100644 --- a/backend/app/main.py +++ b/backend/app/main.py @@ -1,20 +1,24 @@ """ -main.py -------- -FastAPI application entry point with CORS, lifespan events, and router includes. +app.main +-------- +FastAPI application entry point: CORS, lifespan events, and router registration. """ from contextlib import asynccontextmanager + from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware -from backend.database import engine, Base -from backend.routers import auth_router, github_router, resume_router, job_router +from app.core.config import get_settings +from app.core.database import engine, Base +from app.api.v1 import auth, github, resumes, jobs + +settings = get_settings() @asynccontextmanager async def lifespan(app: FastAPI): - """Create database tables on startup.""" + """Create database tables on startup and dispose the engine on shutdown.""" async with engine.begin() as conn: await conn.run_sync(Base.metadata.create_all) yield @@ -31,24 +35,20 @@ async def lifespan(app: FastAPI): # CORS — allow frontend dev server and production origins app.add_middleware( CORSMiddleware, - allow_origins=[ - "http://localhost:5173", # Vite dev server - "http://localhost:3000", - "http://127.0.0.1:5173", - ], + allow_origins=settings.cors_origins, allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) -# Include routers -app.include_router(auth_router.router) -app.include_router(github_router.router) -app.include_router(resume_router.router) -app.include_router(job_router.router) +# ── Routers (versioned under /api) ──────────────────────────────────────────── +app.include_router(auth.router) +app.include_router(github.router) +app.include_router(resumes.router) +app.include_router(jobs.router) -@app.get("/api/health") +@app.get("/api/health", tags=["health"]) async def health_check(): """Health check endpoint.""" return {"status": "healthy", "service": "job-application-agent"} diff --git a/backend/app/models/__init__.py b/backend/app/models/__init__.py index ea3723e..bc5c1b2 100644 --- a/backend/app/models/__init__.py +++ b/backend/app/models/__init__.py @@ -15,7 +15,7 @@ Column, Integer, String, Text, Enum, ForeignKey, DateTime, UniqueConstraint, Index ) from sqlalchemy.orm import relationship -from backend.database import Base +from app.core.database import Base class User(Base): diff --git a/backend/app/services/__init__.py b/backend/app/services/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/app/services/crew/__init__.py b/backend/app/services/crew/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/app/services/crew/__main__.py b/backend/app/services/crew/__main__.py new file mode 100644 index 0000000..ee18a15 --- /dev/null +++ b/backend/app/services/crew/__main__.py @@ -0,0 +1,68 @@ +""" +Standalone CLI for the crew pipeline. + +Run from the ``backend/`` directory: + + uv run python -m app.services.crew --url \\ + --github https://github.com/ --resume path/to/resume.md \\ + --name "Your Name" +""" + +import argparse +import json + +from dotenv import load_dotenv + +from app.core.logging import get_logger, log_token_usage +from app.services.crew.crew import build_crew +from app.services.extractors.linkedin_extractor import ( + extract_linkedin_job_details, + JobDetails, +) + +load_dotenv() +logger = get_logger(__name__) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Run the job-application crew end to end.") + parser.add_argument("--url", default="https://www.linkedin.com/jobs/view/4413867953/", + help="LinkedIn job posting URL") + parser.add_argument("--github", default="https://github.com/arijitde92", + help="GitHub profile URL") + parser.add_argument("--resume", default="sample_data/Arijit_De_Resume.md", + help="Path to the applicant's resume (.md)") + parser.add_argument("--name", default="Arijit De", help="Applicant name") + args = parser.parse_args() + + # --- Single MCP scrape: extract job details once, reuse everywhere --- + logger.info("crew CLI: Extracting job details from: %s", args.url) + job_details: JobDetails = extract_linkedin_job_details(args.url) + logger.info("crew CLI: Extracted job '%s' at '%s'", + job_details.job_name, job_details.company_name) + + # Build the JSON passed to all crew tasks — exclude about_company (not needed) + job_info = job_details.to_dict() + job_info.pop("about_company", None) + job_details_json = json.dumps(job_info, indent=2) + + job_application_inputs = { + "applicant_name": args.name.replace(" ", "_"), + "job_posting_url": args.url, + "job_name": job_details.job_name, + "company_name": job_details.company_name, + "github_url": args.github, + "resume_path": args.resume, + "job_details_json": job_details_json, # injected into every task description + } + + logger.info("crew CLI: Starting job applier crew execution...") + crew = build_crew() + result = crew.kickoff(inputs=job_application_inputs) + logger.info("crew CLI: Crew execution completed.") + if hasattr(result, "token_usage"): + log_token_usage(result.token_usage, logger) + + +if __name__ == "__main__": + main() diff --git a/backend/app/services/crew/agents.py b/backend/app/services/crew/agents.py index 0dfd525..acfe032 100644 --- a/backend/app/services/crew/agents.py +++ b/backend/app/services/crew/agents.py @@ -13,9 +13,9 @@ import json import os from dotenv import load_dotenv -from github_repo_extractor import process_github_repo_to_bq, query_github_vector_store -from webpage_extractor import extract_linkedin_job_details, JobDetails -from logger import get_logger +from app.services.extractors.github_extractor import process_github_repo_to_bq, query_github_vector_store +from app.services.extractors.linkedin_extractor import extract_linkedin_job_details, JobDetails +from app.core.logging import get_logger load_dotenv() logger = get_logger(__name__) @@ -63,10 +63,10 @@ def get_repository_links(user_url: str, github_repo_urls: List[str]) -> List[str user_name = user_url.split('/')[-1].split('?')[0] # Fetch the url of each repository - logger.info("agents.py: Searching URL: %s", user_url) + logger.info("crew.agents: Searching URL: %s", user_url) response = requests.get(user_url, headers={'User-Agent': "Chrome/51.0.2704.106"}) if response.status_code != 200: - logger.error("agents.py: Error Occurred: Response Code: %s", response.status_code) + logger.error("crew.agents: Error Occurred: Response Code: %s", response.status_code) return github_repo_urls html_content = response.content soup = BeautifulSoup(html_content, 'html.parser') @@ -74,7 +74,7 @@ def get_repository_links(user_url: str, github_repo_urls: List[str]) -> List[str for repo_heading in repo_headings: repo_name = repo_heading.a.attrs["href"].split('/')[-1] link = 'https://github.com/' + user_name + "/" + repo_name - logger.info("agents.py: Found repo: %s", link) + logger.info("crew.agents: Found repo: %s", link) github_repo_urls.append(link) pages = soup.find_all(attrs={"class": "next_page"}) if len(pages) > 0: @@ -86,11 +86,11 @@ def get_repository_links(user_url: str, github_repo_urls: List[str]) -> List[str # Call the function to get repository links github_repo_urls = get_repository_links(user_url, []) - logger.info("agents.py: Found %d repositories from %s", len(github_repo_urls), user_url) + logger.info("crew.agents: Found %d repositories from %s", len(github_repo_urls), user_url) if not github_repo_urls: - logger.warning("agents.py: No repositories found or an error occurred.") + logger.warning("crew.agents: No repositories found or an error occurred.") return None - logger.info("agents.py: Found %d repositories for user %s", len(github_repo_urls), user_url) + logger.info("crew.agents: Found %d repositories for user %s", len(github_repo_urls), user_url) for repo_url in github_repo_urls[:GITHUB_REPO_SEARCH_LIMIT]: process_github_repo_to_bq(repo_url, file_filter=lambda file_path: file_path.endswith(('.py', '.ipynb', '.md', '.txt')), @@ -115,10 +115,10 @@ def extract_linkedin_job_details_tool(url: str) -> str: str: A JSON-formatted string containing the extracted job details. Returns an error message JSON if scraping fails. """ - logger.info("agents.py: extract_linkedin_job_details_tool called for URL: %s", url) + logger.info("crew.agents: extract_linkedin_job_details_tool called for URL: %s", url) job: JobDetails = extract_linkedin_job_details(url) logger.info( - "agents.py: Extracted job '%s' at '%s'", + "crew.agents: Extracted job '%s' at '%s'", job.job_name, job.company_name ) return job.to_agent_string() @@ -226,7 +226,7 @@ def repo_content_searcher(query: str, job_description: str = None, top_k: int = ) if __name__ == "__main__": - logger.info("agents.py: Testing agents.py") + logger.info("crew.agents: Testing agents.py") # print("Testing LinkedIn Job Details Extraction Tool") # job_posting_url = "https://www.linkedin.com/jobs/view/4234610887/" # job_details = extract_linkedin_job_details_tool.run(url=job_posting_url) diff --git a/backend/app/services/crew/crew.py b/backend/app/services/crew/crew.py index f7ee7dd..bbb3e3c 100644 --- a/backend/app/services/crew/crew.py +++ b/backend/app/services/crew/crew.py @@ -1,83 +1,61 @@ -from crewai import Crew -import os -import json -from dotenv import load_dotenv -from webpage_extractor import extract_linkedin_job_details, JobDetails -from logger import get_logger, log_token_usage +""" +app.services.crew.crew +----------------------- +Crew assembly for the job-application pipeline. -load_dotenv() -logger = get_logger(__name__) +``build_crew()`` wires the four agents and their tasks into a CrewAI ``Crew``. +The web app (``app.services.crew_runner``) and the standalone CLI +(``python -m app.services.crew``) both construct the crew through this factory. +""" + +from crewai import Crew -from agents import ( +from app.core.logging import get_logger +from app.services.crew.agents import ( github_project_summarizer, profiler, resume_strategist, interview_preparer, ) - -from tasks import ( +from app.services.crew.tasks import ( github_summary_task, profile_task, resume_strategy_task, interview_preparation_task, ) -# NOTE: researcher and research_task have been removed. -# Job details are extracted once here and injected into crew inputs as -# job_details_json so every task can access them directly. - -job_applier_crew = Crew( - agents=[ - github_project_summarizer, - profiler, - resume_strategist, - interview_preparer, - ], - tasks=[ - github_summary_task, - profile_task, - resume_strategy_task, - interview_preparation_task, - ], - output_log_file="crew_log.txt", - tracing=True, - verbose=True, -) +logger = get_logger(__name__) -if __name__ == "__main__": - job_posting_url = 'https://www.linkedin.com/jobs/view/4413867953/' - # --- Single MCP scrape: extract job details once, reuse everywhere --- - logger.info("Job_Applier.py: Extracting job details from: %s", job_posting_url) - job_details: JobDetails = extract_linkedin_job_details(job_posting_url) - logger.info( - "Job_Applier.py: Extracted job '%s' at '%s'", - job_details.job_name, job_details.company_name, +def build_crew(output_log_file: str = "crew_log.txt", verbose: bool = True, tracing: bool = True) -> Crew: + """ + Construct the job-application :class:`crewai.Crew`. + + Job details are extracted once by the caller and injected into every task via + the ``job_details_json`` crew input variable, so no researcher agent is needed. + + Args: + output_log_file: Path for CrewAI's verbose execution log. + verbose: Enable verbose agent/task logging. + tracing: Enable CrewAI tracing. + + Returns: + A configured :class:`crewai.Crew` ready for ``kickoff(inputs=...)``. + """ + return Crew( + agents=[ + github_project_summarizer, + profiler, + resume_strategist, + interview_preparer, + ], + tasks=[ + github_summary_task, + profile_task, + resume_strategy_task, + interview_preparation_task, + ], + output_log_file=output_log_file, + tracing=tracing, + verbose=verbose, ) - - # Build the JSON passed to all crew tasks — exclude about_company (not needed) - job_info = job_details.to_dict() - job_info.pop("about_company", None) - job_details_json = json.dumps(job_info, indent=2) - - applicant_name = 'Arijit De' - github_url = 'https://github.com/arijitde92' - resume_path = 'Arijit_De_Resume.md' - - job_application_inputs = { - 'applicant_name': applicant_name.replace(' ', '_'), - 'job_posting_url': job_posting_url, - 'job_name': job_details.job_name, - 'company_name': job_details.company_name, - 'github_url': github_url, - 'resume_path': resume_path, - 'job_details_json': job_details_json, # injected into every task description - } - - logger.info("Job_Applier.py: Job application inputs prepared for '%s' at '%s'", - job_details.job_name, job_details.company_name) - logger.info("Job_Applier.py: Starting job applier crew execution...") - result = job_applier_crew.kickoff(inputs=job_application_inputs) - logger.info("Job_Applier.py: Crew execution completed.") - if hasattr(result, 'token_usage'): - log_token_usage(result.token_usage, logger) \ No newline at end of file diff --git a/backend/app/services/crew/tasks.py b/backend/app/services/crew/tasks.py index a7d6fb3..f626ba2 100644 --- a/backend/app/services/crew/tasks.py +++ b/backend/app/services/crew/tasks.py @@ -1,5 +1,5 @@ from crewai import Task -from agents import ( +from app.services.crew.agents import ( github_project_summarizer, profiler, resume_strategist, diff --git a/backend/app/services/crew_runner.py b/backend/app/services/crew_runner.py index 1af48ac..77a28de 100644 --- a/backend/app/services/crew_runner.py +++ b/backend/app/services/crew_runner.py @@ -1,8 +1,8 @@ """ -crew_runner.py --------------- +app.services.crew_runner +------------------------ Async wrapper for CrewAI crew execution with progress tracking via SSE. -Runs the existing agent pipeline in a thread pool to avoid blocking the async event loop. +Runs the agent pipeline in a thread pool to avoid blocking the async event loop. """ import asyncio @@ -17,10 +17,10 @@ from sqlalchemy import select, update from sqlalchemy.ext.asyncio import AsyncSession -from backend.config import get_settings -from backend.database import AsyncSessionLocal -from backend.gcs import download_file, upload_tailored_resume, upload_interview_materials -from logger import get_logger +from app.core.config import get_settings +from app.core.database import AsyncSessionLocal +from app.services.gcs_service import download_file, upload_tailored_resume, upload_interview_materials +from app.core.logging import get_logger logger = get_logger(__name__) settings = get_settings() @@ -32,13 +32,14 @@ progress_store: Dict[int, Dict[str, Any]] = {} # ── Crew verbose log path ───────────────────────────────────────────────────── -_LOG_DIR = Path(__file__).parent.parent / "logs" +# app/services/crew_runner.py → parents[2] resolves to the `backend/` directory. +_LOG_DIR = Path(__file__).resolve().parents[2] / "logs" def _update_progress(job_id: int, step: str, error: str = None): """Update the in-memory progress store for SSE broadcasting.""" progress_store[job_id] = {"current_step": step, "error": error} - logger.info("crew_runner.py: Job %d → step: %s", job_id, step) + logger.info("crew_runner: Job %d → step: %s", job_id, step) def _run_crew_sync( @@ -56,13 +57,13 @@ def _run_crew_sync( # Step 1: Extract job details _update_progress(job_id, "extracting_job_info") try: - from webpage_extractor import extract_linkedin_job_details + from app.services.extractors.linkedin_extractor import extract_linkedin_job_details job_details = extract_linkedin_job_details(job_url) job_info = job_details.to_dict() job_info.pop("about_company", None) job_details_json = json.dumps(job_info, indent=2) except Exception as e: - logger.error("crew_runner.py: Failed to extract job details for job %d: %s", job_id, e) + logger.error("crew_runner: Failed to extract job details for job %d: %s", job_id, e) raise RuntimeError(f"Failed to extract job details: {e}") @@ -76,12 +77,9 @@ def _run_crew_sync( # Step 3: Build crew and run _update_progress(job_id, "building_profile") - from crewai import Crew - from agents import ( - github_project_summarizer, profiler, resume_strategist, interview_preparer, - ) - from tasks import ( - github_summary_task, profile_task, resume_strategy_task, interview_preparation_task, + from app.services.crew.crew import build_crew + from app.services.crew.tasks import ( + resume_strategy_task, interview_preparation_task, ) applicant_name = user_email.split("@")[0].replace(".", "_") @@ -98,13 +96,8 @@ def _run_crew_sync( ts = datetime.now().strftime("%Y%m%d_%H%M%S") crew_log_path = str(_LOG_DIR / f"{ts}_crew") - crew = Crew( - agents=[github_project_summarizer, profiler, resume_strategist, interview_preparer], - tasks=[github_summary_task, profile_task, resume_strategy_task, interview_preparation_task], - verbose=True, - output_log_file=crew_log_path, - ) - logger.info("crew_runner.py: Crew verbose log → %s", crew_log_path) + crew = build_crew(output_log_file=crew_log_path, verbose=True, tracing=False) + logger.info("crew_runner: Crew verbose log → %s", crew_log_path) _update_progress(job_id, "tailoring_resume") @@ -120,7 +113,7 @@ def _run_crew_sync( } result = crew.kickoff(inputs=job_application_inputs) - logger.info("crew_runner.py: Crew execution completed for job %d. Log saved to %s", job_id, crew_log_path) + logger.info("crew_runner: Crew execution completed for job %d. Log saved to %s", job_id, crew_log_path) # Read output files tailored_resume_content = "" @@ -167,7 +160,7 @@ async def run_crew_for_job( # Update status to processing async with AsyncSessionLocal() as db: - from backend.models import Job + from app.models import Job await db.execute( update(Job).where(Job.id == job_id).values(status="processing") ) @@ -185,7 +178,7 @@ async def run_crew_for_job( # Save extracted job details to DB on the correct async event loop job_details = result["job_details"] async with AsyncSessionLocal() as db: - from backend.models import Job + from app.models import Job await db.execute( update(Job).where(Job.id == job_id).values( job_name=job_details.job_name, @@ -200,7 +193,7 @@ async def run_crew_for_job( ) ) await db.commit() - logger.info("crew_runner.py: Job details saved for job %d", job_id) + logger.info("crew_runner: Job details saved for job %d", job_id) # Upload results to GCS _update_progress(job_id, "uploading_results") @@ -219,7 +212,7 @@ async def run_crew_for_job( # Update job record async with AsyncSessionLocal() as db: - from backend.models import Job + from app.models import Job await db.execute( update(Job).where(Job.id == job_id).values( status="completed", @@ -231,16 +224,16 @@ async def run_crew_for_job( await db.commit() _update_progress(job_id, "completed") - logger.info("crew_runner.py: Job %d completed successfully", job_id) + logger.info("crew_runner: Job %d completed successfully", job_id) except Exception as e: - logger.error("crew_runner.py: Job %d failed: %s", job_id, e, exc_info=True) + logger.error("crew_runner: Job %d failed: %s", job_id, e, exc_info=True) _update_progress(job_id, "failed", error=str(e)) # Update job status to failed try: async with AsyncSessionLocal() as db: - from backend.models import Job + from app.models import Job await db.execute( update(Job).where(Job.id == job_id).values( status="failed", error_message=str(e)[:2000], @@ -248,4 +241,4 @@ async def run_crew_for_job( ) await db.commit() except Exception as db_err: - logger.error("crew_runner.py: Failed to update job %d status: %s", job_id, db_err) + logger.error("crew_runner: Failed to update job %d status: %s", job_id, db_err) diff --git a/backend/app/services/extractors/__init__.py b/backend/app/services/extractors/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/app/services/extractors/github_extractor.py b/backend/app/services/extractors/github_extractor.py index 07b3629..762a92c 100644 --- a/backend/app/services/extractors/github_extractor.py +++ b/backend/app/services/extractors/github_extractor.py @@ -10,7 +10,7 @@ import openpyxl import os # Added for directory and file operations from dotenv import load_dotenv -from logger import get_logger +from app.core.logging import get_logger logger = get_logger(__name__) # MIME types considered non-textual BINARY_MIME_PREFIXES = [ @@ -181,12 +181,12 @@ def process_github_repo_to_bq(repo_url, branch="main", file_filter=None, access_ # Check if data exists for row in results: if row.count > 0: - logger.info("github_repo_extractor.py: Data for %s/%s already exists in BigQuery. Skipping processing.", owner, repo) + logger.info("github_extractor: Data for %s/%s already exists in BigQuery. Skipping processing.", owner, repo) return except Exception as e: - logger.error("github_repo_extractor.py: Error while querying BigQuery Table with query: %s. Error: %s", query, e) + logger.error("github_extractor: Error while querying BigQuery Table with query: %s. Error: %s", query, e) - logger.info("github_repo_extractor.py: No existing data found for %s/%s. Proceeding with processing.", owner, repo) + logger.info("github_extractor: No existing data found for %s/%s. Proceeding with processing.", owner, repo) # 1. Load files from GitHub loader = GithubFileLoader( repo=f"{owner}/{repo}", @@ -197,7 +197,7 @@ def process_github_repo_to_bq(repo_url, branch="main", file_filter=None, access_ ) documents = loader.load() if not documents: - logger.warning("github_repo_extractor.py: No documents loaded from %s", repo_url) + logger.warning("github_extractor: No documents loaded from %s", repo_url) return # Add repo_username and repo_name as metadata to each document @@ -214,7 +214,7 @@ def process_github_repo_to_bq(repo_url, branch="main", file_filter=None, access_ separators=["\n\n", "\n", ".", "!", "?", ",", " ", ""], ) doc_splits = text_splitter.split_documents(documents) - logger.info("github_repo_extractor.py: Loaded and split %d document chunks from %s", len(doc_splits), repo_url) + logger.info("github_extractor: Loaded and split %d document chunks from %s", len(doc_splits), repo_url) # 3. Create Vertex AI Embeddings using the new genai package embedding_model = GoogleGenerativeAIEmbeddings( @@ -241,11 +241,11 @@ def process_github_repo_to_bq(repo_url, branch="main", file_filter=None, access_ ids = bq_store.add_documents(batch) if ids: doc_ids.extend(ids) - logger.info("github_repo_extractor.py: Added batch of %d documents.", len(batch)) + logger.info("github_extractor: Added batch of %d documents.", len(batch)) except Exception as e: - logger.error("github_repo_extractor.py: Error adding batch to BigQuery: %s", e) + logger.error("github_extractor: Error adding batch to BigQuery: %s", e) - logger.info("github_repo_extractor.py: Added %d documents to BigQuery vector store.", len(doc_ids)) + logger.info("github_extractor: Added %d documents to BigQuery vector store.", len(doc_ids)) def query_github_vector_store(query, top_k=5, dataset_name=None): """ @@ -289,8 +289,8 @@ def query_github_vector_store(query, top_k=5, dataset_name=None): # Example query query = "How do I run the chatbot locally?" results = query_github_vector_store(query) - logger.info("github_repo_extractor.py: Top relevant chunks:") + logger.info("github_extractor: Top relevant chunks:") for i, (content, metadata) in enumerate(results, 1): - logger.info("github_repo_extractor.py: Result %d:", i) - logger.info("github_repo_extractor.py: Metadata: %s", metadata) - logger.info("github_repo_extractor.py: Content: %s%s", content[:500], "..." if len(content) > 500 else "") + logger.info("github_extractor: Result %d:", i) + logger.info("github_extractor: Metadata: %s", metadata) + logger.info("github_extractor: Content: %s%s", content[:500], "..." if len(content) > 500 else "") diff --git a/backend/app/services/extractors/linkedin_extractor.py b/backend/app/services/extractors/linkedin_extractor.py index 131a265..2709325 100644 --- a/backend/app/services/extractors/linkedin_extractor.py +++ b/backend/app/services/extractors/linkedin_extractor.py @@ -5,7 +5,7 @@ from dotenv import load_dotenv from pydantic import BaseModel, Field from crewai_tools import MCPServerAdapter -from logger import get_logger +from app.core.logging import get_logger load_dotenv() logger = get_logger(__name__) @@ -86,7 +86,7 @@ def extract_linkedin_job_details(url: str, json_output: bool = False): JobDetails | str: Extracted job details. """ logger.info( - "webpage_extractor.py: Scraping LinkedIn job URL via Bright Data MCP: %s", url + "linkedin_extractor: Scraping LinkedIn job URL via Bright Data MCP: %s", url ) server_params = _get_mcp_server_params() @@ -96,7 +96,7 @@ def extract_linkedin_job_details(url: str, json_output: bool = False): with MCPServerAdapter(server_params) as mcp_tools: tool_names = [t.name for t in mcp_tools] logger.info( - "webpage_extractor.py: Connected to Bright Data MCP. Available tools: %s", + "linkedin_extractor: Connected to Bright Data MCP. Available tools: %s", tool_names, ) @@ -106,17 +106,17 @@ def extract_linkedin_job_details(url: str, json_output: bool = False): ) scrape_tool = next(t for t in mcp_tools if t.name == "scrape_as_markdown") - logger.info("webpage_extractor.py: Calling scrape_as_markdown for: %s", url) + logger.info("linkedin_extractor: Calling scrape_as_markdown for: %s", url) result = scrape_tool._run(url=url) markdown_content = str(result) logger.info( - "webpage_extractor.py: Scraping complete. Content length: %d chars", + "linkedin_extractor: Scraping complete. Content length: %d chars", len(markdown_content), ) except Exception as exc: logger.error( - "webpage_extractor.py: Failed to scrape %s — %s", url, exc, exc_info=True + "linkedin_extractor: Failed to scrape %s — %s", url, exc, exc_info=True ) error_model = JobDetails(url=url, job_name=f"ERROR: {exc}") return error_model.to_agent_string() if json_output else error_model @@ -323,7 +323,7 @@ def section_between( if __name__ == "__main__": test_url = "https://www.linkedin.com/jobs/view/4413867953/" - logger.info("webpage_extractor.py: Running standalone extraction for: %s", test_url) + logger.info("linkedin_extractor: Running standalone extraction for: %s", test_url) job: JobDetails = extract_linkedin_job_details(test_url) diff --git a/backend/app/services/gcs_service.py b/backend/app/services/gcs_service.py index a48dc41..86130e4 100644 --- a/backend/app/services/gcs_service.py +++ b/backend/app/services/gcs_service.py @@ -13,7 +13,7 @@ import datetime from google.cloud import storage -from backend.config import get_settings +from app.core.config import get_settings settings = get_settings() diff --git a/backend/pyproject.toml b/backend/pyproject.toml new file mode 100644 index 0000000..00c0ca9 --- /dev/null +++ b/backend/pyproject.toml @@ -0,0 +1,76 @@ +[project] +name = "job-application-agent-backend" +version = "1.0.0" +description = "AI-powered resume tailoring and interview preparation — FastAPI backend + CrewAI pipeline" +readme = "README.md" +requires-python = ">=3.11" +dependencies = [ + # ── Web framework ────────────────────────────────────────────────────── + # Pinned to the 0.115 line: fastapi 0.137.x has a regression where + # include_router() silently registers no routes in this environment. + # Bump only after verifying include_router works (see tests/test_health.py + # and the /api route count). + "fastapi>=0.115.0,<0.116", + "uvicorn[standard]>=0.30.0", + "python-multipart>=0.0.7", + "sse-starlette>=1.8.0", + # ── Database ─────────────────────────────────────────────────────────── + "sqlalchemy[asyncio]>=2.0", + "asyncmy>=0.2.9", + "pymysql>=1.1.0", + # ── Config & validation ──────────────────────────────────────────────── + "pydantic[email]>=2.0", + "pydantic-settings>=2.0", + "python-dotenv>=1.0.0", + # ── Auth ─────────────────────────────────────────────────────────────── + "python-jose[cryptography]>=3.3.0", + "passlib[bcrypt]>=1.7.4", + "google-auth>=2.25.0", + # ── Google Cloud ─────────────────────────────────────────────────────── + "google-cloud-storage>=2.14.0", + "google-cloud-bigquery>=3.0.0", + # ── LLM / Agent pipeline (CrewAI) ────────────────────────────────────── + "crewai[anthropic]", + "crewai-tools", + # ── RAG / embeddings ─────────────────────────────────────────────────── + "langchain-community", + "langchain-google-genai", + "langchain-google-community[featurestore]", + "langchain-text-splitters", + # ── Document parsing & scraping ──────────────────────────────────────── + "beautifulsoup4", + "openpyxl", + "pandas", + "pypdf", + "python-docx", + "requests", +] + +[project.scripts] +# Custom commands — invoke from backend/ with `uv run `. +api = "app.cli:api" # FastAPI dev server (reload) +api-prod = "app.cli:api_prod" # FastAPI production server (workers, no reload) +frontend = "app.cli:frontend" # Vite dev server (npm run dev) +dev = "app.cli:dev" # backend api + cloud SQL Proxy + frontend together +db-proxy = "app.cli:db_proxy" # Cloud SQL Auth Proxy (127.0.0.1:3306) +crew = "app.cli:crew" # CrewAI pipeline CLI + +[dependency-groups] +dev = [ + "pytest>=8.0", + "pytest-asyncio>=0.23", + "httpx>=0.27", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +# The importable package is `app/`; only it needs to be built into the wheel +# so the [project.scripts] entrypoints resolve. +packages = ["app"] + +[tool.pytest.ini_options] +asyncio_mode = "auto" +testpaths = ["tests"] diff --git 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real values. +# Only variables prefixed with VITE_ are exposed to the client bundle. +# ────────────────────────────────────────────────────────────────────────── + +# Base URL for the backend API. In dev, Vite proxies /api → http://localhost:8000 +# (see vite.config.js), so this can stay as the relative default. +VITE_API_BASE_URL=/api + +# Google OAuth client ID (for "Sign in with Google") +VITE_GOOGLE_OAUTH_CLIENT_ID= diff --git a/frontend/Dockerfile b/frontend/Dockerfile new file mode 100644 index 0000000..e69de29 diff --git a/frontend/README.md b/frontend/README.md index a36934d..aca47a8 100644 --- a/frontend/README.md +++ b/frontend/README.md @@ -1,16 +1,47 @@ -# React + Vite +# Job Application Agent — Frontend -This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules. +React + Vite (JavaScript) single-page app. Authenticates against the FastAPI +backend, lets users link a GitHub profile and upload a resume, kicks off the +CrewAI tailoring pipeline, and streams live progress via Server-Sent Events. -Currently, two official plugins are available: +## Layout -- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Oxc](https://oxc.rs) -- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/) +``` +frontend/ +├── src/ +│ ├── assets/ # Static media (images, icons) +│ ├── components/ # Reusable UI components +│ ├── pages/ # Route-level views +│ ├── context/ # React context providers (AuthContext) +│ ├── hooks/ # Custom React hooks +│ ├── services/ # API client (api.js — axios instance + interceptors) +│ ├── App.jsx # Route layout +│ └── main.jsx # Entry point +├── public/ +├── .env.example +├── package.json +└── vite.config.js # Dev server proxies /api → http://localhost:8000 +``` -## React Compiler +## Setup -The React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see [this documentation](https://react.dev/learn/react-compiler/installation). +```bash +cp .env.example .env +npm install +``` -## Expanding the ESLint configuration +## Develop -If you are developing a production application, we recommend using TypeScript with type-aware lint rules enabled. Check out the [TS template](https://github.com/vitejs/vite/tree/main/packages/create-vite/template-react-ts) for information on how to integrate TypeScript and [`typescript-eslint`](https://typescript-eslint.io) in your project. +```bash +npm run dev # http://localhost:5173 +``` + +The dev server proxies `/api/*` to the backend at `http://localhost:8000` +(see `vite.config.js`), so run the backend alongside it. + +## Build + +```bash +npm run build # outputs to dist/ +npm run preview +``` diff --git a/frontend/src/components/GithubProfileAdd.jsx b/frontend/src/components/GithubProfileAdd.jsx index 2d44baa..3ddcd12 100644 --- a/frontend/src/components/GithubProfileAdd.jsx +++ b/frontend/src/components/GithubProfileAdd.jsx @@ -1,5 +1,5 @@ import { useState } from 'react'; -import api from '../api/client'; +import api from '../services/api'; import { FiGithub, FiTrash2, FiPlus } from 'react-icons/fi'; export default function GithubProfileAdd({ profiles, onRefresh }) { diff --git a/frontend/src/components/LoginSection.jsx b/frontend/src/components/LoginSection.jsx index 3aecd77..2ee3752 100644 --- a/frontend/src/components/LoginSection.jsx +++ b/frontend/src/components/LoginSection.jsx @@ -1,5 +1,5 @@ import { useState } from 'react'; -import api from '../api/client'; +import api from '../services/api'; import { useAuth } from '../context/AuthContext'; import { FiMail, FiLock } from 'react-icons/fi'; diff --git a/frontend/src/components/ResumePreviewModal.jsx b/frontend/src/components/ResumePreviewModal.jsx index 00f0029..e0bcd15 100644 --- a/frontend/src/components/ResumePreviewModal.jsx +++ b/frontend/src/components/ResumePreviewModal.jsx @@ -1,7 +1,7 @@ import { useState, useEffect } from 'react'; import ReactMarkdown from 'react-markdown'; import remarkGfm from 'remark-gfm'; -import api from '../api/client'; +import api from '../services/api'; import { FiX } from 'react-icons/fi'; export default function ResumePreviewModal({ jobId, resumeId, onClose }) { diff --git a/frontend/src/components/ResumeReadyModal.jsx b/frontend/src/components/ResumeReadyModal.jsx index 03246aa..c9b9e3c 100644 --- a/frontend/src/components/ResumeReadyModal.jsx +++ b/frontend/src/components/ResumeReadyModal.jsx @@ -1,6 +1,6 @@ import { useNavigate } from 'react-router-dom'; import { FiEye, FiDownload, FiBookOpen, FiX } from 'react-icons/fi'; -import api from '../api/client'; +import api from '../services/api'; export default function ResumeReadyModal({ job, onClose, onPreviewResume }) { const navigate = useNavigate(); diff --git a/frontend/src/components/ResumeUpload.jsx b/frontend/src/components/ResumeUpload.jsx index 5fa8b93..0081908 100644 --- a/frontend/src/components/ResumeUpload.jsx +++ b/frontend/src/components/ResumeUpload.jsx @@ -1,5 +1,5 @@ import { useState, useRef } from 'react'; -import api from '../api/client'; +import api from '../services/api'; import { FiUploadCloud, FiFile, FiTrash2, FiExternalLink } from 'react-icons/fi'; export default function ResumeUpload({ resumes, onRefresh }) { diff --git a/frontend/src/components/TailorResumeForm.jsx b/frontend/src/components/TailorResumeForm.jsx index f409645..0b0a5d9 100644 --- a/frontend/src/components/TailorResumeForm.jsx +++ b/frontend/src/components/TailorResumeForm.jsx @@ -1,5 +1,5 @@ import { useState } from 'react'; -import api from '../api/client'; +import api from '../services/api'; import { FiLink, FiSend, FiEye } from 'react-icons/fi'; export default function TailorResumeForm({ githubProfiles, resumes, onJobCreated, onPreviewUploadedResume }) { diff --git a/frontend/src/components/TailoredResumeList.jsx b/frontend/src/components/TailoredResumeList.jsx index cc3cd71..e988d03 100644 --- a/frontend/src/components/TailoredResumeList.jsx +++ b/frontend/src/components/TailoredResumeList.jsx @@ -1,6 +1,6 @@ import { useNavigate } from 'react-router-dom'; import { FiEye, FiDownload, FiBookOpen } from 'react-icons/fi'; -import api from '../api/client'; +import api from '../services/api'; export default function TailoredResumeList({ jobs, onPreviewResume }) { const navigate = useNavigate(); diff --git a/frontend/src/context/AuthContext.jsx b/frontend/src/context/AuthContext.jsx index f6f09ec..c8b995a 100644 --- a/frontend/src/context/AuthContext.jsx +++ b/frontend/src/context/AuthContext.jsx @@ -1,5 +1,5 @@ import { createContext, useContext, useState, useEffect, useCallback } from 'react'; -import api from '../api/client'; +import api from '../services/api'; const AuthContext = createContext(null); diff --git a/frontend/src/hooks/.gitkeep b/frontend/src/hooks/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/frontend/src/pages/HomePage.jsx b/frontend/src/pages/HomePage.jsx index dbb102c..769c324 100644 --- a/frontend/src/pages/HomePage.jsx +++ b/frontend/src/pages/HomePage.jsx @@ -1,6 +1,6 @@ import { useState, useEffect, useCallback } from 'react'; import { useAuth } from '../context/AuthContext'; -import api from '../api/client'; +import api from '../services/api'; import Navbar from '../components/Navbar'; import ResumeUpload from '../components/ResumeUpload'; import GithubProfileAdd from '../components/GithubProfileAdd'; diff --git a/frontend/src/pages/InterviewMaterialsPage.jsx b/frontend/src/pages/InterviewMaterialsPage.jsx index e585012..436faca 100644 --- a/frontend/src/pages/InterviewMaterialsPage.jsx +++ b/frontend/src/pages/InterviewMaterialsPage.jsx @@ -2,7 +2,7 @@ import { useState, useEffect } from 'react'; import { useParams, Link } from 'react-router-dom'; import ReactMarkdown from 'react-markdown'; import remarkGfm from 'remark-gfm'; -import api from '../api/client'; +import api from '../services/api'; import Navbar from '../components/Navbar'; import { FiArrowLeft, FiDownload } from 'react-icons/fi'; diff --git a/frontend/src/pages/RegisterPage.jsx b/frontend/src/pages/RegisterPage.jsx index 0d66cac..f847438 100644 --- a/frontend/src/pages/RegisterPage.jsx +++ b/frontend/src/pages/RegisterPage.jsx @@ -1,6 +1,6 @@ import { useState } from 'react'; import { Link, useNavigate } from 'react-router-dom'; -import api from '../api/client'; +import api from '../services/api'; import { useAuth } from '../context/AuthContext'; import { FiUser, FiMail, FiLock, FiArrowLeft } from 'react-icons/fi'; diff --git a/frontend/src/api/client.js b/frontend/src/services/api.js similarity index 100% rename from frontend/src/api/client.js rename to frontend/src/services/api.js From 5bdf766b7002f2f67169581581d1b5e3d3b9df83 Mon Sep 17 00:00:00 2001 From: Arijit De Date: Thu, 18 Jun 2026 16:31:06 +0000 Subject: [PATCH 06/11] feat: optional GitHub profile, resume view/download/delete, UI fixes GitHub profile is now optional for resume tailoring and interview prep. Also adds resume view/download/delete and fixes UI overlap issues. GitHub optional: - crew tasks.py: replace module-level Task singletons with a build_tasks( include_github) factory returning fresh tasks per run. Without GitHub, the summarizer task is omitted and the profiler/strategist tasks get explicit grounding rules so agents do not fabricate projects, skills, or metrics beyond the resume + job details. Fresh per-request tasks also fix a race where concurrent jobs overwrote each other's output_file. - crew.py: build_crew(include_github=True) conditionally wires in the GitHub agent + task; return type stays Crew (CLI/runner unchanged). - crew_runner.py: GitHub params default to None; derive include_github from github_url; stamp output paths on per-request crew.tasks[-1]/[-2]; null-guard github_profile access; skip the searching_projects step. - jobs.py: POST /tailor looks up a GitHub profile only when one is given. - schemas: github_profile_id is now Optional[int] = None. - models: Job.github_profile_id is nullable with ON DELETE SET NULL. - frontend: GitHub select optional ("None / no GitHub"); omit github_profile_id when unset; ProgressTracker hides the GitHub step. - migrations.md: documents the one-time manual ALTER for existing DBs (no Alembic; create_all does not alter existing columns). Resume management & UI: - Add view & download buttons for uploaded resumes, reusing the existing /resumes/{id}/content endpoint. - Render ResumePreviewModal via a portal to document.body so it escapes the glass-card's backdrop-filter containing block (full-width, centered). - Add DELETE /api/jobs/{job_id} to remove tailored resumes: cleans up GCS artifacts and the DB record, with a confirm-guarded delete button in TailoredResumeList. - Fix LandingPage upload UI overlap. Co-Authored-By: Claude Opus 4.8 (1M context) --- backend/app/api/v1/jobs.py | 55 +++- backend/app/models/__init__.py | 4 +- backend/app/schemas/__init__.py | 2 +- backend/app/services/crew/crew.py | 46 +-- backend/app/services/crew/tasks.py | 261 +++++++++++------- backend/app/services/crew_runner.py | 48 +++- backend/docs/migrations.md | 41 +++ frontend/src/components/ProgressTracker.jsx | 9 +- .../src/components/ResumePreviewModal.jsx | 6 +- frontend/src/components/ResumeUpload.jsx | 45 ++- frontend/src/components/TailorResumeForm.jsx | 21 +- .../src/components/TailoredResumeList.jsx | 67 +++-- frontend/src/pages/HomePage.jsx | 8 +- frontend/src/pages/LandingPage.jsx | 8 +- 14 files changed, 432 insertions(+), 189 deletions(-) create mode 100644 backend/docs/migrations.md diff --git a/backend/app/api/v1/jobs.py b/backend/app/api/v1/jobs.py index 49adece..86b4c73 100644 --- a/backend/app/api/v1/jobs.py +++ b/backend/app/api/v1/jobs.py @@ -15,7 +15,7 @@ from app.models import User, Job, GithubProfile, Resume from app.schemas import TailorJobRequest, JobResponse, JobDetailResponse from app.api.deps import get_current_user -from app.services.gcs_service import download_file +from app.services.gcs_service import download_file, delete_file from app.services.crew_runner import run_crew_for_job, progress_store router = APIRouter(prefix="/api/jobs", tags=["jobs"]) @@ -27,16 +27,22 @@ async def tailor_resume( current_user: User = Depends(get_current_user), db: AsyncSession = Depends(get_db), ): - """Start a crew execution to tailor a resume for a job posting.""" - gh_result = await db.execute( - select(GithubProfile).where( - GithubProfile.id == req.github_profile_id, - GithubProfile.user_id == current_user.id, + """Start a crew execution to tailor a resume for a job posting. + + A GitHub profile is optional — when ``github_profile_id`` is omitted the + resume is tailored from the job description + uploaded resume alone. + """ + github_profile = None + if req.github_profile_id is not None: + gh_result = await db.execute( + select(GithubProfile).where( + GithubProfile.id == req.github_profile_id, + GithubProfile.user_id == current_user.id, + ) ) - ) - github_profile = gh_result.scalar_one_or_none() - if not github_profile: - raise HTTPException(status_code=404, detail="GitHub profile not found.") + github_profile = gh_result.scalar_one_or_none() + if not github_profile: + raise HTTPException(status_code=404, detail="GitHub profile not found.") resume_result = await db.execute( select(Resume).where(Resume.id == req.resume_id, Resume.user_id == current_user.id) @@ -93,6 +99,35 @@ async def get_job_detail( return job +@router.delete("/{job_id}", status_code=status.HTTP_204_NO_CONTENT) +async def delete_job( + job_id: int, current_user: User = Depends(get_current_user), + db: AsyncSession = Depends(get_db), +): + """ + Delete a generated/tailored resume job. Removes the tailored resume and + interview materials from GCS, then deletes the DB record. + """ + result = await db.execute( + select(Job).where(Job.id == job_id, Job.user_id == current_user.id) + ) + job = result.scalar_one_or_none() + if not job: + raise HTTPException(status_code=404, detail="Job not found.") + + # Best-effort cleanup of GCS artifacts; missing files should not block deletion. + for gcs_path in (job.tailored_resume_gcs_path, job.interview_materials_gcs_path): + if gcs_path: + try: + delete_file(gcs_path) + except Exception: + pass + + await db.delete(job) + await db.commit() + return Response(status_code=status.HTTP_204_NO_CONTENT) + + @router.get("/{job_id}/progress") async def stream_job_progress( job_id: int, current_user: User = Depends(get_current_user), diff --git a/backend/app/models/__init__.py b/backend/app/models/__init__.py index bc5c1b2..31ec2cb 100644 --- a/backend/app/models/__init__.py +++ b/backend/app/models/__init__.py @@ -77,7 +77,9 @@ class Job(Base): id = Column(Integer, primary_key=True, autoincrement=True) user_id = Column(Integer, ForeignKey("users.id", ondelete="CASCADE"), nullable=False) - github_profile_id = Column(Integer, ForeignKey("github_profiles.id", ondelete="RESTRICT"), nullable=False) + # Optional: a user may tailor a resume without a GitHub profile. + # ON DELETE SET NULL so deleting a profile leaves historical jobs intact. + github_profile_id = Column(Integer, ForeignKey("github_profiles.id", ondelete="SET NULL"), nullable=True) resume_id = Column(Integer, ForeignKey("resumes.id", ondelete="RESTRICT"), nullable=False) linkedin_job_url = Column(String(500), nullable=False) diff --git a/backend/app/schemas/__init__.py b/backend/app/schemas/__init__.py index 6e595a6..ec8eece 100644 --- a/backend/app/schemas/__init__.py +++ b/backend/app/schemas/__init__.py @@ -73,7 +73,7 @@ class ResumeResponse(BaseModel): class TailorJobRequest(BaseModel): linkedin_job_url: str = Field(..., description="LinkedIn job posting URL") - github_profile_id: int = Field(..., description="ID of the GitHub profile to use") + github_profile_id: Optional[int] = Field(None, description="ID of the GitHub profile to use (optional)") resume_id: int = Field(..., description="ID of the uploaded resume to use") diff --git a/backend/app/services/crew/crew.py b/backend/app/services/crew/crew.py index bbb3e3c..b0624c4 100644 --- a/backend/app/services/crew/crew.py +++ b/backend/app/services/crew/crew.py @@ -3,9 +3,14 @@ ----------------------- Crew assembly for the job-application pipeline. -``build_crew()`` wires the four agents and their tasks into a CrewAI ``Crew``. +``build_crew()`` wires the agents and their tasks into a CrewAI ``Crew``. The web app (``app.services.crew_runner``) and the standalone CLI (``python -m app.services.crew``) both construct the crew through this factory. + +When ``include_github`` is False (the applicant has no GitHub profile) the +GitHub summarizer agent and task are omitted, and the remaining agents work +from the resume + job details alone with explicit grounding rules (see +``app.services.crew.tasks``). """ from crewai import Crew @@ -17,17 +22,17 @@ resume_strategist, interview_preparer, ) -from app.services.crew.tasks import ( - github_summary_task, - profile_task, - resume_strategy_task, - interview_preparation_task, -) +from app.services.crew.tasks import build_tasks logger = get_logger(__name__) -def build_crew(output_log_file: str = "crew_log.txt", verbose: bool = True, tracing: bool = True) -> Crew: +def build_crew( + output_log_file: str = "crew_log.txt", + verbose: bool = True, + tracing: bool = True, + include_github: bool = True, +) -> Crew: """ Construct the job-application :class:`crewai.Crew`. @@ -38,23 +43,24 @@ def build_crew(output_log_file: str = "crew_log.txt", verbose: bool = True, trac output_log_file: Path for CrewAI's verbose execution log. verbose: Enable verbose agent/task logging. tracing: Enable CrewAI tracing. + include_github: When True, include the GitHub summarizer agent/task and + require the ``github_url`` / ``bq_dataset_name`` crew inputs. When + False, skip GitHub entirely and tailor from the resume + job details. Returns: A configured :class:`crewai.Crew` ready for ``kickoff(inputs=...)``. + The task list order is stable: the interview task is always last and the + resume task always second-to-last (``crew.tasks[-1]`` / ``[-2]``). """ + tasks = build_tasks(include_github=include_github) + + agents = [profiler, resume_strategist, interview_preparer] + if include_github: + agents.insert(0, github_project_summarizer) + return Crew( - agents=[ - github_project_summarizer, - profiler, - resume_strategist, - interview_preparer, - ], - tasks=[ - github_summary_task, - profile_task, - resume_strategy_task, - interview_preparation_task, - ], + agents=agents, + tasks=tasks, output_log_file=output_log_file, tracing=tracing, verbose=verbose, diff --git a/backend/app/services/crew/tasks.py b/backend/app/services/crew/tasks.py index f626ba2..45fb352 100644 --- a/backend/app/services/crew/tasks.py +++ b/backend/app/services/crew/tasks.py @@ -8,114 +8,181 @@ repo_content_searcher, read_resume, semantic_search_resume, - scrape_tool, - search_tool, ) # --------------------------------------------------------------------------- # NOTE: The researcher agent and research_task have been removed. # Job details (job_description, requirements, seniority_level, etc.) are -# pre-fetched by Job_Applier.py using extract_linkedin_job_details() and +# pre-fetched by the crew_runner using extract_linkedin_job_details() and # injected into every task via the {job_details_json} crew input variable. # This avoids a redundant MCP web scrape during crew execution. +# +# Tasks are built per crew run by build_tasks(include_github). When the +# applicant has NOT provided a GitHub profile, the GitHub summarizer task is +# omitted entirely (no scraping / BigQuery indexing) and the remaining tasks +# are given explicit grounding rules so the agents do NOT fabricate any +# GitHub projects, skills, or experience that is not in the resume. +# +# Building fresh Task objects per call (rather than reusing module-level +# singletons) also makes per-request output_file overrides thread-safe under +# the crew_runner's ThreadPoolExecutor. # --------------------------------------------------------------------------- -# Task 1: GitHub Project Summarizer — index repos and find relevant projects -github_summary_task = Task( - description=( - "You have been given structured job details in JSON format:\n\n" - "{job_details_json}\n\n" - "Step 1 — Index the applicant's GitHub repositories:\n" - " Use the extract_github_repos_tool with the GitHub URL: {github_url}\n\n" - "Step 2 — Build a search query:\n" - " From the job_description and requirements fields in the job details above, " - "identify the key tech stacks, programming languages, frameworks, tools, and " - "cloud technologies required. Formulate a concise search query from these.\n\n" - "Step 3 — Search the vector store:\n" - " Use the repo_content_searcher tool with your query (input argument 'query') " - "and pass the job_description as the 'job_description' argument.\n\n" - "Step 4 — Summarise:\n" - " Analyse the returned repository content and identify the applicant's most " - "relevant projects. Highlight tech stacks, languages, frameworks, tools, and " - "cloud technologies that match the job requirements." - ), - expected_output=( - "A concise summary of the applicant's most relevant GitHub projects, including " - "project names, tech stacks, programming languages, frameworks, tools, and cloud " - "technologies used. The summary should clearly connect the applicant's project " - "experience to the specific job requirements." - ), - tools=[extract_github_repos_tool, repo_content_searcher], - agent=github_project_summarizer, - async_execution=False, -) - +# Description fragments toggled by include_github ------------------------------- -# Task 2: Profiler — build a comprehensive applicant profile -profile_task = Task( - description=( - "Compile a detailed personal and professional profile for the applicant.\n\n" - "Job details (for context):\n{job_details_json}\n\n" - "Steps:\n" - " 1. Read and semantically understand the resume from: {resume_path}\n" - " 2. Use the job_description and requirements from the job details above " - "to understand what the employer is looking for.\n" - " 3. Use the GitHub projects summary from the previous task as additional context.\n" - " 4. Synthesise all three sources into a comprehensive applicant profile." - ), - context=[github_summary_task], - expected_output=( - "A comprehensive profile document that includes the applicant's skills, " - "project experiences, contributions, interests, and communication style, " - "grounded in the resume, GitHub work, and aligned to the job requirements." - ), - tools=[read_resume, semantic_search_resume], - agent=profiler, - async_execution=False, +# Profiler — step list ending differs depending on whether GitHub data exists. +_PROFILE_GITHUB_STEPS = ( + " 3. Use the GitHub projects summary from the previous task as additional context.\n" + " 4. Synthesise all three sources into a comprehensive applicant profile." ) - -# Task 3: Resume Strategist — tailor the resume to the job -resume_strategy_task = Task( - description=( - "Tailor the applicant's resume to maximise its fit for the target role.\n\n" - "Job details (use for ATS keyword alignment):\n{job_details_json}\n\n" - "Use the applicant profile and GitHub projects summary from previous tasks, " - "together with the job_description and requirements above, to:\n" - " - Update every resume section (summary, work experience, skills, education).\n" - " - Embed relevant keywords from the job description for ATS optimisation.\n" - " - Quantify achievements with practical, believable numbers where appropriate.\n" - " - Ensure the resume catches the recruiter's eye at first glance." - ), - expected_output=( - "An updated resume in Markdown format that effectively highlights the candidate's " - "qualifications and experiences most relevant to the target role." - ), - output_file="{applicant_name}_{company_name}_{job_name}_resume.md", - context=[profile_task], - agent=resume_strategist, - tools=[read_resume, semantic_search_resume], - markdown=True, - async_execution=False, +_PROFILE_NO_GITHUB_STEPS = ( + " 3. Synthesise the resume and job details into a comprehensive applicant profile.\n\n" + "IMPORTANT GROUNDING RULES:\n" + " - No GitHub profile was provided for this applicant.\n" + " - Use ONLY information that appears in the resume and the job details above.\n" + " - Do NOT invent, assume, or fabricate any projects, repositories, GitHub " + "activity, employers, dates, metrics, or skills that are not present in the resume." ) -# Task 4: Interview Preparer — generate interview questions and talking points -interview_preparation_task = Task( - description=( - "Create targeted interview questions and talking points for the applicant.\n\n" - "Job details:\n{job_details_json}\n\n" - "Use the tailored resume from the resume strategy task and the profile from " - "the profile task to:\n" - " - Generate likely interview questions based on the job_description and requirements.\n" - " - Prepare concise talking points that highlight how the applicant's experience " - "matches each key requirement.\n" - " - Help the candidate confidently address all aspects of the role." - ), - expected_output=( - "A document containing key interview questions and tailored talking points that " - "help the candidate demonstrate how their skills and experience match the role." - ), - output_file="interview_materials.md", - context=[profile_task, resume_strategy_task], - agent=interview_preparer, - async_execution=False, +# Resume Strategist — clause naming the inputs differs depending on GitHub data. +_RESUME_GITHUB_CLAUSE = ( + "Use the applicant profile and GitHub projects summary from previous tasks, " + "together with the job_description and requirements above, to:\n" ) +_RESUME_NO_GITHUB_CLAUSE = ( + "Use the applicant profile from the previous task, together with the " + "job_description and requirements above, to:\n" + "DO NOT add any experience, projects, or skills that are not already present " + "in the applicant profile / original resume. Rephrase and reorder for fit only.\n" +) + + +def _github_summary_task() -> Task: + """Task 1 — index GitHub repos and summarise the most relevant projects.""" + return Task( + description=( + "You have been given structured job details in JSON format:\n\n" + "{job_details_json}\n\n" + "Step 1 — Index the applicant's GitHub repositories:\n" + " Use the extract_github_repos_tool with the GitHub URL: {github_url}\n\n" + "Step 2 — Build a search query:\n" + " From the job_description and requirements fields in the job details above, " + "identify the key tech stacks, programming languages, frameworks, tools, and " + "cloud technologies required. Formulate a concise search query from these.\n\n" + "Step 3 — Search the vector store:\n" + " Use the repo_content_searcher tool with your query (input argument 'query') " + "and pass the job_description as the 'job_description' argument.\n\n" + "Step 4 — Summarise:\n" + " Analyse the returned repository content and identify the applicant's most " + "relevant projects. Highlight tech stacks, languages, frameworks, tools, and " + "cloud technologies that match the job requirements." + ), + expected_output=( + "A concise summary of the applicant's most relevant GitHub projects, including " + "project names, tech stacks, programming languages, frameworks, tools, and cloud " + "technologies used. The summary should clearly connect the applicant's project " + "experience to the specific job requirements." + ), + tools=[extract_github_repos_tool, repo_content_searcher], + agent=github_project_summarizer, + async_execution=False, + ) + + +def _profile_task(include_github: bool, github_task: Task | None) -> Task: + """Task 2 — build a comprehensive applicant profile.""" + steps = _PROFILE_GITHUB_STEPS if include_github else _PROFILE_NO_GITHUB_STEPS + grounded_in = "the resume, GitHub work, and" if include_github else "the resume and" + return Task( + description=( + "Compile a detailed personal and professional profile for the applicant.\n\n" + "Job details (for context):\n{job_details_json}\n\n" + "Steps:\n" + " 1. Read and semantically understand the resume from: {resume_path}\n" + " 2. Use the job_description and requirements from the job details above " + "to understand what the employer is looking for.\n" + + steps + ), + context=[github_task] if include_github else [], + expected_output=( + "A comprehensive profile document that includes the applicant's skills, " + "project experiences, contributions, interests, and communication style, " + f"grounded in {grounded_in} aligned to the job requirements." + ), + tools=[read_resume, semantic_search_resume], + agent=profiler, + async_execution=False, + ) + + +def _resume_strategy_task(include_github: bool, profile_task: Task) -> Task: + """Task 3 — tailor the resume to the job.""" + clause = _RESUME_GITHUB_CLAUSE if include_github else _RESUME_NO_GITHUB_CLAUSE + return Task( + description=( + "Tailor the applicant's resume to maximise its fit for the target role.\n\n" + "Job details (use for ATS keyword alignment):\n{job_details_json}\n\n" + + clause + + " - Update every resume section (summary, work experience, skills, education).\n" + " - Embed relevant keywords from the job description for ATS optimisation.\n" + " - Quantify achievements with practical, believable numbers where appropriate.\n" + " - Ensure the resume catches the recruiter's eye at first glance." + ), + expected_output=( + "An updated resume in Markdown format that effectively highlights the candidate's " + "qualifications and experiences most relevant to the target role." + ), + output_file="{applicant_name}_{company_name}_{job_name}_resume.md", + context=[profile_task], + agent=resume_strategist, + tools=[read_resume, semantic_search_resume], + markdown=True, + async_execution=False, + ) + + +def _interview_preparation_task(profile_task: Task, resume_task: Task) -> Task: + """Task 4 — generate interview questions and talking points.""" + return Task( + description=( + "Create targeted interview questions and talking points for the applicant.\n\n" + "Job details:\n{job_details_json}\n\n" + "Use the tailored resume from the resume strategy task and the profile from " + "the profile task to:\n" + " - Generate likely interview questions based on the job_description and requirements.\n" + " - Prepare concise talking points that highlight how the applicant's experience " + "matches each key requirement.\n" + " - Help the candidate confidently address all aspects of the role." + ), + expected_output=( + "A document containing key interview questions and tailored talking points that " + "help the candidate demonstrate how their skills and experience match the role." + ), + output_file="interview_materials.md", + context=[profile_task, resume_task], + agent=interview_preparer, + async_execution=False, + ) + + +def build_tasks(include_github: bool = True) -> list[Task]: + """ + Construct a fresh, correctly-wired task list for one crew run. + + When ``include_github`` is False the GitHub summarizer task is omitted and + the profile/resume tasks are given grounding rules so the agents do not + fabricate GitHub-derived content. + + Return order is stable across both modes: the interview task is always the + last element and the resume task always the second-to-last, so the runner + can stamp per-request ``output_file`` paths via ``crew.tasks[-1]/[-2]``. + """ + github_task = _github_summary_task() if include_github else None + profile = _profile_task(include_github, github_task) + resume = _resume_strategy_task(include_github, profile) + interview = _interview_preparation_task(profile, resume) + + tasks = [profile, resume, interview] + if include_github: + tasks.insert(0, github_task) + return tasks diff --git a/backend/app/services/crew_runner.py b/backend/app/services/crew_runner.py index 77a28de..335029c 100644 --- a/backend/app/services/crew_runner.py +++ b/backend/app/services/crew_runner.py @@ -44,16 +44,22 @@ def _update_progress(job_id: int, step: str, error: str = None): def _run_crew_sync( job_id: int, user_id: int, user_email: str, - github_username: str, github_url: str, bq_dataset_name: str, - resume_content: str, job_url: str, + github_username: str = None, github_url: str = None, bq_dataset_name: str = None, + resume_content: str = None, job_url: str = None, ): """ Synchronous crew execution — runs in a thread. This imports and uses the existing crew pipeline. + + When no GitHub URL is supplied the crew is built without the GitHub + summarizer agent/task and tailors the resume from the resume + job + details alone. """ from dotenv import load_dotenv load_dotenv() + include_github = bool(github_url) + # Step 1: Extract job details _update_progress(job_id, "extracting_job_info") try: @@ -68,7 +74,9 @@ def _run_crew_sync( # Step 2: Write resume to temp file for crew to read - _update_progress(job_id, "searching_projects") + # The "searching_projects" step only applies when GitHub repos are indexed; + # without GitHub we advance straight to building the profile. + _update_progress(job_id, "searching_projects" if include_github else "building_profile") with tempfile.NamedTemporaryFile(mode="w", suffix=".md", delete=False, prefix="resume_") as f: f.write(resume_content) resume_path = f.name @@ -78,9 +86,6 @@ def _run_crew_sync( _update_progress(job_id, "building_profile") from app.services.crew.crew import build_crew - from app.services.crew.tasks import ( - resume_strategy_task, interview_preparation_task, - ) applicant_name = user_email.split("@")[0].replace(".", "_") @@ -88,17 +93,21 @@ def _run_crew_sync( resume_output = tempfile.mktemp(suffix="_resume.md", prefix=f"job{job_id}_") interview_output = tempfile.mktemp(suffix="_interview.md", prefix=f"job{job_id}_") - # Override output_file dynamically - resume_strategy_task.output_file = resume_output - interview_preparation_task.output_file = interview_output - _LOG_DIR.mkdir(parents=True, exist_ok=True) ts = datetime.now().strftime("%Y%m%d_%H%M%S") crew_log_path = str(_LOG_DIR / f"{ts}_crew") - crew = build_crew(output_log_file=crew_log_path, verbose=True, tracing=False) + crew = build_crew( + output_log_file=crew_log_path, verbose=True, tracing=False, + include_github=include_github, + ) logger.info("crew_runner: Crew verbose log → %s", crew_log_path) + # Stamp per-request output paths on this run's fresh task objects. + # build_tasks() guarantees interview is last and resume second-to-last. + crew.tasks[-1].output_file = interview_output + crew.tasks[-2].output_file = resume_output + _update_progress(job_id, "tailoring_resume") job_application_inputs = { @@ -106,11 +115,12 @@ def _run_crew_sync( "job_posting_url": job_url, "job_name": job_details.job_name, "company_name": job_details.company_name, - "github_url": github_url, "resume_path": resume_path, "job_details_json": job_details_json, - "bq_dataset_name": bq_dataset_name, } + if include_github: + job_application_inputs["github_url"] = github_url + job_application_inputs["bq_dataset_name"] = bq_dataset_name result = crew.kickoff(inputs=job_application_inputs) logger.info("crew_runner: Crew execution completed for job %d. Log saved to %s", job_id, crew_log_path) @@ -150,6 +160,9 @@ async def run_crew_for_job( """ Async entry point for crew execution. Updates job status in DB. Runs the synchronous crew pipeline in a thread pool executor. + + ``github_profile`` may be ``None`` when the user tailors a resume without a + GitHub profile; the crew then runs without the GitHub summarizer. """ _update_progress(job_id, "pending") @@ -166,13 +179,18 @@ async def run_crew_for_job( ) await db.commit() + # github_profile may be None when the user tailors without GitHub. + gh_username = github_profile.github_username if github_profile else None + gh_url = github_profile.github_url if github_profile else None + gh_dataset = github_profile.bq_dataset_name if github_profile else None + # Run crew in thread pool loop = asyncio.get_event_loop() result = await loop.run_in_executor( _executor, _run_crew_sync, job_id, user_id, user_email, - github_profile.github_username, github_profile.github_url, - github_profile.bq_dataset_name, resume_content, job_url, + gh_username, gh_url, gh_dataset, + resume_content, job_url, ) # Save extracted job details to DB on the correct async event loop diff --git a/backend/docs/migrations.md b/backend/docs/migrations.md new file mode 100644 index 0000000..42e46a4 --- /dev/null +++ b/backend/docs/migrations.md @@ -0,0 +1,41 @@ +# Database Migrations (manual) + +This project has **no Alembic**. Tables are auto-created on startup via +`Base.metadata.create_all` (`app/main.py`), which only creates **missing** +tables — it does **not** alter columns on tables that already exist. + +When a model change alters an existing column, you must apply the change by +hand against the running MySQL database (e.g. Cloud SQL). Fresh databases pick +up the new schema automatically via `create_all`. + +--- + +## 2026-06-18 — Make `jobs.github_profile_id` optional + +**Why:** A GitHub profile is now optional when tailoring a resume. The column +was `NOT NULL` with `ON DELETE RESTRICT`; it becomes nullable with +`ON DELETE SET NULL` so that (a) jobs can be created without a GitHub profile, +and (b) deleting a GitHub profile leaves historical jobs intact (link cleared). + +**Apply once to any existing (non-fresh) database:** + +```sql +-- 1) Find the existing foreign-key constraint name on the column. +SELECT CONSTRAINT_NAME + FROM information_schema.KEY_COLUMN_USAGE + WHERE TABLE_NAME = 'jobs' + AND COLUMN_NAME = 'github_profile_id' + AND REFERENCED_TABLE_NAME IS NOT NULL; + +-- 2) Drop the old RESTRICT FK, relax the column, re-add as SET NULL. +-- Replace with the value from step 1 (often jobs_ibfk_2). +ALTER TABLE jobs DROP FOREIGN KEY ; +ALTER TABLE jobs MODIFY COLUMN github_profile_id INT NULL; +ALTER TABLE jobs + ADD CONSTRAINT fk_jobs_github_profile + FOREIGN KEY (github_profile_id) REFERENCES github_profiles(id) + ON DELETE SET NULL; +``` + +**Fresh databases:** no action needed — `create_all` builds the `jobs` table +with the nullable column and `SET NULL` FK directly from the updated model. diff --git a/frontend/src/components/ProgressTracker.jsx b/frontend/src/components/ProgressTracker.jsx index 419a93a..72c39fd 100644 --- a/frontend/src/components/ProgressTracker.jsx +++ b/frontend/src/components/ProgressTracker.jsx @@ -9,10 +9,13 @@ const STEPS = [ { key: 'uploading_results', label: 'Uploading results' }, ]; -export default function ProgressTracker({ jobId, onComplete }) { +export default function ProgressTracker({ jobId, includeGithub = true, onComplete }) { const [currentStep, setCurrentStep] = useState('pending'); const [error, setError] = useState(null); + // Without GitHub, the backend never emits the 'searching_projects' step. + const steps = includeGithub ? STEPS : STEPS.filter((s) => s.key !== 'searching_projects'); + useEffect(() => { if (!jobId) return; @@ -36,7 +39,7 @@ export default function ProgressTracker({ jobId, onComplete }) { return () => eventSource.close(); }, [jobId, onComplete]); - const getStepIndex = () => STEPS.findIndex((s) => s.key === currentStep); + const getStepIndex = () => steps.findIndex((s) => s.key === currentStep); const activeIdx = getStepIndex(); return ( @@ -51,7 +54,7 @@ export default function ProgressTracker({ jobId, onComplete }) {
- {STEPS.map((step, idx) => { + {steps.map((step, idx) => { let status = 'pending'; if (currentStep === 'completed') status = 'done'; else if (currentStep === 'failed' && idx <= activeIdx) status = idx === activeIdx ? 'failed' : 'done'; diff --git a/frontend/src/components/ResumePreviewModal.jsx b/frontend/src/components/ResumePreviewModal.jsx index e0bcd15..6f82e40 100644 --- a/frontend/src/components/ResumePreviewModal.jsx +++ b/frontend/src/components/ResumePreviewModal.jsx @@ -1,4 +1,5 @@ import { useState, useEffect } from 'react'; +import { createPortal } from 'react-dom'; import ReactMarkdown from 'react-markdown'; import remarkGfm from 'remark-gfm'; import api from '../services/api'; @@ -33,7 +34,7 @@ export default function ResumePreviewModal({ jobId, resumeId, onClose }) { fetchContent(); }, [jobId, resumeId]); - return ( + return createPortal(
e.stopPropagation()}>
@@ -50,6 +51,7 @@ export default function ResumePreviewModal({ jobId, resumeId, onClose }) {
)}
-
+
, + document.body ); } diff --git a/frontend/src/components/ResumeUpload.jsx b/frontend/src/components/ResumeUpload.jsx index 0081908..5e8978a 100644 --- a/frontend/src/components/ResumeUpload.jsx +++ b/frontend/src/components/ResumeUpload.jsx @@ -1,11 +1,13 @@ import { useState, useRef } from 'react'; import api from '../services/api'; -import { FiUploadCloud, FiFile, FiTrash2, FiExternalLink } from 'react-icons/fi'; +import { FiUploadCloud, FiFile, FiTrash2, FiExternalLink, FiEye, FiDownload } from 'react-icons/fi'; +import ResumePreviewModal from './ResumePreviewModal'; export default function ResumeUpload({ resumes, onRefresh }) { const [uploading, setUploading] = useState(false); const [dragActive, setDragActive] = useState(false); const [error, setError] = useState(''); + const [previewId, setPreviewId] = useState(null); const fileInputRef = useRef(null); const handleUpload = async (file) => { @@ -39,6 +41,24 @@ export default function ResumeUpload({ resumes, onRefresh }) { } }; + const handleDownload = async (resume) => { + setError(''); + try { + const res = await api.get(`/resumes/${resume.id}/content`); + const blob = new Blob([res.data.content], { type: 'text/markdown' }); + const url = URL.createObjectURL(blob); + const a = document.createElement('a'); + a.href = url; + a.download = res.data.filename || resume.original_filename || 'resume.md'; + document.body.appendChild(a); + a.click(); + a.remove(); + URL.revokeObjectURL(url); + } catch (err) { + setError(err.response?.data?.detail || 'Download failed.'); + } + }; + const handleDrop = (e) => { e.preventDefault(); setDragActive(false); @@ -98,16 +118,29 @@ export default function ResumeUpload({ resumes, onRefresh }) { padding: 'var(--space-3) var(--space-4)', background: 'var(--bg-secondary)', borderRadius: 'var(--radius-sm)', fontSize: 'var(--font-size-sm)', }}> - - {r.original_filename} + + + {r.original_filename} + + + + + -
))}
)} + + {previewId && ( + setPreviewId(null)} /> + )}
); } diff --git a/frontend/src/components/TailorResumeForm.jsx b/frontend/src/components/TailorResumeForm.jsx index 0b0a5d9..32f4945 100644 --- a/frontend/src/components/TailorResumeForm.jsx +++ b/frontend/src/components/TailorResumeForm.jsx @@ -11,20 +11,21 @@ export default function TailorResumeForm({ githubProfiles, resumes, onJobCreated const handleSubmit = async (e) => { e.preventDefault(); - if (!jobUrl || !githubId || !resumeId) { - setError('All fields are required.'); + if (!jobUrl || !resumeId) { + setError('Job URL and resume are required.'); return; } setError(''); setLoading(true); try { - const res = await api.post('/jobs/tailor', { + const payload = { linkedin_job_url: jobUrl, - github_profile_id: parseInt(githubId), resume_id: parseInt(resumeId), - }); + }; + if (githubId) payload.github_profile_id = parseInt(githubId); + const res = await api.post('/jobs/tailor', payload); setJobUrl(''); - onJobCreated(res.data); + onJobCreated(res.data, !!githubId); } catch (err) { setError(err.response?.data?.detail || 'Failed to start tailoring.'); } finally { @@ -48,9 +49,9 @@ export default function TailorResumeForm({ githubProfiles, resumes, onJobCreated
- - setGithubId(e.target.value)}> + {githubProfiles.map((p) => ( ))} @@ -76,7 +77,7 @@ export default function TailorResumeForm({ githubProfiles, resumes, onJobCreated {error &&

{error}

} - diff --git a/frontend/src/components/TailoredResumeList.jsx b/frontend/src/components/TailoredResumeList.jsx index e988d03..2aee95d 100644 --- a/frontend/src/components/TailoredResumeList.jsx +++ b/frontend/src/components/TailoredResumeList.jsx @@ -1,9 +1,28 @@ +import { useState } from 'react'; import { useNavigate } from 'react-router-dom'; -import { FiEye, FiDownload, FiBookOpen } from 'react-icons/fi'; +import { FiEye, FiDownload, FiBookOpen, FiTrash2 } from 'react-icons/fi'; import api from '../services/api'; -export default function TailoredResumeList({ jobs, onPreviewResume }) { +export default function TailoredResumeList({ jobs, onPreviewResume, onRefresh }) { const navigate = useNavigate(); + const [deletingId, setDeletingId] = useState(null); + + const handleDelete = async (job) => { + const label = job.job_name + ? `"${job.job_name}"${job.company_name ? ` at ${job.company_name}` : ''}` + : 'this entry'; + if (!window.confirm(`Delete the tailored resume for ${label}? This cannot be undone.`)) return; + setDeletingId(job.id); + try { + await api.delete(`/jobs/${job.id}`); + onRefresh?.(); + } catch (err) { + console.error('Delete failed:', err); + alert(err.response?.data?.detail || 'Failed to delete. Please try again.'); + } finally { + setDeletingId(null); + } + }; const downloadFile = async (jobId, type) => { try { @@ -48,22 +67,36 @@ export default function TailoredResumeList({ jobs, onPreviewResume }) {
{job.status} - {job.status === 'completed' && ( -
- - + + + + + )} + {job.status !== 'pending' && job.status !== 'processing' && ( + - - -
- )} + )} + {job.status === 'failed' && job.error_message && (

{job.error_message} diff --git a/frontend/src/pages/HomePage.jsx b/frontend/src/pages/HomePage.jsx index 769c324..1670adf 100644 --- a/frontend/src/pages/HomePage.jsx +++ b/frontend/src/pages/HomePage.jsx @@ -17,6 +17,7 @@ export default function HomePage() { const [resumes, setResumes] = useState([]); const [githubProfiles, setGithubProfiles] = useState([]); const [activeJobId, setActiveJobId] = useState(null); + const [activeJobHasGithub, setActiveJobHasGithub] = useState(true); const [completedJob, setCompletedJob] = useState(null); const [previewJobId, setPreviewJobId] = useState(null); const [previewResumeId, setPreviewResumeId] = useState(null); @@ -32,8 +33,9 @@ export default function HomePage() { useEffect(() => { fetchData(); }, [fetchData]); - const handleJobCreated = (job) => { + const handleJobCreated = (job, hasGithub = true) => { setActiveJobId(job.id); + setActiveJobHasGithub(hasGithub); setJobs((prev) => [job, ...prev]); }; @@ -62,7 +64,7 @@ export default function HomePage() {

Previously Tailored Resumes

- setPreviewJobId(id)} /> + setPreviewJobId(id)} onRefresh={fetchData} />
@@ -87,7 +89,7 @@ export default function HomePage() { {activeJobId && (
- +
)} diff --git a/frontend/src/pages/LandingPage.jsx b/frontend/src/pages/LandingPage.jsx index 9956289..3cf6f63 100644 --- a/frontend/src/pages/LandingPage.jsx +++ b/frontend/src/pages/LandingPage.jsx @@ -90,7 +90,10 @@ export default function LandingPage() {

{STEPS.map((s, i) => ( -
+
+ + STEP {s.num} + {s.icon} - - STEP {s.num} -

{s.title}

{s.desc}

From 14d5fb6cbfdcd791474fa68491935500a164100d Mon Sep 17 00:00:00 2001 From: Arijit De Date: Fri, 19 Jun 2026 05:08:47 +0000 Subject: [PATCH 07/11] fix: update crewai-tools dependency to include optional mcp extras --- backend/pyproject.toml | 2 +- backend/uv.lock | 56 ++++++++++++++++++++++++++++++++++++++++-- 2 files changed, 55 insertions(+), 3 deletions(-) diff --git a/backend/pyproject.toml b/backend/pyproject.toml index 00c0ca9..130c70f 100644 --- a/backend/pyproject.toml +++ b/backend/pyproject.toml @@ -31,7 +31,7 @@ dependencies = [ "google-cloud-bigquery>=3.0.0", # ── LLM / Agent pipeline (CrewAI) ────────────────────────────────────── "crewai[anthropic]", - "crewai-tools", + "crewai-tools[mcp]", # ── RAG / embeddings ─────────────────────────────────────────────────── "langchain-community", "langchain-google-genai", diff --git a/backend/uv.lock b/backend/uv.lock index 16ca2de..7c51ca3 100644 --- a/backend/uv.lock +++ b/backend/uv.lock @@ -723,6 +723,13 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/7a/57/2ceb2cfec8845e3d9e358c441cb382f82c9b3b8b964384d665e290e565b0/crewai_tools-1.14.7-py3-none-any.whl", hash = "sha256:e6f1b0a36b40a43ba57c42519cf9320c59a937cfd1a5c110246ae0c7bc99d595", size = 809653, upload-time = "2026-06-11T17:14:49.845Z" }, ] +[package.optional-dependencies] +mcp = [ + { name = "mcp" }, + { name = "mcpadapt", version = "0.1.19", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, + { name = "mcpadapt", version = "0.1.20", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, +] + [[package]] name = "cryptography" version = "49.0.0" @@ -1791,7 +1798,7 @@ dependencies = [ { name = "asyncmy" }, { name = "beautifulsoup4" }, { name = "crewai", extra = ["anthropic"] }, - { name = "crewai-tools" }, + { name = "crewai-tools", extra = ["mcp"] }, { name = "fastapi" }, { name = "google-auth" }, { name = "google-cloud-bigquery" }, @@ -1829,7 +1836,7 @@ requires-dist = [ { name = "asyncmy", specifier = ">=0.2.9" }, { name = "beautifulsoup4" }, { name = "crewai", extras = ["anthropic"] }, - { name = "crewai-tools" }, + { name = "crewai-tools", extras = ["mcp"] }, { name = "fastapi", specifier = ">=0.115.0,<0.116" }, { name = "google-auth", specifier = ">=2.25.0" }, { name = "google-cloud-bigquery", specifier = ">=3.0.0" }, @@ -2461,6 +2468,51 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/fd/d9/eaa1f80170d2b7c5ba23f3b59f766f3a0bb41155fbc32a69adfa1adaaef9/mcp-1.26.0-py3-none-any.whl", hash = "sha256:904a21c33c25aa98ddbeb47273033c435e595bbacfdb177f4bd87f6dceebe1ca", size = 233615, upload-time = "2026-01-24T19:40:30.652Z" }, ] +[package.optional-dependencies] +ws = [ + { name = "websockets" }, +] + +[[package]] +name = "mcpadapt" +version = "0.1.19" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version < '3.12'", +] +dependencies = [ + { name = "jsonref", marker = 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"https://files.pythonhosted.org/packages/ba/d8/5b6c8cf2070d765904fcb9066f8d7956cb9d399807d86c7fb7f7503b80bf/mcpadapt-0.1.20-py3-none-any.whl", hash = "sha256:117a661eb536dfb0b2a73e5730c2f5ad4e611263e014fb1cebaaff9e78a18f78", size = 19481, upload-time = "2025-10-24T15:35:00.159Z" }, +] + [[package]] name = "mdit-py-plugins" version = "0.6.1" From fa9d6c773dc18c512f81f08b1ab60fd52fee42d6 Mon Sep 17 00:00:00 2001 From: Arijit De Date: Fri, 19 Jun 2026 14:38:12 +0000 Subject: [PATCH 08/11] feat: add retry endpoint for jobs, update frontend to support retry functionality --- backend/app/api/v1/jobs.py | 62 ++++++++++++++++++- backend/app/cli.py | 8 +++ backend/app/main.py | 28 ++++++++- backend/app/schemas/__init__.py | 1 + .../src/components/TailoredResumeList.jsx | 30 ++++++++- frontend/src/pages/HomePage.jsx | 8 ++- 6 files changed, 132 insertions(+), 5 deletions(-) diff --git a/backend/app/api/v1/jobs.py b/backend/app/api/v1/jobs.py index 86b4c73..31b988d 100644 --- a/backend/app/api/v1/jobs.py +++ b/backend/app/api/v1/jobs.py @@ -8,7 +8,7 @@ import asyncio from fastapi import APIRouter, Depends, HTTPException, status from fastapi.responses import StreamingResponse, Response -from sqlalchemy import select +from sqlalchemy import select, update from sqlalchemy.ext.asyncio import AsyncSession from app.api.deps import get_db @@ -128,6 +128,66 @@ async def delete_job( return Response(status_code=status.HTTP_204_NO_CONTENT) +@router.post("/{job_id}/retry", response_model=JobResponse) +async def retry_job( + job_id: int, + current_user: User = Depends(get_current_user), + db: AsyncSession = Depends(get_db), +): + """Re-run a failed or stuck job: reset to pending, clear error, relaunch crew.""" + result = await db.execute( + select(Job).where(Job.id == job_id, Job.user_id == current_user.id) + ) + job = result.scalar_one_or_none() + if not job: + raise HTTPException(status_code=404, detail="Job not found.") + if job.status == "completed": + raise HTTPException(status_code=409, detail="Completed jobs cannot be retried.") + if job.status == "processing": + raise HTTPException(status_code=409, detail="Job is already running.") + + # Re-fetch GitHub profile (optional; FK is SET NULL so it may be None already). + github_profile = None + if job.github_profile_id is not None: + gh_result = await db.execute( + select(GithubProfile).where( + GithubProfile.id == job.github_profile_id, + GithubProfile.user_id == current_user.id, + ) + ) + github_profile = gh_result.scalar_one_or_none() # gone → run without GitHub + + # Resume must still exist. + resume_result = await db.execute( + select(Resume).where(Resume.id == job.resume_id, Resume.user_id == current_user.id) + ) + resume = resume_result.scalar_one_or_none() + if not resume: + raise HTTPException( + status_code=409, + detail="The resume for this job no longer exists. Cannot retry.", + ) + + await db.execute( + update(Job).where(Job.id == job_id).values(status="pending", error_message=None) + ) + await db.commit() + await db.refresh(job) + + # Pre-seed progress so a re-attaching SSE stream doesn't see the stale + # terminal 'failed' step and close instantly. + progress_store[job_id] = {"current_step": "pending", "error": None} + + asyncio.create_task( + run_crew_for_job( + job_id=job.id, user_id=current_user.id, user_email=current_user.email, + github_profile=github_profile, resume_gcs_path=resume.gcs_path, + job_url=job.linkedin_job_url, + ) + ) + return job + + @router.get("/{job_id}/progress") async def stream_job_progress( job_id: int, current_user: User = Depends(get_current_user), diff --git a/backend/app/cli.py b/backend/app/cli.py index 3786d0e..7ab8202 100644 --- a/backend/app/cli.py +++ b/backend/app/cli.py @@ -106,11 +106,15 @@ def api() -> None: """Start the FastAPI app with autoreload (development).""" import uvicorn + # uvicorn excludes whole directory trees only when given absolute paths + # (it matches them against each changed file's absolute parents). Relative + # globs like ".venv/*" do NOT work. "*.log" still matches by filename. uvicorn.run( "app.main:app", host=DEFAULT_API_HOST, port=int(DEFAULT_API_PORT), reload=True, + reload_excludes=[str(BACKEND_DIR / ".venv"), str(BACKEND_DIR / "logs"), "*.log"], ) @@ -225,6 +229,10 @@ def dev() -> None: [ sys.executable, "-m", "uvicorn", "app.main:app", "--host", DEFAULT_API_HOST, "--port", DEFAULT_API_PORT, "--reload", + # Absolute dir paths so uvicorn excludes the whole tree (see api()). + "--reload-exclude", str(BACKEND_DIR / ".venv"), + "--reload-exclude", str(BACKEND_DIR / "logs"), + "--reload-exclude", "*.log", ], BACKEND_DIR, ) diff --git a/backend/app/main.py b/backend/app/main.py index 085a75e..0801ddc 100644 --- a/backend/app/main.py +++ b/backend/app/main.py @@ -5,22 +5,48 @@ """ from contextlib import asynccontextmanager +from datetime import datetime, timedelta from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware +from sqlalchemy import update from app.core.config import get_settings from app.core.database import engine, Base +from app.core.logging import get_logger from app.api.v1 import auth, github, resumes, jobs settings = get_settings() +logger = get_logger(__name__) @asynccontextmanager async def lifespan(app: FastAPI): - """Create database tables on startup and dispose the engine on shutdown.""" + """Create tables; best-effort fail jobs stuck >3h; dispose engine on shutdown.""" async with engine.begin() as conn: await conn.run_sync(Base.metadata.create_all) + + # Best-effort: fail jobs left pending/processing for >3h (orphaned by a + # prior restart). Reuses THIS connection — no new session/checkout — and + # swallows all errors so it can never abort startup or kill the proxy. + try: + from app.models import Job + cutoff = datetime.utcnow() - timedelta(hours=3) + await conn.execute( + update(Job) + .where( + Job.status.in_(("pending", "processing")), + Job.created_at < cutoff, + ) + .values( + status="failed", + error_message="Job was stuck in progress for over 3 hours " + "(likely a server restart). Please retry.", + ) + ) + except Exception as exc: # noqa: BLE001 — never let cleanup break startup + logger.warning("Startup stuck-job cleanup skipped: %s", exc) + yield await engine.dispose() diff --git a/backend/app/schemas/__init__.py b/backend/app/schemas/__init__.py index ec8eece..0654a88 100644 --- a/backend/app/schemas/__init__.py +++ b/backend/app/schemas/__init__.py @@ -79,6 +79,7 @@ class TailorJobRequest(BaseModel): class JobResponse(BaseModel): id: int + github_profile_id: Optional[int] = None linkedin_job_url: str job_name: Optional[str] = None company_name: Optional[str] = None diff --git a/frontend/src/components/TailoredResumeList.jsx b/frontend/src/components/TailoredResumeList.jsx index 2aee95d..6252958 100644 --- a/frontend/src/components/TailoredResumeList.jsx +++ b/frontend/src/components/TailoredResumeList.jsx @@ -1,11 +1,25 @@ import { useState } from 'react'; import { useNavigate } from 'react-router-dom'; -import { FiEye, FiDownload, FiBookOpen, FiTrash2 } from 'react-icons/fi'; +import { FiEye, FiDownload, FiBookOpen, FiTrash2, FiRefreshCw } from 'react-icons/fi'; import api from '../services/api'; -export default function TailoredResumeList({ jobs, onPreviewResume, onRefresh }) { +export default function TailoredResumeList({ jobs, onPreviewResume, onRefresh, onRetry }) { const navigate = useNavigate(); const [deletingId, setDeletingId] = useState(null); + const [retryingId, setRetryingId] = useState(null); + + const handleRetry = async (job) => { + setRetryingId(job.id); + try { + const res = await api.post(`/jobs/${job.id}/retry`); + onRetry?.(res.data); + } catch (err) { + console.error('Retry failed:', err); + alert(err.response?.data?.detail || 'Failed to retry. Please try again.'); + } finally { + setRetryingId(null); + } + }; const handleDelete = async (job) => { const label = job.job_name @@ -84,6 +98,18 @@ export default function TailoredResumeList({ jobs, onPreviewResume, onRefresh }) )} + {(job.status === 'failed' || job.status === 'pending') && ( + + )} {job.status !== 'pending' && job.status !== 'processing' && (
From 985ab62f278ac0529f72a4ab09f31100be08f65a Mon Sep 17 00:00:00 2001 From: Arijit De Date: Thu, 25 Jun 2026 13:02:22 +0000 Subject: [PATCH 09/11] feat: add polling mechanism for in-flight jobs to refresh status automatically --- frontend/src/pages/HomePage.jsx | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/frontend/src/pages/HomePage.jsx b/frontend/src/pages/HomePage.jsx index 3c873d3..1d2dde6 100644 --- a/frontend/src/pages/HomePage.jsx +++ b/frontend/src/pages/HomePage.jsx @@ -33,6 +33,19 @@ export default function HomePage() { useEffect(() => { fetchData(); }, [fetchData]); + // Jobs that were in-flight before this page mounted (e.g. after a reload or + // from a previous session) have no SSE tracker attached, so the list would + // sit at PENDING/PROCESSING until a manual refresh. Poll while any job is in + // a non-terminal state; stop once everything has settled. + useEffect(() => { + const hasInFlight = jobs.some( + (job) => job.status === 'pending' || job.status === 'processing' + ); + if (!hasInFlight) return; + const id = setInterval(fetchData, 5000); + return () => clearInterval(id); + }, [jobs, fetchData]); + const handleJobCreated = (job, hasGithub = true) => { setActiveJobId(job.id); setActiveJobHasGithub(hasGithub); From 602167f67fb0a866b568b5f5bab63d2c328f74de Mon Sep 17 00:00:00 2001 From: Arijit De Date: Mon, 6 Jul 2026 06:01:19 +0000 Subject: [PATCH 10/11] Changed the file logging level to INFO --- backend/app/core/logging.py | 2 +- backend/app/services/crew/agents.py | 9 +++++---- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/backend/app/core/logging.py b/backend/app/core/logging.py index 20f849d..0995cc9 100644 --- a/backend/app/core/logging.py +++ b/backend/app/core/logging.py @@ -79,7 +79,7 @@ def _setup_root_logger() -> None: backupCount=BACKUP_COUNT, encoding="utf-8", ) - file_handler.setLevel(logging.DEBUG) + file_handler.setLevel(getattr(logging, LOG_LEVEL_ENV, logging.INFO)) file_handler.setFormatter(file_fmt) # ── Console handler ─────────────────────────────────────────────────────── diff --git a/backend/app/services/crew/agents.py b/backend/app/services/crew/agents.py index acfe032..972360b 100644 --- a/backend/app/services/crew/agents.py +++ b/backend/app/services/crew/agents.py @@ -148,10 +148,11 @@ def repo_content_searcher(query: str, job_description: str = None, top_k: int = } for content, metadata in results ] -# Agent 1: GitHub Project Summarizer # NOTE: The Researcher agent has been removed. Job extraction is done # directly in Job_Applier.py via extract_linkedin_job_details() and the # structured JobDetails are passed into every task via crew input variables. + +# Agent 1: GitHub Project Summarizer github_project_summarizer = Agent( role="GitHub Project Summarizer", goal="Summarize the user's most relevant GitHub projects for a job application, highlighting tech stacks, languages, frameworks, tools, and cloud technologies used.", @@ -170,7 +171,7 @@ def repo_content_searcher(query: str, job_description: str = None, top_k: int = ) -# Agent 3: Profiler +# Agent 2: Profiler profiler = Agent( role="Personal Profiler for Engineers", goal="Do incredible analytical research on job applicants to help them stand out in the job market", @@ -187,7 +188,7 @@ def repo_content_searcher(query: str, job_description: str = None, top_k: int = ) ) -# Agent 4: Resume Strategist +# Agent 3: Resume Strategist resume_strategist = Agent( role="Resume Strategist for Engineers", goal="Find all the best ways to make a resume stand out in the job market.", @@ -205,7 +206,7 @@ def repo_content_searcher(query: str, job_description: str = None, top_k: int = ) ) -# Agent 5: Interview Preparer +# Agent 4: Interview Preparer interview_preparer = Agent( role="Engineering Interview Preparer", goal="Create interview questions and talking points " From 562faa640a6da7da996ecc25ebc6fa89698d877a Mon Sep 17 00:00:00 2001 From: Arijit De Date: Sat, 11 Jul 2026 12:56:30 +0000 Subject: [PATCH 11/11] feat: add resume_analyzer CrewAI agent with structured parsing Adds a resume_analyzer agent that extracts .pdf/.docx/.md resumes into a fixed JSON schema, uploads to GCS, and records parsed_resume_path on the Resume table. Runs in parallel with the GitHub task; output feeds the profiler. Includes hallucination + URL guardrails, computed years_of_experience, category-keyed skills, PDF/DOCX upload support, and an isolation test script. --- .gitignore | 4 +- backend/app/api/v1/jobs.py | 6 + backend/app/api/v1/resumes.py | 19 +- backend/app/core/database.py | 23 + backend/app/main.py | 17 + backend/app/models/__init__.py | 6 +- backend/app/schemas/__init__.py | 1 + backend/app/services/crew/agents.py | 47 +- backend/app/services/crew/crew.py | 15 +- backend/app/services/crew/resume_tools.py | 357 ++++++++++++++ backend/app/services/crew/tasks.py | 167 ++++++- backend/app/services/crew_runner.py | 67 ++- backend/app/services/gcs_service.py | 40 +- backend/app/services/resume_parser.py | 454 ++++++++++++++++++ .../resume_analyzer_agent_creation_prompt.txt | 370 ++++++++++++++ .../skill_extractor_agent_creation_prompt.txt | 173 +++++++ backend/pyproject.toml | 1 + .../sample_data/Arijit De Resume 2026.docx | Bin 0 -> 465216 bytes backend/sample_data/Arijit De Resume 2026.md | 55 +++ backend/sample_data/Arijit De Resume 2026.pdf | Bin 0 -> 106237 bytes backend/sample_data/Arijit_De_Resume.md | 80 --- backend/sample_data/Yash_Pal_Resume.pdf | Bin 0 -> 173386 bytes .../sample_data/Yash_Raj_Singh_Oct_Resume.pdf | Bin 0 -> 181452 bytes backend/sample_data/interview_materials.md | 112 ----- backend/tests/run_resume_analyzer_test.py | 177 +++++++ backend/uv.lock | 237 ++++++--- frontend/src/components/ProgressTracker.jsx | 1 + frontend/src/components/ResumeUpload.jsx | 45 +- 28 files changed, 2174 insertions(+), 300 deletions(-) create mode 100644 backend/app/services/crew/resume_tools.py create mode 100644 backend/app/services/resume_parser.py create mode 100644 backend/docs/resume_analyzer_agent_creation_prompt.txt create mode 100644 backend/docs/skill_extractor_agent_creation_prompt.txt create mode 100644 backend/sample_data/Arijit De Resume 2026.docx create mode 100644 backend/sample_data/Arijit De Resume 2026.md create mode 100644 backend/sample_data/Arijit De Resume 2026.pdf delete mode 100644 backend/sample_data/Arijit_De_Resume.md create mode 100644 backend/sample_data/Yash_Pal_Resume.pdf create mode 100644 backend/sample_data/Yash_Raj_Singh_Oct_Resume.pdf delete mode 100644 backend/sample_data/interview_materials.md create mode 100644 backend/tests/run_resume_analyzer_test.py diff --git a/.gitignore b/.gitignore index 1701bca..109e226 100644 --- a/.gitignore +++ b/.gitignore @@ -181,4 +181,6 @@ logs/ client_secret_*.json *-service-account*.json project-*.json -backend/credentials/ \ No newline at end of file +backend/credentials/ +**:Zone.Identifier +backend/tests/output/ \ No newline at end of file diff --git a/backend/app/api/v1/jobs.py b/backend/app/api/v1/jobs.py index 31b988d..e93a724 100644 --- a/backend/app/api/v1/jobs.py +++ b/backend/app/api/v1/jobs.py @@ -67,6 +67,9 @@ async def tailor_resume( job_id=job.id, user_id=current_user.id, user_email=current_user.email, github_profile=github_profile, resume_gcs_path=resume.gcs_path, job_url=req.linkedin_job_url, + resume_id=resume.id, + user_name=f"{current_user.first_name} {current_user.last_name}", + resume_filename=resume.original_filename, ) ) return job @@ -183,6 +186,9 @@ async def retry_job( job_id=job.id, user_id=current_user.id, user_email=current_user.email, github_profile=github_profile, resume_gcs_path=resume.gcs_path, job_url=job.linkedin_job_url, + resume_id=resume.id, + user_name=f"{current_user.first_name} {current_user.last_name}", + resume_filename=resume.original_filename, ) ) return job diff --git a/backend/app/api/v1/resumes.py b/backend/app/api/v1/resumes.py index c6a0616..0f022f5 100644 --- a/backend/app/api/v1/resumes.py +++ b/backend/app/api/v1/resumes.py @@ -4,6 +4,8 @@ Endpoints for uploading, listing, previewing, and deleting resumes. """ +from pathlib import Path + from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, status from sqlalchemy import select from sqlalchemy.ext.asyncio import AsyncSession @@ -18,6 +20,8 @@ settings = get_settings() router = APIRouter(prefix="/api/resumes", tags=["resumes"]) +ALLOWED_RESUME_EXTENSIONS = {".md", ".pdf", ".docx"} + @router.post("/upload", response_model=ResumeResponse, status_code=status.HTTP_201_CREATED) async def upload_resume_file( @@ -25,12 +29,12 @@ async def upload_resume_file( current_user: User = Depends(get_current_user), db: AsyncSession = Depends(get_db), ): - """Upload a .md resume file to GCS.""" + """Upload a resume file (.md, .pdf or .docx) to GCS.""" # Validate file extension - if not file.filename or not file.filename.endswith(".md"): + if not file.filename or Path(file.filename).suffix.lower() not in ALLOWED_RESUME_EXTENSIONS: raise HTTPException( status_code=status.HTTP_400_BAD_REQUEST, - detail="Only Markdown (.md) files are accepted.", + detail="Only Markdown (.md), PDF (.pdf) or Word (.docx) files are accepted.", ) # Read file content @@ -120,6 +124,15 @@ async def get_resume_content( if not resume: raise HTTPException(status_code=404, detail="Resume not found.") + # Binary resumes (.pdf/.docx) cannot be rendered as markdown text — + # the /preview signed-URL endpoint handles those. + if Path(resume.original_filename or "").suffix.lower() != ".md": + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail="In-browser text preview is only available for Markdown resumes. " + "Use the preview endpoint for PDF/DOCX files.", + ) + try: content_bytes = download_file(resume.gcs_path) return {"content": content_bytes.decode("utf-8"), "filename": resume.original_filename} diff --git a/backend/app/core/database.py b/backend/app/core/database.py index a80cade..6b4d1f7 100644 --- a/backend/app/core/database.py +++ b/backend/app/core/database.py @@ -5,6 +5,8 @@ Uses asyncmy driver for Cloud SQL MySQL connectivity. """ +from sqlalchemy import create_engine +from sqlalchemy.engine import Engine from sqlalchemy.ext.asyncio import create_async_engine, async_sessionmaker, AsyncSession from sqlalchemy.orm import DeclarativeBase @@ -42,6 +44,27 @@ def _patched_ping(self, reconnect: bool = False) -> None: ) +# ── Sync engine (lazy) ──────────────────────────────────────────────────────── +# The async engine's pooled connections are bound to the main event loop, so +# synchronous code running in worker threads (e.g. CrewAI tools inside the +# crew_runner's ThreadPoolExecutor) must not use it. They get a small pymysql +# engine instead, created on first use. +_sync_engine: Engine | None = None + + +def get_sync_engine() -> Engine: + """Return the lazily-created synchronous engine (pymysql driver).""" + global _sync_engine + if _sync_engine is None: + _sync_engine = create_engine( + settings.mysql_url_sync, + pool_pre_ping=True, + pool_size=2, + max_overflow=2, + ) + return _sync_engine + + # ── Declarative Base ────────────────────────────────────────────────────────── class Base(DeclarativeBase): pass diff --git a/backend/app/main.py b/backend/app/main.py index 0801ddc..8094bf7 100644 --- a/backend/app/main.py +++ b/backend/app/main.py @@ -26,6 +26,23 @@ async def lifespan(app: FastAPI): async with engine.begin() as conn: await conn.run_sync(Base.metadata.create_all) + # Lightweight migration: create_all only creates missing tables, it + # never alters existing ones. Add resumes.parsed_resume_path (written + # by the resume_analyzer agent) if the column is missing. Idempotent + # and best-effort, same policy as the cleanup below. + try: + def _add_parsed_resume_column(sync_conn): + from sqlalchemy import inspect, text + columns = {c["name"] for c in inspect(sync_conn).get_columns("resumes")} + if "parsed_resume_path" not in columns: + sync_conn.execute(text( + "ALTER TABLE resumes ADD COLUMN parsed_resume_path VARCHAR(500) NULL" + )) + logger.info("Added resumes.parsed_resume_path column") + await conn.run_sync(_add_parsed_resume_column) + except Exception as exc: # noqa: BLE001 — never let migration break startup + logger.warning("Startup parsed_resume_path migration skipped: %s", exc) + # Best-effort: fail jobs left pending/processing for >3h (orphaned by a # prior restart). Reuses THIS connection — no new session/checkout — and # swallows all errors so it can never abort startup or kill the proxy. diff --git a/backend/app/models/__init__.py b/backend/app/models/__init__.py index 31ec2cb..81a1498 100644 --- a/backend/app/models/__init__.py +++ b/backend/app/models/__init__.py @@ -64,7 +64,11 @@ class Resume(Base): id = Column(Integer, primary_key=True, autoincrement=True) user_id = Column(Integer, ForeignKey("users.id", ondelete="CASCADE"), nullable=False) original_filename = Column(String(255), nullable=False) - gcs_path = Column(String(500), nullable=False) # gs://bucket/uploads//.md + gcs_path = Column(String(500), nullable=False) # gs://bucket/uploads// + # gs://bucket/parsed//___parsed_resume.json + # Set by the resume_analyzer agent only after the JSON is written and + # uploaded to GCS successfully. + parsed_resume_path = Column(String(500), nullable=True) uploaded_at = Column(DateTime, default=datetime.utcnow) # Relationships diff --git a/backend/app/schemas/__init__.py b/backend/app/schemas/__init__.py index 0654a88..5066889 100644 --- a/backend/app/schemas/__init__.py +++ b/backend/app/schemas/__init__.py @@ -64,6 +64,7 @@ class ResumeResponse(BaseModel): id: int original_filename: str gcs_path: str + parsed_resume_path: Optional[str] = None uploaded_at: datetime model_config = {"from_attributes": True} diff --git a/backend/app/services/crew/agents.py b/backend/app/services/crew/agents.py index 972360b..75b3e72 100644 --- a/backend/app/services/crew/agents.py +++ b/backend/app/services/crew/agents.py @@ -19,7 +19,7 @@ load_dotenv() logger = get_logger(__name__) -# LLM configuration — Claude Sonnet 4 +# LLM configuration — Gemini gemini_llm = LLM( model="gemini/gemini-2.5-flash", api_key=os.environ.get("GEMINI_API_KEY"), @@ -27,6 +27,15 @@ max_tokens=8000 ) +# Low-temperature variant for structured extraction (resume_analyzer): the +# parsed-resume JSON must be reproduced verbatim and can exceed 8k tokens. +gemini_llm_extraction = LLM( + model="gemini/gemini-2.5-flash", + api_key=os.environ.get("GEMINI_API_KEY"), + temperature=0.1, + max_tokens=16000 +) + search_tool = SerperDevTool() scrape_tool = ScrapeWebsiteTool() read_resume = FileReadTool() @@ -171,7 +180,37 @@ def repo_content_searcher(query: str, job_description: str = None, top_k: int = ) -# Agent 2: Profiler +# Agent 2: Resume Analyzer +# No agent-level tools: the per-run save_parsed_resume tool (closured over +# user/resume IDs) is attached at the Task level by build_tasks(), which keeps +# this module-level singleton thread-safe across concurrent crew runs. +resume_analyzer = Agent( + role="Resume Analyzer", + goal=( + "Carefully scan a resume and extract every piece of relevant applicant " + "information into a strictly valid JSON document matching the required schema." + ), + llm=gemini_llm_extraction, + verbose=True, + max_iter=8, + max_rpm=10, + respect_context_window=True, + backstory=( + "You are a meticulous structured-data extraction specialist for resumes. " + "You read every line of a resume and map each fact to the correct field of " + "a fixed JSON schema. You never invent, guess, or embellish information: " + "every value you output must be traceable to explicit text in the resume, " + "and anything the resume does not state is left as an empty string, null, an " + "empty list, or false, exactly as the schema prescribes. When the resume " + "groups skills under category headings you preserve those headings verbatim; " + "when it does not, you place the skills under a single \"Default\" category. " + "You always produce strictly valid JSON — no comments, no trailing commas, " + "no markdown fences — and you always persist your work with the " + "save_parsed_resume tool before finishing." + ) +) + +# Agent 3: Profiler profiler = Agent( role="Personal Profiler for Engineers", goal="Do incredible analytical research on job applicants to help them stand out in the job market", @@ -188,7 +227,7 @@ def repo_content_searcher(query: str, job_description: str = None, top_k: int = ) ) -# Agent 3: Resume Strategist +# Agent 4: Resume Strategist resume_strategist = Agent( role="Resume Strategist for Engineers", goal="Find all the best ways to make a resume stand out in the job market.", @@ -206,7 +245,7 @@ def repo_content_searcher(query: str, job_description: str = None, top_k: int = ) ) -# Agent 4: Interview Preparer +# Agent 5: Interview Preparer interview_preparer = Agent( role="Engineering Interview Preparer", goal="Create interview questions and talking points " diff --git a/backend/app/services/crew/crew.py b/backend/app/services/crew/crew.py index b0624c4..3f9764a 100644 --- a/backend/app/services/crew/crew.py +++ b/backend/app/services/crew/crew.py @@ -18,10 +18,12 @@ from app.core.logging import get_logger from app.services.crew.agents import ( github_project_summarizer, + resume_analyzer, profiler, resume_strategist, interview_preparer, ) +from app.services.crew.resume_tools import ResumeAnalysisContext from app.services.crew.tasks import build_tasks logger = get_logger(__name__) @@ -32,6 +34,7 @@ def build_crew( verbose: bool = True, tracing: bool = True, include_github: bool = True, + resume_ctx: ResumeAnalysisContext | None = None, ) -> Crew: """ Construct the job-application :class:`crewai.Crew`. @@ -39,6 +42,11 @@ def build_crew( Job details are extracted once by the caller and injected into every task via the ``job_details_json`` crew input variable, so no researcher agent is needed. + The resume analysis task and the GitHub summary task run in parallel + (``async_execution=True``); the profile task waits for both via its + ``context``. If resume analysis fails, ``kickoff()`` raises before any + downstream task runs. + Args: output_log_file: Path for CrewAI's verbose execution log. verbose: Enable verbose agent/task logging. @@ -46,15 +54,18 @@ def build_crew( include_github: When True, include the GitHub summarizer agent/task and require the ``github_url`` / ``bq_dataset_name`` crew inputs. When False, skip GitHub entirely and tailor from the resume + job details. + resume_ctx: Identity/state context for the resume analysis task's save + tool. When None (standalone CLI), the parsed resume JSON is written + to temp storage only — no GCS upload, no database write. Returns: A configured :class:`crewai.Crew` ready for ``kickoff(inputs=...)``. The task list order is stable: the interview task is always last and the resume task always second-to-last (``crew.tasks[-1]`` / ``[-2]``). """ - tasks = build_tasks(include_github=include_github) + tasks = build_tasks(include_github=include_github, resume_ctx=resume_ctx) - agents = [profiler, resume_strategist, interview_preparer] + agents = [resume_analyzer, profiler, resume_strategist, interview_preparer] if include_github: agents.insert(0, github_project_summarizer) diff --git a/backend/app/services/crew/resume_tools.py b/backend/app/services/crew/resume_tools.py new file mode 100644 index 0000000..d6cefcc --- /dev/null +++ b/backend/app/services/crew/resume_tools.py @@ -0,0 +1,357 @@ +""" +app.services.crew.resume_tools +------------------------------ +Per-run tool and guardrail factories for the resume_analyzer agent. + +The save tool is built fresh for every crew run (like the Task objects in +``app.services.crew.tasks``) so that user_id / resume_id / user_name are +closured into the tool rather than supplied by the LLM, and so a shared +``ResumeAnalysisContext.state`` dict can carry the outcome to the task +guardrail. The tool enforces the required ordering in code: + + validate JSON → write temp file → upload to GCS → update database + +The database is only touched after the file write and the GCS upload have +both succeeded. With ``local_only=True`` (isolation test script, CLI) the +tool stops after the temp-file write — no GCS, no database. + +CrewAI converts tool exceptions into error messages fed back to the LLM, so +this tool never relies on raising: every failure returns an "ERROR: ..." +string the agent can react to, and the task guardrail aborts the crew if no +successful save happened after the retry budget. +""" + +import json +import os +import tempfile +from dataclasses import dataclass, field +from typing import Any, Callable, Dict + +from crewai import TaskOutput +from crewai.tools import tool +from pydantic import ValidationError + +from app.core.logging import get_logger +from app.services.resume_parser import ( + ParsedResume, + build_parsed_resume_filename, + compute_years_of_experience, + find_invalid_url_fields, +) + +logger = get_logger(__name__) + + +@dataclass +class ResumeAnalysisContext: + """Per-run identity and shared state for the resume-analysis task.""" + + user_id: int + resume_id: int + user_name: str + local_only: bool = False + # The extracted resume text, used as the reference context for the + # hallucination guardrail so the agent cannot invent skills/positions/etc. + resume_text: str = "" + state: Dict[str, Any] = field(default_factory=dict) + + +def _strip_code_fences(text: str) -> str: + """Remove a surrounding ```json ... ``` / ``` ... ``` fence if present.""" + stripped = text.strip() + if stripped.startswith("```"): + first_newline = stripped.find("\n") + if first_newline != -1: + stripped = stripped[first_newline + 1:] + if stripped.rstrip().endswith("```"): + stripped = stripped.rstrip()[:-3] + return stripped.strip() + + +def make_save_parsed_resume_tool(ctx: ResumeAnalysisContext): + """Build the save_parsed_resume tool bound to one run's ``ctx``.""" + + @tool("save_parsed_resume") + def save_parsed_resume(parsed_resume_json: str) -> str: + """ + Persist the parsed resume JSON. Validates the JSON against the required + schema, writes it to a local file, uploads it to cloud storage, and + records the storage path in the database. Call this exactly once with + the COMPLETE parsed resume JSON string (the full {"id": ..., "result": + {...}} document). If the response starts with "ERROR:", fix the + reported problem and call the tool again with the corrected JSON. + + Args: + parsed_resume_json (str): The complete parsed resume JSON string. + + Returns: + str: "SUCCESS: ..." when everything was persisted, otherwise + "ERROR: ". + """ + raw = _strip_code_fences(parsed_resume_json) + + # 1. Validate against the required schema. + try: + parsed = ParsedResume.model_validate_json(raw) + except ValidationError as e: + logger.warning( + "resume_tools: invalid parsed-resume JSON for resume %d: %s", + ctx.resume_id, e, + ) + return ( + "ERROR: The JSON is invalid or does not match the required schema: " + f"{e}. Fix the JSON and call save_parsed_resume again with the " + "complete corrected document." + ) + + # 2. The id is not the LLM's to choose. + parsed.id = ctx.resume_id + + # 2a. Reject invalid URL fields so the agent re-extracts (or blanks them). + invalid_urls = find_invalid_url_fields(parsed.result) + if invalid_urls: + logger.warning( + "resume_tools: invalid URL field(s) %s for resume %d", + invalid_urls, ctx.resume_id, + ) + details = ", ".join( + f"{field}={getattr(parsed.result, field)!r}" for field in invalid_urls + ) + return ( + f"ERROR: These URL fields are not valid URLs: {details}. Every " + "*_url field must be a complete http(s) URL (e.g. " + "'https://www.linkedin.com/in/username') or an empty string \"\". " + "If the resume only shows the link's label/anchor text (like " + "'LinkedIn', 'Portfolio', 'Kaggle', or a name) and not the actual " + "URL, set that field to \"\". Fix these and call save_parsed_resume " + "again." + ) + + # 2b. Compute years_of_experience from position dates when the agent did + # not extract a value from the summary/objective (left it at 0). + if not parsed.result.years_of_experience: + computed = compute_years_of_experience(parsed.result) + if computed: + logger.info( + "resume_tools: computed years_of_experience=%d from positions " + "for resume %d", computed, ctx.resume_id, + ) + parsed.result.years_of_experience = computed + + normalized = parsed.model_dump_json(indent=2) + + # 3. Write to local temp storage. + filename = build_parsed_resume_filename(ctx.user_name, ctx.user_id, ctx.resume_id) + local_path = os.path.join(tempfile.gettempdir(), filename) + try: + with open(local_path, "w", encoding="utf-8") as f: + f.write(normalized) + except OSError as e: + logger.error( + "resume_tools: failed to write parsed resume to '%s': %s", + local_path, e, exc_info=True, + ) + return f"ERROR: Could not write the parsed resume file locally: {e}." + logger.info( + "resume_tools: parsed resume written to '%s' (%d bytes)", + local_path, len(normalized.encode("utf-8")), + ) + + if ctx.local_only: + ctx.state.update({"saved": True, "local_path": local_path, "json": normalized}) + logger.info( + "resume_tools: local_only run — skipping GCS upload and DB update " + "for resume %d", ctx.resume_id, + ) + return f"SUCCESS: Parsed resume saved locally to {local_path}." + + # 4. Upload to GCS. The database is not touched unless this succeeds. + try: + from app.services.gcs_service import upload_parsed_resume + + gcs_path = upload_parsed_resume(ctx.user_id, filename, normalized) + except Exception as e: # noqa: BLE001 — surface any GCS failure to the agent + logger.error( + "resume_tools: GCS upload of parsed resume for resume %d failed: %s", + ctx.resume_id, e, exc_info=True, + ) + return ( + "ERROR: The parsed resume file was created but the upload to cloud " + f"storage failed: {e}. Call save_parsed_resume again to retry." + ) + logger.info("resume_tools: parsed resume uploaded to %s", gcs_path) + + # 5. Record the GCS path on the Resume row (only after upload success). + try: + from sqlalchemy import update + from sqlalchemy.orm import Session + + from app.core.database import get_sync_engine + from app.models import Resume + + with Session(get_sync_engine()) as session: + session.execute( + update(Resume) + .where(Resume.id == ctx.resume_id) + .values(parsed_resume_path=gcs_path) + ) + session.commit() + except Exception as e: # noqa: BLE001 — surface any DB failure to the agent + logger.error( + "resume_tools: DB update of parsed_resume_path for resume %d failed: %s", + ctx.resume_id, e, exc_info=True, + ) + return ( + "ERROR: The parsed resume was uploaded but recording it in the " + f"database failed: {e}. Call save_parsed_resume again to retry." + ) + logger.info( + "resume_tools: resumes.parsed_resume_path set to '%s' for resume %d", + gcs_path, ctx.resume_id, + ) + + ctx.state.update({ + "saved": True, + "local_path": local_path, + "gcs_path": gcs_path, + "json": normalized, + }) + return ( + f"SUCCESS: Parsed resume saved to {gcs_path} and recorded in the database." + ) + + return save_parsed_resume + + +# Faithfulness score (0-10) the parsed JSON must meet against the resume text. +_HALLUCINATION_THRESHOLD = 7 + + +def _make_hallucination_guardrail(ctx: ResumeAnalysisContext): + """ + Build a CrewAI HallucinationGuardrail bound to this run's resume text, or + ``None`` when no reference text / LLM is available (e.g. the CLI path). + + The guardrail scores the parsed JSON's faithfulness against the extracted + resume text so the agent cannot invent skills, positions, or other facts. + + NOTE: in the open-source crewai package this guardrail is a pass-through + (it logs a notice and returns the output unchanged) — real faithfulness + scoring runs only on the CrewAI enterprise platform (app.crewai.com). The + wiring here is intentionally forward-compatible: it activates automatically + when running against enterprise, and never rejects a correctly-saved resume + on open source. + """ + if not ctx.resume_text: + return None + try: + from crewai.tasks.hallucination_guardrail import HallucinationGuardrail + + from app.services.crew.agents import gemini_llm_extraction + + return HallucinationGuardrail( + context=ctx.resume_text, + llm=gemini_llm_extraction, + threshold=_HALLUCINATION_THRESHOLD, + ) + except Exception as e: # noqa: BLE001 — never let guardrail setup break the run + logger.warning( + "resume_tools: could not build hallucination guardrail for resume %d: %s", + ctx.resume_id, e, + ) + return None + + +def make_parsed_resume_guardrail( + ctx: ResumeAnalysisContext, +) -> Callable[[TaskOutput], tuple[bool, Any]]: + """ + Build the resume-analysis task guardrail bound to one run's ``ctx``. + + A task's ``guardrail`` accepts a single value, so this composes three checks: + + 1. The save-tool invariant — the task passes only when save_parsed_resume + reported success. This replaces the task output with the normalized + JSON so downstream tasks (profiler) receive the exact persisted + document, and — on failure — is what aborts the crew after the retry + budget is exhausted (CrewAI raises, ``kickoff()`` propagates it). + 2. A URL-format check — every *_url field on the saved JSON must be a + valid http(s) URL or empty. (The save tool blocks invalid URLs before + it marks the run saved, so this is a secondary net.) + 3. A CrewAI HallucinationGuardrail — once the save is confirmed, the + normalized JSON is scored for faithfulness against the extracted + resume text so the agent cannot invent skills, positions, or other + facts. A failed check returns feedback for a retry; a guardrail + *error* (e.g. LLM unavailable) fails open so a transient outage does + not block a correctly-saved resume. + """ + hallucination_guardrail = _make_hallucination_guardrail(ctx) + + def parsed_resume_guardrail(task_output: TaskOutput) -> tuple[bool, Any]: + # 1. Save-tool invariant (load-bearing — aborts the crew on failure). + if not (ctx.state.get("saved") and ctx.state.get("json")): + logger.warning( + "resume_tools: guardrail rejected resume-analysis output for resume " + "%d — no successful save recorded", ctx.resume_id, + ) + return ( + False, + "The parsed resume was NOT saved. You MUST call the save_parsed_resume " + "tool with the complete parsed resume JSON and get a SUCCESS response " + "before producing your final answer. Fix any ERROR the tool reports " + "and try again.", + ) + + normalized = ctx.state["json"] + + # 2. URL-format check on the saved JSON (secondary net; the save tool + # already blocks invalid URLs). + try: + saved = ParsedResume.model_validate_json(normalized) + invalid_urls = find_invalid_url_fields(saved.result) + except ValidationError: + invalid_urls = [] + if invalid_urls: + logger.warning( + "resume_tools: guardrail found invalid URL field(s) %s for resume %d", + invalid_urls, ctx.resume_id, + ) + return ( + False, + f"These URL fields are not valid URLs: {', '.join(invalid_urls)}. " + "Every *_url field must be a complete http(s) URL or an empty " + "string \"\". If only the link's label/anchor text is visible (not " + "the actual URL), set that field to \"\". Fix these and call " + "save_parsed_resume again.", + ) + + # 3. Hallucination check against the resume text. + if hallucination_guardrail is not None: + try: + validated_output = TaskOutput( + description=task_output.description, + raw=normalized, + agent=task_output.agent, + ) + is_faithful, feedback = hallucination_guardrail(validated_output) + except Exception as e: # noqa: BLE001 — fail open on guardrail errors + logger.warning( + "resume_tools: hallucination guardrail errored for resume %d " + "(passing the saved output through): %s", ctx.resume_id, e, + ) + return True, normalized + if not is_faithful: + logger.warning( + "resume_tools: hallucination guardrail flagged resume %d: %s", + ctx.resume_id, feedback, + ) + return ( + False, + "The extracted data was not fully grounded in the resume. " + f"{feedback} Re-extract using ONLY facts present in the resume, " + "then call save_parsed_resume again with the corrected JSON.", + ) + + return True, normalized + + return parsed_resume_guardrail diff --git a/backend/app/services/crew/tasks.py b/backend/app/services/crew/tasks.py index 45fb352..f85b19c 100644 --- a/backend/app/services/crew/tasks.py +++ b/backend/app/services/crew/tasks.py @@ -1,6 +1,7 @@ from crewai import Task from app.services.crew.agents import ( github_project_summarizer, + resume_analyzer, profiler, resume_strategist, interview_preparer, @@ -9,6 +10,12 @@ read_resume, semantic_search_resume, ) +from app.services.crew.resume_tools import ( + ResumeAnalysisContext, + make_save_parsed_resume_tool, + make_parsed_resume_guardrail, +) +from app.services.resume_parser import PARSED_RESUME_SCHEMA # --------------------------------------------------------------------------- # NOTE: The researcher agent and research_task have been removed. @@ -26,20 +33,30 @@ # Building fresh Task objects per call (rather than reusing module-level # singletons) also makes per-request output_file overrides thread-safe under # the crew_runner's ThreadPoolExecutor. +# +# Parallelism: the GitHub summary task and the resume analysis task both run +# with async_execution=True. The (sync) profile task lists them in its +# ``context``, so CrewAI drains their futures — waiting for both and +# re-raising any failure — before profiling starts. A resume-analysis failure +# (guardrail retries exhausted) therefore aborts kickoff() before any +# downstream task runs. CrewAI validators require that the crew not END with +# more than one async task and that async tasks not reference each other in +# ``context``; the ordering below satisfies both. # --------------------------------------------------------------------------- # Description fragments toggled by include_github ------------------------------- # Profiler — step list ending differs depending on whether GitHub data exists. _PROFILE_GITHUB_STEPS = ( - " 3. Use the GitHub projects summary from the previous task as additional context.\n" - " 4. Synthesise all three sources into a comprehensive applicant profile." + " 4. Use the GitHub projects summary from the GitHub summary task as additional context.\n" + " 5. Synthesise all three sources into a comprehensive applicant profile." ) _PROFILE_NO_GITHUB_STEPS = ( - " 3. Synthesise the resume and job details into a comprehensive applicant profile.\n\n" + " 4. Synthesise the parsed resume and job details into a comprehensive applicant profile.\n\n" "IMPORTANT GROUNDING RULES:\n" " - No GitHub profile was provided for this applicant.\n" - " - Use ONLY information that appears in the resume and the job details above.\n" + " - Use ONLY information that appears in the parsed resume JSON, the raw resume, " + "and the job details above.\n" " - Do NOT invent, assume, or fabricate any projects, repositories, GitHub " "activity, employers, dates, metrics, or skills that are not present in the resume." ) @@ -57,8 +74,95 @@ ) +def _resume_analysis_task(ctx: ResumeAnalysisContext, *, run_async: bool = True) -> Task: + """ + Resume analysis task — extract the resume into the parsed-resume JSON and + persist it via the per-run save_parsed_resume tool. + + Runs with ``async_execution=True`` in the full crew so it executes in + parallel with the GitHub summary task; the isolation test script passes + ``run_async=False`` to run it standalone. + """ + schema = PARSED_RESUME_SCHEMA.replace("", str(ctx.resume_id)) + return Task( + # NOTE: this description embeds a JSON template. CrewAI interpolates + # bare {identifier} tokens in descriptions — the template's braces are + # all followed by quoted keys/whitespace so they are safe, but never + # add a bare {word} token here unless it is a real crew input. + description=( + "Carefully scan and analyze the applicant's resume and extract every " + "piece of relevant information into a structured JSON document.\n\n" + "Step 1 — Read the resume:\n" + " Read the full resume text from: {resume_path}\n\n" + "Step 2 — Extract the information into EXACTLY this JSON structure. " + "Keep every key. When the resume does not state a value, keep the " + "empty default shown (\"\", null, [], false or 0). Do not add keys.\n\n" + + schema + + "\n\nExtraction rules:\n" + f" - \"id\" MUST be {ctx.resume_id}.\n" + " - Dates use the YYYY-MM-DD format. When the resume gives only a month " + "or year, use the first day of that month/year. A position's end_date is " + "null when it is the applicant's current job.\n" + " - Do NOT invent, guess, or embellish anything that is not in the resume.\n" + " - Every *_url field (linkedin_url, github_url, twitter_url, " + "website_url, kaggle_url) MUST be a complete, valid URL — it must start " + "with 'http://' or 'https://' and contain a domain (e.g. " + "'https://www.linkedin.com/in/username'). Resumes often show only the " + "clickable link text (e.g. 'LinkedIn', 'Portfolio', 'Kaggle', or a " + "person's name) while the real URL is hidden in the hyperlink. If you " + "can only see such link text and NOT the actual URL, leave that field " + "as an empty string \"\". NEVER put link labels, names, or partial " + "fragments in a URL field.\n" + " - For years_of_experience: if the summary/objective explicitly states " + "a number of years of professional experience (e.g. 'over 2 years of " + "experience'), use that number (rounded down to a whole number). " + "Otherwise leave years_of_experience as 0 — it will be computed from the " + "position dates automatically; do NOT estimate it yourself.\n" + " - Derive has_remote_work_experience, remote_work_type, " + "has_management_experience and management_level from the positions and " + "their descriptions.\n" + " - Write a concise 2-4 sentence brief_summary of the applicant.\n" + " - \"skills\" is a dictionary. If the resume groups its skills under " + "category headings (e.g. \"Languages\", \"Frameworks / Libraries\", " + "\"Cloud & Ops\"), use each heading VERBATIM as a key and list that " + "category's skills as an array of strings. Split parenthetical or " + "comma-separated groupings into individual skill strings (e.g. " + "\"Multi Agent Orchestration (Crew AI, Google ADK)\" -> \"Crew AI\", " + "\"Google ADK\"). If the resume lists skills with NO category headings, " + "put them all under a single \"Default\" key. Only include skills that " + "actually appear in the resume.\n" + " Examples:\n" + " Resume text: 'Languages: Python, C++, SQL\\nCloud & Ops: GCP, AWS, " + "Docker' -> \"skills\": {\"Languages\": [\"Python\", \"C++\", \"SQL\"], " + "\"Cloud & Ops\": [\"GCP\", \"AWS\", \"Docker\"]}\n" + " Resume text: 'Programming: Python, C, C++\\nTools & Technologies: " + "PyTorch, Git' -> \"skills\": {\"Programming\": [\"Python\", \"C\", " + "\"C++\"], \"Tools & Technologies\": [\"PyTorch\", \"Git\"]}\n" + " Resume text: 'Python, HTML, CSS, JavaScript, React, AWS' -> " + "\"skills\": {\"Default\": [\"Python\", \"HTML\", \"CSS\", " + "\"JavaScript\", \"React\", \"AWS\"]}\n" + " - The JSON must be strictly valid: no comments, no trailing commas, " + "no markdown fences.\n\n" + "Step 3 — MANDATORY FINAL STEP:\n" + " Call the save_parsed_resume tool with the COMPLETE JSON string. If it " + "returns an ERROR, fix the reported problem and call it again with the " + "corrected JSON. Only after it returns SUCCESS, output that same JSON as " + "your final answer." + ), + expected_output=( + "The complete parsed-resume JSON document, exactly as successfully " + "persisted via the save_parsed_resume tool." + ), + tools=[read_resume, make_save_parsed_resume_tool(ctx)], + agent=resume_analyzer, + async_execution=run_async, + guardrail=make_parsed_resume_guardrail(ctx), + guardrail_max_retries=3, + ) + + def _github_summary_task() -> Task: - """Task 1 — index GitHub repos and summarise the most relevant projects.""" + """GitHub task — index GitHub repos and summarise the most relevant projects.""" return Task( description=( "You have been given structured job details in JSON format:\n\n" @@ -85,12 +189,16 @@ def _github_summary_task() -> Task: ), tools=[extract_github_repos_tool, repo_content_searcher], agent=github_project_summarizer, - async_execution=False, + # Runs in parallel with the resume analysis task; the profile task's + # context drains both before profiling starts. + async_execution=True, ) -def _profile_task(include_github: bool, github_task: Task | None) -> Task: - """Task 2 — build a comprehensive applicant profile.""" +def _profile_task( + include_github: bool, github_task: Task | None, resume_analysis_task: Task +) -> Task: + """Profile task — build a comprehensive applicant profile.""" steps = _PROFILE_GITHUB_STEPS if include_github else _PROFILE_NO_GITHUB_STEPS grounded_in = "the resume, GitHub work, and" if include_github else "the resume and" return Task( @@ -98,12 +206,21 @@ def _profile_task(include_github: bool, github_task: Task | None) -> Task: "Compile a detailed personal and professional profile for the applicant.\n\n" "Job details (for context):\n{job_details_json}\n\n" "Steps:\n" - " 1. Read and semantically understand the resume from: {resume_path}\n" + " 1. Use the structured parsed-resume JSON from the resume analysis task " + "(provided in your context) as the PRIMARY source of the applicant's " + "details: contact info, positions, skills, education, projects, " + "publications, certifications, and experience summary.\n" " 2. Use the job_description and requirements from the job details above " "to understand what the employer is looking for.\n" + " 3. Only if a detail is missing or unclear in the parsed JSON, consult " + "the raw resume at: {resume_path}\n" + steps ), - context=[github_task] if include_github else [], + context=( + [github_task, resume_analysis_task] + if include_github + else [resume_analysis_task] + ), expected_output=( "A comprehensive profile document that includes the applicant's skills, " "project experiences, contributions, interests, and communication style, " @@ -116,7 +233,7 @@ def _profile_task(include_github: bool, github_task: Task | None) -> Task: def _resume_strategy_task(include_github: bool, profile_task: Task) -> Task: - """Task 3 — tailor the resume to the job.""" + """Resume strategy task — tailor the resume to the job.""" clause = _RESUME_GITHUB_CLAUSE if include_github else _RESUME_NO_GITHUB_CLAUSE return Task( description=( @@ -142,7 +259,7 @@ def _resume_strategy_task(include_github: bool, profile_task: Task) -> Task: def _interview_preparation_task(profile_task: Task, resume_task: Task) -> Task: - """Task 4 — generate interview questions and talking points.""" + """Interview task — generate interview questions and talking points.""" return Task( description=( "Create targeted interview questions and talking points for the applicant.\n\n" @@ -165,24 +282,44 @@ def _interview_preparation_task(profile_task: Task, resume_task: Task) -> Task: ) -def build_tasks(include_github: bool = True) -> list[Task]: +def build_tasks( + include_github: bool = True, + resume_ctx: ResumeAnalysisContext | None = None, +) -> list[Task]: """ Construct a fresh, correctly-wired task list for one crew run. + Task order: [github?, resume_analysis, profile, resume, interview]. The + GitHub summary and resume analysis tasks run with ``async_execution=True`` + (in parallel); the profile task lists them in its ``context`` and starts + only after both succeed. If the resume analysis fails (guardrail retries + exhausted), ``kickoff()`` raises before the profile task runs. + When ``include_github`` is False the GitHub summarizer task is omitted and the profile/resume tasks are given grounding rules so the agents do not fabricate GitHub-derived content. + ``resume_ctx`` carries the user/resume identity for the resume analysis + task's save tool. When None (standalone CLI), a local-only context is used: + the parsed JSON is written to temp storage but never uploaded to GCS or + recorded in the database. + Return order is stable across both modes: the interview task is always the last element and the resume task always the second-to-last, so the runner can stamp per-request ``output_file`` paths via ``crew.tasks[-1]/[-2]``. """ + if resume_ctx is None: + resume_ctx = ResumeAnalysisContext( + user_id=0, resume_id=0, user_name="cli_user", local_only=True + ) + github_task = _github_summary_task() if include_github else None - profile = _profile_task(include_github, github_task) + analysis = _resume_analysis_task(resume_ctx) + profile = _profile_task(include_github, github_task, analysis) resume = _resume_strategy_task(include_github, profile) interview = _interview_preparation_task(profile, resume) - tasks = [profile, resume, interview] + tasks = [analysis, profile, resume, interview] if include_github: tasks.insert(0, github_task) return tasks diff --git a/backend/app/services/crew_runner.py b/backend/app/services/crew_runner.py index 335029c..7e1427b 100644 --- a/backend/app/services/crew_runner.py +++ b/backend/app/services/crew_runner.py @@ -45,12 +45,17 @@ def _update_progress(job_id: int, step: str, error: str = None): def _run_crew_sync( job_id: int, user_id: int, user_email: str, github_username: str = None, github_url: str = None, bq_dataset_name: str = None, - resume_content: str = None, job_url: str = None, + resume_bytes: bytes = None, resume_filename: str = None, job_url: str = None, + resume_id: int = None, user_name: str = None, ): """ Synchronous crew execution — runs in a thread. This imports and uses the existing crew pipeline. + The raw resume file bytes (.pdf/.docx/.md) are parsed to text BEFORE the + crew is built; a ResumeParsingError aborts the run so the crew never + starts and the job is marked failed (the UI offers a retry). + When no GitHub URL is supplied the crew is built without the GitHub summarizer agent/task and tailors the resume from the resume + job details alone. @@ -72,20 +77,42 @@ def _run_crew_sync( logger.error("crew_runner: Failed to extract job details for job %d: %s", job_id, e) raise RuntimeError(f"Failed to extract job details: {e}") + # Step 2: Parse the resume document (.pdf/.docx/.md) into text. + # Failure here must stop everything — no crew, no LLM tokens. + _update_progress(job_id, "parsing_resume") + from app.services.resume_parser import ResumeParsingError, extract_resume_text + + suffix = Path(resume_filename).suffix.lower() if resume_filename else ".md" + with tempfile.NamedTemporaryFile(mode="wb", suffix=suffix, delete=False, prefix="resume_raw_") as f: + f.write(resume_bytes) + raw_resume_path = f.name + + try: + resume_text = extract_resume_text(raw_resume_path) + except ResumeParsingError as e: + logger.error( + "crew_runner: Resume parsing failed for job %d (resume %s, file '%s'): %s", + job_id, resume_id, resume_filename, e, exc_info=True, + ) + if os.path.exists(raw_resume_path): + os.unlink(raw_resume_path) + raise RuntimeError(f"Resume parsing failed: {e}") - # Step 2: Write resume to temp file for crew to read + # Step 3: Write extracted text to a temp .md for the crew to read + # (FileReadTool / MDXSearchTool operate on this file). # The "searching_projects" step only applies when GitHub repos are indexed; # without GitHub we advance straight to building the profile. _update_progress(job_id, "searching_projects" if include_github else "building_profile") with tempfile.NamedTemporaryFile(mode="w", suffix=".md", delete=False, prefix="resume_") as f: - f.write(resume_content) + f.write(resume_text) resume_path = f.name try: - # Step 3: Build crew and run + # Step 4: Build crew and run _update_progress(job_id, "building_profile") from app.services.crew.crew import build_crew + from app.services.crew.resume_tools import ResumeAnalysisContext applicant_name = user_email.split("@")[0].replace(".", "_") @@ -97,9 +124,20 @@ def _run_crew_sync( ts = datetime.now().strftime("%Y%m%d_%H%M%S") crew_log_path = str(_LOG_DIR / f"{ts}_crew") + # Identity context for the resume_analyzer's save tool: the parsed + # JSON is uploaded to GCS and recorded on resumes.parsed_resume_path. + # resume_text is passed as the hallucination-guardrail reference context. + resume_ctx = ResumeAnalysisContext( + user_id=user_id, + resume_id=resume_id, + user_name=user_name or applicant_name, + local_only=False, + resume_text=resume_text, + ) + crew = build_crew( output_log_file=crew_log_path, verbose=True, tracing=False, - include_github=include_github, + include_github=include_github, resume_ctx=resume_ctx, ) logger.info("crew_runner: Crew verbose log → %s", crew_log_path) @@ -124,6 +162,11 @@ def _run_crew_sync( result = crew.kickoff(inputs=job_application_inputs) logger.info("crew_runner: Crew execution completed for job %d. Log saved to %s", job_id, crew_log_path) + if resume_ctx.state.get("gcs_path"): + logger.info( + "crew_runner: Parsed resume for job %d stored at %s", + job_id, resume_ctx.state["gcs_path"], + ) # Read output files tailored_resume_content = "" @@ -147,7 +190,7 @@ def _run_crew_sync( finally: # Clean up temp files - for path in [resume_path]: + for path in [resume_path, raw_resume_path]: if os.path.exists(path): os.unlink(path) @@ -156,6 +199,7 @@ def _run_crew_sync( async def run_crew_for_job( job_id: int, user_id: int, user_email: str, github_profile, resume_gcs_path: str, job_url: str, + resume_id: int = None, user_name: str = None, resume_filename: str = None, ): """ Async entry point for crew execution. Updates job status in DB. @@ -163,13 +207,17 @@ async def run_crew_for_job( ``github_profile`` may be ``None`` when the user tailors a resume without a GitHub profile; the crew then runs without the GitHub summarizer. + + ``resume_id`` / ``user_name`` / ``resume_filename`` identify the resume for + the resume_analyzer agent (parsed-resume filename, GCS blob, and the + resumes.parsed_resume_path DB update). The raw bytes are kept binary — + PDF/DOCX resumes must not be utf-8 decoded. """ _update_progress(job_id, "pending") try: - # Download resume from GCS + # Download resume from GCS (raw bytes — may be a binary .pdf/.docx) resume_bytes = download_file(resume_gcs_path) - resume_content = resume_bytes.decode("utf-8") # Update status to processing async with AsyncSessionLocal() as db: @@ -190,7 +238,8 @@ async def run_crew_for_job( _executor, _run_crew_sync, job_id, user_id, user_email, gh_username, gh_url, gh_dataset, - resume_content, job_url, + resume_bytes, resume_filename, job_url, + resume_id, user_name, ) # Save extracted job details to DB on the correct async event loop diff --git a/backend/app/services/gcs_service.py b/backend/app/services/gcs_service.py index 86130e4..e4d7718 100644 --- a/backend/app/services/gcs_service.py +++ b/backend/app/services/gcs_service.py @@ -5,18 +5,27 @@ signed URLs for resume and interview material files. Bucket structure: - gs:///uploads//.md — User-uploaded original resumes + gs:///uploads// — User-uploaded original resumes (.md/.pdf/.docx) + gs:///parsed//___parsed_resume.json — Agent-parsed resume JSON gs:///tailored//_resume.md — Agent-generated tailored resumes gs:///tailored//_interview.md — Agent-generated interview materials """ import datetime +from pathlib import Path + from google.cloud import storage from app.core.config import get_settings settings = get_settings() +_RESUME_CONTENT_TYPES = { + ".md": "text/markdown", + ".pdf": "application/pdf", + ".docx": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", +} + def _get_client() -> storage.Client: """Return a GCS client.""" @@ -52,14 +61,41 @@ def upload_resume(user_id: int, file_bytes: bytes, filename: str) -> str: ) blob_path = f"uploads/{user_id}/{filename}" + content_type = _RESUME_CONTENT_TYPES.get( + Path(filename).suffix.lower(), "application/octet-stream" + ) bucket = _get_bucket() blob = bucket.blob(blob_path) - blob.upload_from_string(file_bytes, content_type="text/markdown") + blob.upload_from_string(file_bytes, content_type=content_type) gcs_path = f"gs://{settings.GCS_BUCKET_NAME}/{blob_path}" return gcs_path +def upload_parsed_resume(user_id: int, filename: str, content: str) -> str: + """ + Upload the resume_analyzer agent's parsed-resume JSON to GCS. + + The blob path is deterministic (parsed//), so a retried + job simply overwrites the previous upload. + + Args: + user_id: The user's database ID. + filename: Parsed-resume filename + (___parsed_resume.json). + content: JSON content of the parsed resume. + + Returns: + The GCS path (gs://bucket/parsed//). + """ + blob_path = f"parsed/{user_id}/{filename}" + bucket = _get_bucket() + blob = bucket.blob(blob_path) + blob.upload_from_string(content.encode("utf-8"), content_type="application/json") + + return f"gs://{settings.GCS_BUCKET_NAME}/{blob_path}" + + def upload_tailored_resume(user_id: int, job_id: int, content: str, filename: str) -> str: """ Upload an agent-generated tailored resume to GCS. diff --git a/backend/app/services/resume_parser.py b/backend/app/services/resume_parser.py new file mode 100644 index 0000000..5d663d4 --- /dev/null +++ b/backend/app/services/resume_parser.py @@ -0,0 +1,454 @@ +""" +app.services.resume_parser +-------------------------- +Document-type detection and text extraction for user-uploaded resumes +(.pdf / .docx / .md), plus the Pydantic models and JSON schema template +used by the resume_analyzer agent. + +Extraction strategy: + .pdf — pypdf first; if it errors or yields near-empty text (e.g. a + scanned PDF), fall back to pdfplumber; if that also fails, + raise ResumeParsingError. + .docx — python-docx (paragraphs + table cells); failures raise + ResumeParsingError. + .md — file content used as-is. +""" + +from pathlib import Path +from typing import Dict, List, Optional + +from pydantic import BaseModel, Field + +from app.core.logging import get_logger + +logger = get_logger(__name__) + +# A pypdf result with fewer non-whitespace characters than this is treated as +# a failed extraction (typically a scanned/image-only PDF) and retried with +# pdfplumber. +_MIN_EXTRACTED_CHARS = 50 + +SUPPORTED_EXTENSIONS = {".pdf": "pdf", ".docx": "docx", ".md": "md"} + + +class ResumeParsingError(Exception): + """Raised when a resume file cannot be parsed into text.""" + + +# ── Document type detection ─────────────────────────────────────────────────── + +def detect_document_type(file_path: str) -> str: + """ + Return the document type ("pdf" | "docx" | "md") for ``file_path``. + + Raises: + ResumeParsingError: If the extension is not one of .pdf/.docx/.md. + """ + suffix = Path(file_path).suffix.lower() + doc_type = SUPPORTED_EXTENSIONS.get(suffix) + if doc_type is None: + raise ResumeParsingError( + f"Unsupported resume document type '{suffix}' for file '{file_path}'. " + f"Supported types: {', '.join(sorted(SUPPORTED_EXTENSIONS))}." + ) + return doc_type + + +# ── Per-type extractors ─────────────────────────────────────────────────────── + +def _non_whitespace_len(text: str) -> int: + return len("".join(text.split())) + + +def extract_pdf_text(file_path: str) -> str: + """ + Extract text from a PDF resume. + + Tries pypdf first; on any exception or a near-empty result falls back to + pdfplumber. If both fail, raises ResumeParsingError. + """ + pypdf_error: Exception | None = None + try: + from pypdf import PdfReader + + reader = PdfReader(file_path) + text = "\n".join((page.extract_text() or "") for page in reader.pages) + if _non_whitespace_len(text) >= _MIN_EXTRACTED_CHARS: + logger.info( + "resume_parser: pypdf extracted %d chars from '%s' (%d pages)", + len(text), file_path, len(reader.pages), + ) + return text + logger.warning( + "resume_parser: pypdf returned near-empty text (%d non-ws chars) for '%s'; " + "falling back to pdfplumber", + _non_whitespace_len(text), file_path, + ) + except Exception as e: # noqa: BLE001 — any pypdf failure triggers the fallback + pypdf_error = e + logger.warning( + "resume_parser: pypdf failed for '%s' (%s); falling back to pdfplumber", + file_path, e, + ) + + try: + import pdfplumber + + with pdfplumber.open(file_path) as pdf: + text = "\n".join((page.extract_text() or "") for page in pdf.pages) + if _non_whitespace_len(text) < _MIN_EXTRACTED_CHARS: + raise ResumeParsingError( + f"pdfplumber extracted no meaningful text from '{file_path}' " + "(possibly a scanned/image-only PDF)." + ) + logger.info( + "resume_parser: pdfplumber extracted %d chars from '%s'", len(text), file_path + ) + return text + except ResumeParsingError: + logger.error( + "resume_parser: PDF extraction failed for '%s' — pypdf%s and pdfplumber " + "both produced no usable text", + file_path, f" ({pypdf_error})" if pypdf_error else "", + exc_info=True, + ) + raise + except Exception as e: + logger.error( + "resume_parser: PDF extraction failed for '%s' — pypdf error: %s; " + "pdfplumber error: %s", + file_path, pypdf_error, e, exc_info=True, + ) + raise ResumeParsingError( + f"Failed to parse PDF '{file_path}': pypdf error: {pypdf_error}; " + f"pdfplumber error: {e}" + ) from e + + +def extract_docx_text(file_path: str) -> str: + """ + Extract text from a DOCX resume (paragraphs and table cells). + + Raises: + ResumeParsingError: If python-docx cannot read the file or the file + contains no meaningful text. + """ + try: + from docx import Document + + document = Document(file_path) + parts = [para.text for para in document.paragraphs] + for table in document.tables: + for row in table.rows: + parts.extend(cell.text for cell in row.cells) + text = "\n".join(part for part in parts if part and part.strip()) + except Exception as e: + logger.error( + "resume_parser: python-docx failed for '%s': %s", file_path, e, exc_info=True + ) + raise ResumeParsingError(f"Failed to parse DOCX '{file_path}': {e}") from e + + if _non_whitespace_len(text) < _MIN_EXTRACTED_CHARS: + logger.error( + "resume_parser: DOCX '%s' contains no meaningful text (%d non-ws chars)", + file_path, _non_whitespace_len(text), + ) + raise ResumeParsingError( + f"DOCX '{file_path}' contains no meaningful text." + ) + logger.info( + "resume_parser: python-docx extracted %d chars from '%s'", len(text), file_path + ) + return text + + +def extract_md_text(file_path: str) -> str: + """Return a Markdown resume's content as-is.""" + try: + text = Path(file_path).read_text(encoding="utf-8") + except UnicodeDecodeError: + logger.warning( + "resume_parser: '%s' is not valid utf-8; re-reading with errors='replace'", + file_path, + ) + text = Path(file_path).read_text(encoding="utf-8", errors="replace") + except Exception as e: + logger.error( + "resume_parser: failed to read markdown '%s': %s", file_path, e, exc_info=True + ) + raise ResumeParsingError(f"Failed to read Markdown '{file_path}': {e}") from e + logger.info("resume_parser: read %d chars from markdown '%s'", len(text), file_path) + return text + + +def extract_resume_text(file_path: str) -> str: + """ + Detect the document type of ``file_path`` and extract its text. + + Raises: + ResumeParsingError: If the type is unsupported or extraction fails. + """ + doc_type = detect_document_type(file_path) + logger.info("resume_parser: extracting '%s' as %s", file_path, doc_type) + if doc_type == "pdf": + return extract_pdf_text(file_path) + if doc_type == "docx": + return extract_docx_text(file_path) + return extract_md_text(file_path) + + +# ── Parsed-resume filename helpers ──────────────────────────────────────────── + +def sanitize_user_name(name: str) -> str: + """Lowercase ``name`` and collapse every non-alphanumeric run to a single '_'.""" + sanitized = "".join(c if c.isalnum() else "_" for c in name.lower()) + while "__" in sanitized: + sanitized = sanitized.replace("__", "_") + return sanitized.strip("_") or "user" + + +def build_parsed_resume_filename(user_name: str, user_id: int, resume_id: int) -> str: + """Return '___parsed_resume.json'.""" + return f"{sanitize_user_name(user_name)}_{user_id}_{resume_id}_parsed_resume.json" + + +# ── Structured output models (guardrail validation) ────────────────────────── + +class ProjectItem(BaseModel): + project_name: str = "" + description: str = "" + url: str = "" + + +class PublicationItem(BaseModel): + title: str = "" + publisher: str = "" + date: str = "" + url: str = "" + + +class PositionItem(BaseModel): + position_name: str = "" + company_name: str = "" + country: str = "" + start_date: str = "" + end_date: Optional[str] = None + skills: List[str] = Field(default_factory=list) + job_type: str = "" + job_details: str = "" + + +class EducationItem(BaseModel): + school_name: str = "" + school_type: str = "" + degree_type: str = "" + faculty_department: str = "" + specialization_subjects: str = "" + country: str = "" + start_date: str = "" + end_date: str = "" + learning_mode: str = "" + education_details: str = "" + + +class ParsedResumeResult(BaseModel): + name: str = "" + email: str = "" + phone: str = "" + address: str = "" + city: str = "" + country: str = "" + language: str = "" + spoken_languages: List[str] = Field(default_factory=list) + honors_and_awards: List[str] = Field(default_factory=list) + courses_and_certifications: List[str] = Field(default_factory=list) + linkedin_url: str = "" + github_url: str = "" + twitter_url: str = "" + website_url: str = "" + kaggle_url: str = "" + date_of_birth: Optional[str] = None + nationality: str = "" + summary_objective: str = "" + work_authorization: str = "" + years_of_experience: int = 0 + has_remote_work_experience: bool = False + remote_work_type: str = "" + has_management_experience: bool = False + management_level: str = "" + approximate_age: Optional[int] = None + brief_summary: str = "" + drivers_licenses: List[str] = Field(default_factory=list) + interests_hobbies: List[str] = Field(default_factory=list) + projects: List[ProjectItem] = Field(default_factory=list) + volunteer_experience: List[str] = Field(default_factory=list) + publications: List[PublicationItem] = Field(default_factory=list) + positions: List[PositionItem] = Field(default_factory=list) + # Skills grouped by the category headings used in the resume (e.g. + # "Languages", "Frameworks / Libraries"). When the resume lists skills + # without any category headings, all skills go under a single "Default" key. + skills: Dict[str, List[str]] = Field(default_factory=dict) + education_qualifications: List[EducationItem] = Field(default_factory=list) + + +class ParsedResume(BaseModel): + id: int + result: ParsedResumeResult + + +# ── URL validation & years-of-experience computation ───────────────────────── + +# The *_url fields that must hold a valid URL or an empty string. +URL_FIELDS = ( + "linkedin_url", "github_url", "twitter_url", "website_url", "kaggle_url", +) + + +def is_valid_url(value: str) -> bool: + """ + Return True if ``value`` is a syntactically valid http(s) URL. + + An empty string is considered valid (a URL that could not be extracted is + intentionally left blank). Bare link labels ("LinkedIn", "Portfolio", + "Kaggle", a person's name) are rejected. + """ + if value == "": + return True + from urllib.parse import urlparse + + try: + parsed = urlparse(value.strip()) + except Exception: # noqa: BLE001 — any parse failure is an invalid URL + return False + return parsed.scheme in ("http", "https") and bool(parsed.netloc) and "." in parsed.netloc + + +def find_invalid_url_fields(result: "ParsedResumeResult") -> List[str]: + """Return the names of *_url fields on ``result`` that are not valid URLs.""" + return [field for field in URL_FIELDS if not is_valid_url(getattr(result, field, ""))] + + +def _parse_iso_date(value: Optional[str]): + """Parse a YYYY-MM-DD string into a ``date``; return None on any failure.""" + if not value: + return None + from datetime import date + + try: + return date.fromisoformat(value.strip()[:10]) + except (ValueError, TypeError): + return None + + +def compute_years_of_experience(result: "ParsedResumeResult") -> int: + """ + Compute total years of professional experience from the position dates. + + The span runs from the earliest position start_date to the latest end_date + (a missing/blank end_date is treated as today, i.e. a current position), + and is floored to whole years. Returns 0 when no usable dates exist. + """ + from datetime import date + + starts, ends = [], [] + for pos in result.positions: + start = _parse_iso_date(pos.start_date) + if start is None: + continue + starts.append(start) + # A blank/missing end_date means the position is current → use today. + end = _parse_iso_date(pos.end_date) or date.today() + ends.append(end) + + if not starts: + return 0 + + earliest, latest = min(starts), max(ends) + if latest <= earliest: + return 0 + return (latest - earliest).days // 365 + + +# JSON template embedded verbatim in the resume-analysis task description. +# Safe for CrewAI description interpolation: interpolation only replaces bare +# {identifier} tokens, and every brace here is followed by a quoted key or +# whitespace. Never add a bare {word} token to this literal — CrewAI would +# raise a KeyError for a missing crew input. +PARSED_RESUME_SCHEMA: str = """{ + "id": , + "result": { + "name": "", + "email": "", + "phone": "", + "address": "", + "city": "", + "country": "", + "language": "", + "spoken_languages": [], + "honors_and_awards": [], + "courses_and_certifications": [], + "linkedin_url": "", + "github_url": "", + "twitter_url": "", + "kaggle_url": "", + "website_url": "", + "date_of_birth": null, + "nationality": "", + "summary_objective": "", + "work_authorization": "", + "years_of_experience": 0, + "has_remote_work_experience": false, + "remote_work_type": "", + "has_management_experience": false, + "management_level": "", + "approximate_age": null, + "brief_summary": "", + "drivers_licenses": [], + "interests_hobbies": [], + "projects": [ + { + "project_name": "", + "description": "", + "url": "" + } + ], + "volunteer_experience": [], + "publications": [ + { + "title": "", + "publisher": "", + "date": "", + "url": "" + } + ], + "positions": [ + { + "position_name": "", + "company_name": "", + "country": "", + "start_date": "", + "end_date": null, + "skills": [], + "job_type": "", + "job_details": "" + } + ], + "skills": { + "Default": [] + }, + "education_qualifications": [ + { + "school_name": "", + "school_type": "", + "degree_type": "", + "faculty_department": "", + "specialization_subjects": "", + "country": "", + "start_date": "", + "end_date": "", + "learning_mode": "", + "education_details": "" + } + ] + } +}""" diff --git a/backend/docs/resume_analyzer_agent_creation_prompt.txt b/backend/docs/resume_analyzer_agent_creation_prompt.txt new file mode 100644 index 0000000..54930f1 --- /dev/null +++ b/backend/docs/resume_analyzer_agent_creation_prompt.txt @@ -0,0 +1,370 @@ +Create a new CrewAI agent called "resume_analyzer" that carefully scans and analyzes the given resume to extract all the relevant information it can find and produce them as output in the JSON format given below. It should create a file called "___parsed_resume.json" in local temp storage and then upload it to the GCS storage. It should store the GCS path of the uploaded file in the database table "Resume" under field "parsed_resume_path". Create this field if the current Database table does not have it. Modify "backend/app/models/__init__.py" which contains the Database table details accordingly. Also update the schema if necessary present in "backend/app/schemas/__init__.py". The agent should only write to the database if the file creation and the subsequent upload of the file to GCS is successful. + +Use existing tools or create new tools that this agent can use to write, upload files or update database. + +Create helper functions to first check the document type (.pdf, .docx or .md), then create three helper functions to extract data from each document type. +For PDF use "PyPDF" package initially and if it fails to properly parse the document, use "pdfplumber" package. If that even fails, raise proper Error. +For .docx type, use "python-docx" package to extract the data and implemenent proper failure resolution. +For .md file use the file as is. + +If the parsing fails, the Crew should not continue further and notify the user that resume parsing failed and all related logs should be carefully written. +The user will have the option to try again from the UI interface which is already implemented. Carefully think and create the relevant CrewAI task for the same. +The Resume analyzing task should be done in parallel to the "github_task". Modify crew in "backend/app/services/crew/crew.py" and build_tasks in "backend/app/services/crew/tasks.py" accordingly. +Always follow the official documentation guides while implementing CrewAI related code. You can use Context7 MCP to get latest documentation. + +The contents of this JSON file should be passed to the "profiler" agent. So also modify the task description of "_profile_task" and the task build order in "backend/app/services/crew/tasks.py". + +Create a python script in "backend/tests" to run this agent and task in isolation to check the output quality. +Let the agent create the output file in local temp storage but it should not upload to gcs or write to database. +It should copy the file from temp folder to an "output" folder inside "backend/tests". +The filename should be "test_parsed_resume_.json" as the usual filename mentioned above cannot be used due to lack of user_name, user_id and resume_id information. + +Output JSON format: +""" +{ "id": + "result": { + "name": "", + "email": "", + "phone": "", + "address": "", + "city": "", + "country": "", + "language": "", + "spoken_languages": [], + "honors_and_awards": [], + "courses_and_certifications": [], + "linkedin_url": "", + "github_url": "", + "twitter_url": "", + "website_url": "", + "date_of_birth": null, + "nationality": "", + "summary_objective": "", + "work_authorization": "", + "years_of_experience": 0, + "has_remote_work_experience": false, + "remote_work_type": "", + "has_management_experience": false, + "management_level": "", + "approximate_age": null, + "brief_summary": "", + "drivers_licenses": [], + "interests_hobbies": [], + "projects": [ + { + "project_name": "", + "description": "", + "url": "" + } + ], + "volunteer_experience": [], + "publications": [ + { + "title": "", + "publisher": "", + "date": "", + "url": "" + } + ], + "positions": [ + { + "position_name": "", + "company_name": "", + "country": "", + "start_date": "", + "end_date": null, + "skills": [], + "job_type": "", + "job_details": "" + } + ], + "education_qualifications": [ + { + "school_name": "", + "school_type": "", + "degree_type": "", + "faculty_department": "", + "specialization_subjects": "", + "country": "", + "start_date": "", + "end_date": "", + "learning_mode": "", + "education_details": "" + } + ] + } +} +""" + +Example Output: +Source file present in "/home/arijit/Job_Application_Agent/backend/sample_data/Arijit De Resume 2026.pdf" +""" +{ + "id": 2 + "result": { + "name": "Arijit De", + "email": "arijitde2050@gmail.com", + "phone": "+918981800329", + "address": "", + "city": "Kolkata", + "country": "India", + "language": "", + "spoken_languages": [], + "honors_and_awards": [], + "courses_and_certifications": [ + "Deep Learning, a 5-course specialization, by Deeplearning.ai. Verify here.", + "MCPS: Microsoft Certified Professional, Microsoft Certification No.: 1042C4-5DHF30" + ], + "linkedin": "https://www.linkedin.com/in/de-arijit/", + "github": "https://github.com/arijitde92", + "twitter": "", + "website": "", + "date_of_birth": null, + "nationality": "", + "summary_objective": "", + "work_authorization": "", + "years_of_experience": 8, + "has_remote_work_experience": true, + "remote_work_type": "fully_remote", + "has_management_experience": true, + "management_level": "team_lead", + "approximate_age": null, + "brief_summary": "Machine Learning Engineer and Computer Vision Engineer with experience in multi-agent AI systems, RAG pipelines, MLOps, backend engineering, and 3D computer vision. Strong background in Python, PyTorch, FastAPI, cloud platforms, and applied AI research, with industry and academic experience across construction tech, neuroimaging, ADAS, and enterprise systems.", + "drivers_licenses": [], + "interests_hobbies": [], + "projects": [ + { + "project_name": "Doctor Appointment Scheduler", + "description": "Architected and deployed a scalable, production-level Multi-Agent System for appointment scheduling. Designed the Agent Orchestration and multi agent delegation and coordination using Google ADK, integrating intelligent doctor matching and calendar scheduling (task hand-off/execution). Implemented robust Tool Calling and Execution using Composio for calendar actions and Google Maps MCP. The scalable Backend Engineering architecture utilizes Google Cloud Run for containerized deployment, integrating Google Cloud SQL and Gemini Enterprise.", + "url": "https://doctor-appointment-app-173427564927.asia-south1.run.app/" + }, + { + "project_name": "Job Application Agent", + "description": "Developed a real-world Multi-Agent System for automation, utilizing Crew AI for advanced workflow orchestration of specialized agents. Engineered Shared State/Memory by integrating Google BigQuery (vector store) and Vertex AI (embeddings) for consistent, scalable semantic search and storage across agents. Implemented custom tools (LinkedIn/GitHub scrapers) and leveraged LangChain for core system logic, all built in Python.", + "url": "" + } + ], + "volunteer_experience": [], + "publications": [ + { + "title": "Predicting Genetic Markers for Brain Tumors Using a Composite Loss", + "publisher": "IEEE Transactions on Computational Biology and Bioinformatics (TCBB)", + "date": "2025-07-01", + "url": "" + }, + { + "title": "Shape Induced Multi-class Deep Graph Cut for Hippocampus Subfield Segmentation", + "publisher": "Springer, Cham", + "date": "2025-01-01", + "url": "https://doi.org/10.1007/978-3-031-78201-5_16" + }, + { + "title": "3D Hippocampus Segmentation Using a Hog Based Loss Function with Majority Pooling", + "publisher": "2023 IEEE International Conference on Image Processing (ICIP)", + "date": "2023-10-01", + "url": "" + }, + { + "title": "Brain Tumor Classification from Radiology and Histopathology using Deep Features and Graph Convolutional Network", + "publisher": "2022 26th International Conference on Pattern Recognition (ICPR 2022)", + "date": "2022-01-01", + "url": "" + }, + { + "title": "A Deep Graph Cut Model for 3D Brain Tumor Segmentation", + "publisher": "44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC 2022)", + "date": "2022-01-01", + "url": "" + }, + { + "title": "DTI based Alzheimer's Disease Classification with Rank Modulated Fusion of CNNs and Random Forest", + "publisher": "Expert Syst. Appl.", + "date": "2021-01-01", + "url": "" + } + ], + "references": [], + "positions": [ + { + "position_name": "Computer Vision Engineer", + "company_name": "Synerjix", + "country": "USA", + "start_date": "2025-06-01", + "end_date": "2025-12-01", + "skills": [ + "DinoV3", + "Python", + "FastAPI", + "MySQL", + "AWS", + "Sagemaker", + "EC2", + "EKS", + "CI/CD", + "Docker" + ], + "job_type": "Remote", + "job_details": "Smart Moving Assistant Created a Smart Moving Assistant, helping users estimate shifting cost of items using room photos. Used DinoV3 for object detection, python and FastAPI for backend development, MySQL as the Database and AWS (Sagemaker, EC2, EKS) as the deployment platform along with CI/CD and Docker. Automation improved moving estimation by 70% and improved stakeholder business efficiency by 50%." + }, + { + "position_name": "Machine Learning Engineer", + "company_name": "mVizn Pte. Ltd.", + "country": "Singapore", + "start_date": "2024-01-01", + "end_date": null, + "skills": [ + "CrewAI", + "Retrieval-Augmented Generation (RAG)", + "Pinecone", + "LoRA", + "Gemini", + "Python", + "PyTorch", + "MLflow", + "FastAPI", + "Pydantic", + "asyncio", + "ONNX", + "Docker", + "Google Cloud Platform (GCP)" + ], + "job_type": "Remote", + "job_details": "Smart Property Manager Project Singapore (Remote) Developed a multi-agent platform for the construction domain using CrewAI enabling Autonomous Project Management & Scheduling (APMS), Automated Estimation & Procurement (AEP), and intelligent project information retrieval increasing overall business efficiency by 50%. Conceptualized the orchestration layer for governing through intelligent state & memory management, context propagation, and dynamic routing of user intent. Designed robust multi-agent workflows by balancing technical trade-offs and ensuring model observability, tracing and performance monitoring. Built multi-modal Retrieval-Augmented Generation (RAG) pipelines optimizing for performance and accurate retrieval with hybrid search and reranking techniques across massive datasets of text, images and CAD files leveraging multiple types of embeddings stored in Pinecone. Applied LoRA fine-tuning to adapt Gemini LLMs for domain understanding & structured output formatting. 3D Scan-to-BIM Project Led the development of a scalable end-to-end machine learning pipeline from training to deployment for 3D semantic segmentation of indoor point clouds extracted from LiDAR scans using Python and PyTorch reducing Building Information Modeling (BIM) creation time by 60%. Implemented MLflow-based experiment tracking, model versioning, model registry, and reproducible training workflows to support scalable MLOps practices. Designed and implemented scalable, robust backend architectures using FastAPI, Pydantic, asyncio and multi threaded programming. Optimized models through ONNX and deployed services using Docker and Google Cloud Platform (GCP), including Cloud Storage, Compute Engine VMs, IAM and Cloud SQL services." + }, + { + "position_name": "Computer Vision Engineer", + "company_name": "Institute of Neurosciences", + "country": "India", + "start_date": "2022-01-01", + "end_date": "2023-12-01", + "skills": [ + "PyTorch", + "Python", + "OpenCV", + "Numpy", + "ETL", + "Matplotlib", + "Streamlit", + "Docker" + ], + "job_type": "Onsite", + "job_details": "AI Assisted Neuroimaging Kolkata, India Designed and implemented custom 3D Computer Vision solutions related to various Neurological disorders using PyTorch, Python, OpenCV and Numpy improving diagnosis speed by 40%. Created custom data processing and ingestion (ETL) pipelines for handling massive MRI, CT images. Converted raw patient data into valuable information with innovative data analysis and interactive dashboards for stakeholders using Matplotlib, Streamlit, Docker." + }, + { + "position_name": "Machine Learning Engineer", + "company_name": "Mercedes-Benz Research and Development India", + "country": "India", + "start_date": "2018-08-01", + "end_date": "2019-08-01", + "skills": [ + "Computer Vision", + "YOLOv3", + "AWS", + "Python", + "QA" + ], + "job_type": "Onsite", + "job_details": "Advanced driver-assistance system (ADAS) Bengaluru, India Developed and optimized a computer vision pipeline for Vulnerable Road User (VRU) detection using YOLOv3 and large-scale annotated datasets. Designed & deployed model training, evaluation, and validation workflows in AWS. Built automated quality assurance (QA) and evaluation frameworks using Python, improving annotation throughput by 20% and data quality by 25%." + }, + { + "position_name": "Systems Engineer", + "company_name": "Tata Consultancy Services", + "country": "India", + "start_date": "2014-08-01", + "end_date": "2015-08-01", + "skills": [ + "Java", + "MySQL" + ], + "job_type": "Onsite", + "job_details": "Ultimatix Team Kolkata, India Maintained and enhanced high-availability backend systems (Java, MySQL) for TCS’s internal website, ensuring seamless functionality for over 100,000 users. Streamlined feature release cycle, reducing deployment time by 20%." + } + ], + "education_qualifications": [ + { + "school_name": "Jadavpur University", + "school_type": "University or equivalent", + "degree_type": "Doctorate/PhD or equivalent", + "faculty_department": "", + "specialization_subjects": "", + "country": "India", + "start_date": "2019-01-01", + "end_date": "2024-01-01", + "learning_mode": "In-person learning", + "education_details": "PhD, Jadavpur University, Kolkata, India." + }, + { + "school_name": "Jadavpur University", + "school_type": "University or equivalent", + "degree_type": "Master's Degree or equivalent", + "faculty_department": "", + "specialization_subjects": "Computer Science & Engineering", + "country": "India", + "start_date": "2016-01-01", + "end_date": "2018-01-01", + "learning_mode": "In-person learning", + "education_details": "M.Tech. Computer Science & Engineering, Jadavpur University, Kolkata, India. GPA - 8.83" + }, + { + "school_name": "Techno India", + "school_type": "College or equivalent", + "degree_type": "Bachelor's Degree or equivalent", + "faculty_department": "", + "specialization_subjects": "Computer Science & Engineering", + "country": "India", + "start_date": "2010-01-01", + "end_date": "2014-01-01", + "learning_mode": "In-person learning", + "education_details": "B.Tech. Computer Science & Engineering, Techno India, Kolkata, India. GPA - 8.81" + } + ] + } +} +""" + +Modify "PARSED_RESUME_SCHEMA" to add a skills key after "positions". It should be like a dictionary with skills category as the keys based on the categories given in the resume. If there are no categories, then it should have a "default" category. The skills should a list of strings for each category. +Below are three examples - + +1. Extracted text: +""" +Languages: Python, C++, SQL +LLM/Agent Systems: Multi Agent Orchestration (Crew AI, Google ADK, LangGraph), Agentic Memory, + LangChain, LLM, APIs (OpenAI, Gemini, Claude), DeepEval +Frameworks / Libraries: FastAPI, Flask, PyTorch, Scikit-learn, OpenCV, Open3D, PyTest, +Cloud & Ops: Google Cloud Platform (GCP), Amazon Web Services (AWS), Git, Docker, + MLFlow, Github Actions +""" + +Desired skill section: +"skills":{ + "Languages": ["Python", "C++", "SQL"], + "LLM/Agent Systems": ["Crew AI", "Google ADK", "LangGraph", "Agentic Memory", "LangChain", "LLM", "OpenAI", "Gemini", "Claude", "DeepEval"], + "Frameworks / Libraries": ["FastAPI", "Flask", "Pytorch", "Scikit-learn", "OpenCV", "Open3D", "PyTest"], + "Cloud & Ops": ["Google Cloud Platform (GCP)", "Amazon Web Services (AWS)", "Git", "Docker", "MLFlow", "Github Actions"] +} + +2. Extracted Text: +""" +Programming: Python, C, C++, SQL, Core Java +Tools & Technologies: TensorFlow, PyTorch, Scikit-learn, NumPy, Pandas, Matplotlib, Seaborn, NLTK, spaCy, Transformers, Git, Docker, Linux, PostgreSQL, Elasticsearch, GitHub Actions, Azure, DigitalOcean +""" + +Desired skill section: +"skills":{ + "Programming": ["Python", "C", "C++", "SQL", "Core Java"], + "Tools & Technologies": ["TensorFlow", "PyTorch", "Scikit-learn", "NumPy", "Pandas", "Matplotlib", "Seaborn", "NLTK", "spaCy", "Transformers", "Git", "Docker", "Linux", "PostgreSQL", "Elasticsearch", "GitHub Actions", "Azure", "DigitalOcean"] +} + +3. Extracted Text: +""" +Python, HTML, CSS, JavaScript, React, Node.js, AWS, GCP +""" + +Desired skill section: +"skills":{ + "Default": ["Python", "HTML", "CSS", "JavaScript", "React", "Node.js", "AWS", "GCP"] +} + +Also Modify "_resume_analysis_task" and "resume_analyzer" descriptions if necessary. + +Also apply a guardrail to make sure Agent does not hallucinate. See the documentation given by CrewAI here - "https://docs.crewai.com/v1.15.1/en/enterprise/features/hallucination-guardrail.md" \ No newline at end of file diff --git a/backend/docs/skill_extractor_agent_creation_prompt.txt b/backend/docs/skill_extractor_agent_creation_prompt.txt new file mode 100644 index 0000000..60d0e98 --- /dev/null +++ b/backend/docs/skill_extractor_agent_creation_prompt.txt @@ -0,0 +1,173 @@ +Create a new Crew AI Agent called "skill_extractor" that can extract skills and certifications from a given text like resume, job description or a github profile. It should carefully scan the entire text and find out keywords that match to a skill or certification. Keep in mind that certifications can only be extracted from resumes and not job description or github profiles. Also soft skills can only be extracted from resumes and job_descriptions but not github profiles. It needs to produce the output in a json format as shown below - +""" +{ + "data_source": "resume" or "job description" or "github profile" + "skills": { + "programming_languages": [], + "frameworks": [], + "operating_systems": [], + "cloud_services": [], + "tools": [], + "libraries": [], + "soft_skills": [], + "tech_skills": [], + }, + "certifications": { + "certificate_1": { + "title": "", + "issuer": "", + "issue_date": "", + "expiry_date": "", or "N/A" if it does not expire + "verify_link": "" or "N/A" if not found + }, + "certificate_2": { + "title": + "issuer": + "issue_date": + "expiry_date": + "verify_link": + }, + ... + "certificate_n": { + "title": + "issuer": + "issue_date": + "expiry_date": + "verify_link": + }, + } +} +""" + +Below are examples of complete sample JSONs. +1. +{ + "data_source": "resume", + "skills": { + "programming_languages": ["Python", "C++", "SQL"], + "frameworks": ["TensorFlow", "PyTorch", "Keras"], + "operating_systems": ["Ubuntu Linux", "macOS"], + "cloud_services": ["AWS SageMaker", "Google Cloud AI Platform"], + "tools": ["Docker", "Git", "MLflow", "Jupyter"], + "libraries": ["Scikit-learn", "Pandas", "NumPy", "Matplotlib"], + "soft_skills": ["Problem Solving", "Cross-functional Communication", "Adaptability"], + "tech_skills": ["Model Deployment", "Deep Learning", "Computer Vision", "NLP"] + }, + "certifications": { + "certificate_1": { + "title": "AWS Certified Machine Learning - Specialty", + "issuer": "Amazon Web Services", + "issue_date": "2023-05-15", + "expiry_date": "2026-05-15", + "verify_link": "https://www.credly.com/badges/example-badge-id-ml" + }, + "certificate_2": { + "title": "Deep Learning Specialization", + "issuer": "Coursera (DeepLearning.AI)", + "issue_date": "2022-08-10", + "expiry_date": "N/A", + "verify_link": "https://www.coursera.org/account/accomplishments/specialization/example" + } + } +} + +2. +{ + "data_source": "resume", + "skills": { + "programming_languages": ["Python", "Bash"], + "frameworks": ["Ansible"], + "operating_systems": ["Cisco IOS", "Junos OS", "CentOS"], + "cloud_services": ["AWS VPC", "Azure Virtual Network"], + "tools": ["Wireshark", "SolarWinds", "Cisco Packet Tracer", "Putty"], + "libraries": ["Netmiko", "NAPALM"], + "soft_skills": ["Critical Thinking", "Under Pressure Troubleshooting", "Team Leadership"], + "tech_skills": ["BGP/OSPF Routing", "VLAN/STP Switching", "Firewall Configuration", "Network Automation"] + }, + "certifications": { + "certificate_1": { + "title": "Cisco Certified Network Professional (CCNP) Enterprise", + "issuer": "Cisco", + "issue_date": "2022-11-20", + "expiry_date": "2025-11-20", + "verify_link": "https://www.credly.com/badges/example-badge-id-ccnp" + }, + "certificate_2": { + "title": "CompTIA Network+", + "issuer": "CompTIA", + "issue_date": "2019-04-12", + "expiry_date": "2022-04-12", + "verify_link": "N/A" + } + } +} + +3. +{ + "data_source": "job description", + "skills": { + "programming_languages": ["Python", "R", "Scala", "SQL"], + "frameworks": ["Apache Spark", "Hadoop"], + "operating_systems": ["Linux", "Windows"], + "cloud_services": ["AWS", "GCP BigQuery"], + "tools": ["Tableau", "PowerBI", "Git", "Airflow"], + "libraries": ["SciPy", "Statsmodels", "Seaborn", "NLTK"], + "soft_skills": ["Storytelling", "Business Acumen", "Analytical Thinking"], + "tech_skills": ["Statistical Modeling", "A/B Testing", "Data Mining", "Predictive Analytics"] + }, + "certifications": "N/A" +} + +4. +{ + "data_source": "job description", + "skills": { + "programming_languages": ["Python", "Go", "PowerShell", "C"], + "frameworks": ["Metasploit Framework", "MITRE ATT&CK"], + "operating_systems": ["Kali Linux", "Windows Server", "Debian"], + "cloud_services": ["Azure Security Center", "AWS Shield"], + "tools": ["Burp Suite", "Nessus", "Splunk", "Wireshark"], + "libraries": ["OpenSSL", "Cryptography"], + "soft_skills": ["Attention to Detail", "Ethics and Integrity", "Clear Reporting"], + "tech_skills": ["Penetration Testing", "Incident Response", "Vulnerability Assessment", "Cryptography"] + }, + "certifications": "N/A" +} + +5. +{ + "data_source": "github profile", + "skills": { + "programming_languages": ["JavaScript", "TypeScript", "HTML5", "CSS3"], + "frameworks": ["React.js", "Vue.js", "Next.js"], + "operating_systems": ["macOS", "Windows"], + "cloud_services": ["Vercel", "Netlify", "Firebase"], + "tools": ["Webpack", "Vite", "Figma", "Git"], + "libraries": ["Redux", "Tailwind CSS", "Framer Motion", "React Query"], + "soft_skills": "N/A", + "tech_skills": ["Responsive Web Design", "Web Accessibility (a11y)", "State Management", "SEO Optimization"] + }, + "certifications": "N/A" +} + +6. +{ + "data_source": "github profile", + "skills": { + "programming_languages": ["JavaScript", "Python", "Java", "SQL"], + "frameworks": ["Node.js", "Express.js", "Django", "Spring Boot", "React"], + "operating_systems": ["Ubuntu", "macOS"], + "cloud_services": ["AWS EC2", "AWS S3", "Heroku"], + "tools": ["Docker", "Jenkins", "Postman", "Git"], + "libraries": ["Mongoose", "Axios", "Bcrypt", "Jest"], + "soft_skills": "N/A", + "tech_skills": ["RESTful APIs", "Microservices Architecture", "CI/CD Pipelines", "Database Design (SQL/NoSQL)"] + }, + "certifications": "N/A" +} + + + +Create a well defined task for the same. + +Now instruct the \ No newline at end of file diff --git a/backend/pyproject.toml b/backend/pyproject.toml index 130c70f..c3d3ff4 100644 --- a/backend/pyproject.toml +++ b/backend/pyproject.toml @@ -41,6 +41,7 @@ dependencies = [ "beautifulsoup4", "openpyxl", "pandas", + "pdfplumber", "pypdf", "python-docx", "requests", diff --git a/backend/sample_data/Arijit De Resume 2026.docx b/backend/sample_data/Arijit De Resume 2026.docx new file mode 100644 index 0000000000000000000000000000000000000000..e2fa6c7713f3b9d999beca8c3ce0dcbb923d5ada GIT binary patch literal 465216 zcma%hQ;?`#vSrz}ZQE6+Y}>Z&I%V6oZQJH4+qPYE{_g0B`*M5cd)V>Ce%qOAC03@q 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Scholar](https://scholar.google.com/citations?user=mCaigGsAAAAJ&hl=en) [Homepage](https://arijitde92.github.io) | -| :---- | :---: | ----: | - -## **Work Experience** - -**Machine Learning Engineer mVizn Pte. Ltd.** **Jan 2024 – Present** -3D Scan-to-BIM Project Singapore (Remote) - -* Led the development of a comprehensive Deep Learning pipeline for semantic segmentation of 3D point clouds using Python and PyTorch by researching and implementing state of the art models. -* Enhanced model performance by 30% and improved model robustness significantly by extracting relevant features from the data and incorporating data augmentation techniques. -* Led backend development efforts and deployed AI models in GCP using VMs and Cloud Storage, integrating Python and MySQL, which resulted in a 60% improvement in the client’s system performance and scalability. -* Implemented role-based access control (RBAC) using GCP Identity and Access Management (IAM) to securely manage user permissions for the dashboard that monitored and visualized ML model outputs. -* Resolved over 10 critical issues post-deployment by troubleshooting AI model behavior; collaborated with 5+ stakeholders to align technical solutions with business requirements and enhance user experience. - -**Machine Learning Engineer Mercedes-Benz Research and Development India** **Aug 2018 – Jan 2020** -Advanced driver-assistance system (ADAS) Bengaluru, India - -* Developed a suite of manual quality control tools using PyQt to validate over 10,000 annotated images for Vulnerable Road User (VRU) detection, improving annotation accuracy by 25%. -* Automated quality checks and data analysis/visualization of annotated data using python scripts which increased annotation throughput by 20%. -* Created an AI system to detect VRU by training Yolo v3 model with the above mentioned annotated images that helped in improving Level 3 ADAS vehicle capabilities by 30%. - -**Systems Engineer Tata Consultancy Services** **Aug 2014 – August 2015** -Ultimatix Team Kolkata, India - -* Maintained and enhanced TCS’s internal website, “Ultimatix,” by implementing over 30 feature requests across frontend (HTML, Bootstrap CSS, JavaScript) and backend (Java, MySQL) components, ensuring seamless functionality for 100,000+ users. -* Collaborated with cross-functional teams, including QA and UI/UX designers, to streamline the feature release cycle, reducing deployment time by 20%. - -## **Skills** - -* Languages: Python, C++, SQL -* Technologies: PyTorch, Tensorflow, Scikit-Learn, OpenCV, Numpy, Pandas, Flask, Git, API -* Cloud: Google Cloud Platform (GCP) -* Other: Agile methodology, Problem solving attitude, Collaborative, Punctual. - -## **Education** - -* PhD, Jadavpur University, Kolkata, India. 2020–2024 -* M.Tech. Computer Science & Engineering, Jadavpur University, Kolkata, India. GPA \- 8.83 2016–2018 -* B.Tech. Computer Science & Engineering, Techno India, Kolkata, India. GPA \- 8.81 2010–2014 - -## **Projects** - -* **Online Assignment Submission Portal** (Oct '24 \- Nov ‘24) \- A Flask \+ MySQL WebApp for upload, submission & checking of C programming assignments. Teachers can create assignments whereas students can view those assignments, submit the code and get it evaluated with a built in GCC compiler.. - Live link \- [https://subsequent-heath-arijit-home-3b9d0400.koyeb.app/](https://subsequent-heath-arijit-home-3b9d0400.koyeb.app/) - Github \- [https://github.com/arijitde92/Online\_Programming\_Assignment\_Portal](https://github.com/arijitde92/Online_Programming_Assignment_Portal) -* **Spiritual Chatbot** (Jan’24 \- Feb’24) \- An LLM chatbot that personifies a Hindu God. Uses OpenAI GPT-3.5 turbo model APIs and Langchain for creating prompt templates. Attempted prompt engineering techniques to make chatbot stay within context and purpose. The front end was built using streamlit while python was at backend. - Live link \- [https://spiritualchatbot.streamlit.app/](https://spiritualchatbot.streamlit.app/) Github \- [https://github.com/arijitde92/SpiritualChatBot](https://github.com/arijitde92/SpiritualChatBot) -* **Github Code Analysis Tool** (Apr' 23 \- Jun' 23\) \- An LLM based github repository analyzer that can tell the most technically complex repository among all the repositories of a given github user. Built using OpenAI GPT3.5 turbo, Langchain, DeepLake vector database, Python and Flask. - Github \- [https://github.com/arijitde92/Github\_Code\_Analysis\_Tool](https://github.com/arijitde92/Github_Code_Analysis_Tool) -* **3D Brain Analysis and Visualization App** (Jul’24 \- Aug’24) \- A Flask Application that can segment the hippocampus or a brain tumor from a 3D brain MRI scan. The segmentation models are built using Hippocampus Segmentation Factory (HSF) for Hippocampus Segmentation and a 3D Attention UNet model for brain tumor segmentation. - Github \- [https://github.com/arijitde92/Brain\_Seg\_App](https://github.com/arijitde92/Brain_Seg_App) - -## **Publications** - -1. **A. De**, A. S. Chowdhury, (2025). Shape Induced Multi-class Deep Graph Cut for Hippocampus Subfield Segmentation. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. (***ICPR 2024\)***. Lecture Notes in Computer Science, vol 15313\. Springer, Cham. https://doi.org/10.1007/978-3-031-78201-5\_16 -2. **A. De**, N. Das, PK. Saha, A Comellas, E Hoffman, S Basu, T Chakraborti. MSO-GP: 3-D segmentation of large and complex conjoined tree structures. ***Methods**. **2024*** June 3;229:9-16. doi: 10.1016/j.ymeth.2024.05.016. Epub ahead of print. PMID: 38838947\. -3. **A. De**, P. Ebenezer, "Sleep Apnea sub-type detection from Polysomnography signals," 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (***IATMSI 2024***), Gwalior, India, 2024, pp. 1-6 -4. **A. De**, M. Tiwari, & A. S. Chowdhury. 3D Hippocampus Segmentation Using a Hog Based Loss Function with Majority Pooling. In 2023 IEEE International Conference on Image Processing (***ICIP***), Kuala Lumpur, Malaysia ***2023***, October, pp. 2260-2264. -5. **A. De**, R. Mhatre, M. Tiwari and A. S. Chowdhury, "Brain Tumor Classification from Radiology and Histopathology using Deep Features and Graph Convolutional Network," 2022 26th International Conference on Pattern Recognition (***ICPR 2022***), Montreal, QC, Canada, *2022*, pp. 4420-4426 -6. **A. De**, M. Tiwari, E. Grisan, A.S. Chowdhury, "A Deep Graph Cut Model for 3D Brain Tumor Segmentation", 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (***EMBC 2022***). -7. **A. De**, A.S. Chowdhury, "DTI based Alzheimer's Disease Classification with Rank Modulated Fusion of CNNs and Random Forest", ***Expert Syst. Appl**.* 169 (*2021*), 114338\. DOI: 10.1016/j.eswa.2020.114338 - -## **Voluntary Activities** - -* Selected as a mentee for the IEEE EMBS Student Mentorship Program (SMP) 2023, gaining hands-on experience in healthcare-related signal processing. -* Contributed to research on sleep disorder diagnostics and co-authored a publication on Sleep Apnea Detection using Machine Learning, achieving a 94.26% accuracy in classifying four stages of Sleep Apnea. - -## **Open Source Contributions** - -* Contributed to [AI-Code](https://github.com/Avdhesh-Varshney/AI-Code/pull/30) as part of Social Summer of Code (**SSoC**) 2024 & [DL-Simplified](https://github.com/abhisheks008/DL-Simplified/pull/451) as part of Social Winter of Code (**SWoC**) 2023 Hackathons. -* Contributed to [nexB](https://github.com/nexB) and [TheAlgorithms](https://github.com/TheAlgorithms/Python) repositories pertaining to both feature enhancements and documentations. - - -## **Certifications** - -* Deep Learning, a 5-course specialization, by Deeplearning.ai. Verify [here](https://coursera.org/share/9cdc757ce1bb41134bb22a7548aff40b). -* TensorFlow in Practice Specialization, by Deeplearning.ai. Verify [here](https://coursera.org/share/be06afd8c51589a64831567f8424fe9b). -* MCPS: Microsoft Certified Professional, Microsoft Certification No.: 1042C4-5DHF30 \ No newline at end of file diff --git a/backend/sample_data/Yash_Pal_Resume.pdf b/backend/sample_data/Yash_Pal_Resume.pdf new file mode 100644 index 0000000000000000000000000000000000000000..8d24d1dc4357a15c25bd65fce3b78d7fc7782eee GIT binary patch literal 173386 zcmeFYQZLYFy+qP}nwr#JnZM$lfZQFL$S>OKmzuP`|I8XOJ+?(05#T*em zBWCnDN1r)jM3Tr0i_$RAvOtmaz7&2#F%i%c*cn{Iv)e z*qE5uSqbRG3A6|p{yAb`XCcrbppzqDBcK!c+sw$u@fRrj4_f&jGy^>&0WU9Lva@!vv2`N&i{!i-noNnZVj zun6Ng@GWXk(vpD^nDi6@l+&+tVt|);U?U2Sr-=YO1c==KT>bwX{r`>o;D4p{|F{1? z%mCCs4)||%lQpn0ar*0qtp9RKIeQaZ0V8J%J6i&#|MXco7ia6g68H~@@n0Z$12Yqu z|4<1PCzJnbru(P*uRyT<3-m9w5i)Q#u(mV%Z~FJoK>r^6|EN4l1WbR`Rqn4g{Uf*k zsZ;dy|Fi$<>&NTs>ref4VEs&|5I`_}YgGbM1AYKVa#Q{EDT2PfJ~KkC0@I=aHNE5d zSwo+sq7McM(wffb>}bkp$>+4cfEO}=AulAt z|J`B#nb*HhT)@`W&e`c7mHQVZO#eJE6bYFAk=wt;zZCB8H2$&vYNC*xt+R>k-!HKL 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Returns True on success.""" + # Imports that need sys.path/.env ready. + from crewai import Crew + + from app.services.crew.agents import resume_analyzer + from app.services.crew.resume_tools import ResumeAnalysisContext + from app.services.crew.tasks import _resume_analysis_task + from app.services.resume_parser import ( + ParsedResume, + ResumeParsingError, + extract_resume_text, + ) + + # 1. Extract text (pypdf → pdfplumber fallback / python-docx / raw md). + print(f"Extracting text from: {resume_file}") + try: + resume_text = extract_resume_text(str(resume_file)) + except ResumeParsingError as e: + print(f"ERROR: resume parsing failed: {e}") + return False + print(f"Extracted {len(resume_text)} characters.") + + tmp_md = tempfile.NamedTemporaryFile( + mode="w", suffix=".md", prefix="test_resume_", delete=False, encoding="utf-8" + ) + with tmp_md as f: + f.write(resume_text) + + try: + # 2. Run the agent + task in isolation. local_only=True → the save + # tool writes to temp storage only (no GCS upload, no DB write). + ctx = ResumeAnalysisContext( + user_id=0, resume_id=0, user_name="test_user", local_only=True, + resume_text=resume_text, + ) + task = _resume_analysis_task(ctx, run_async=False) + crew = Crew(agents=[resume_analyzer], tasks=[task], verbose=True) + crew.kickoff(inputs={"resume_path": tmp_md.name}) + + if not ctx.state.get("saved"): + print("FAILED: the agent never saved the parsed resume.") + return False + + # 3. Validate and copy to tests/output/. + parsed = ParsedResume.model_validate_json(ctx.state["json"]) + OUTPUT_DIR.mkdir(parents=True, exist_ok=True) + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + output_path = ( + OUTPUT_DIR + / f"test_parsed_resume_{_sanitize_stem(resume_file.stem)}_{timestamp}.json" + ) + shutil.copy(ctx.state["local_path"], output_path) + + result = parsed.result + filled = sum( + 1 for value in result.model_dump().values() + if value not in ("", None, [], False, 0) + ) + print("\n" + "=" * 70) + print(f"SUCCESS — parsed resume copied to: {output_path}") + print(f" name: {result.name!r}") + print(f" email: {result.email!r}") + print(f" positions: {len(result.positions)}") + print(f" education: {len(result.education_qualifications)}") + print(f" projects: {len(result.projects)}") + print(f" pubs: {len(result.publications)}") + print(f" years_exp: {result.years_of_experience}") + print(f" filled top-level fields: {filled}/{len(result.model_dump())}") + print("=" * 70) + return True + finally: + if os.path.exists(tmp_md.name): + os.unlink(tmp_md.name) + + +def main() -> int: + parser = argparse.ArgumentParser(description="Run the resume_analyzer agent in isolation.") + parser.add_argument( + "--resume", + default=None, + help="Path to a single .pdf/.docx/.md resume. Omit to run all three " + "sample resumes.", + ) + args = parser.parse_args() + + if not os.environ.get("GEMINI_API_KEY"): + print("ERROR: GEMINI_API_KEY is not set (expected in backend/.env).") + return 1 + + # Resolve the list of resumes to process. + if args.resume: + resume_file = Path(args.resume) + if not resume_file.is_absolute(): + resume_file = BACKEND_DIR / resume_file + resumes = [resume_file] + else: + resumes = list(DEFAULT_RESUMES) + + results: list[tuple[Path, bool]] = [] + for i, resume_file in enumerate(resumes, start=1): + print("\n" + "#" * 70) + print(f"# Resume {i}/{len(resumes)}: {resume_file.name}") + print("#" * 70) + if not resume_file.exists(): + print(f"ERROR: resume file not found: {resume_file}") + results.append((resume_file, False)) + continue + try: + ok = analyze_resume(resume_file) + except Exception as e: # noqa: BLE001 — one bad resume shouldn't stop the rest + print(f"ERROR: unexpected failure on {resume_file.name}: {e}") + ok = False + results.append((resume_file, ok)) + + # Summary. + print("\n" + "=" * 70) + print("SUMMARY") + for resume_file, ok in results: + print(f" [{'PASS' if ok else 'FAIL'}] {resume_file.name}") + passed = sum(1 for _, ok in results if ok) + print(f" {passed}/{len(results)} succeeded") + print("=" * 70) + + return 0 if passed == len(results) else 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/backend/uv.lock b/backend/uv.lock index 7c51ca3..63cb2ac 100644 --- a/backend/uv.lock +++ b/backend/uv.lock @@ -377,6 +377,22 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/0d/fe/6bea5c9162869c5beba5d9c8abbed835ec85bf1ec1fba05a3822325c45f3/build-1.5.0-py3-none-any.whl", hash = "sha256:13f3eecb844759ab66efec90ca17639bbf14dc06cb2fdf37a9010322d9c50a6f", size = 26018, upload-time = "2026-04-30T03:18:23.644Z" }, ] +[[package]] +name = "cel-python" +version = "0.5.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "google-re2" }, + { name = 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profile' }, { key: 'tailoring_resume', label: 'Tailoring your resume' }, diff --git a/frontend/src/components/ResumeUpload.jsx b/frontend/src/components/ResumeUpload.jsx index 5e8978a..a1609b6 100644 --- a/frontend/src/components/ResumeUpload.jsx +++ b/frontend/src/components/ResumeUpload.jsx @@ -1,6 +1,6 @@ import { useState, useRef } from 'react'; import api from '../services/api'; -import { FiUploadCloud, FiFile, FiTrash2, FiExternalLink, FiEye, FiDownload } from 'react-icons/fi'; +import { FiUploadCloud, FiFile, FiTrash2, FiEye, FiDownload } from 'react-icons/fi'; import ResumePreviewModal from './ResumePreviewModal'; export default function ResumeUpload({ resumes, onRefresh }) { @@ -12,8 +12,8 @@ export default function ResumeUpload({ resumes, onRefresh }) { const handleUpload = async (file) => { if (!file) return; - if (!file.name.endsWith('.md')) { - setError('Only Markdown (.md) files are accepted.'); + if (!/\.(md|pdf|docx)$/i.test(file.name)) { + setError('Only Markdown (.md), PDF (.pdf) or Word (.docx) files are accepted.'); return; } setError(''); @@ -41,9 +41,17 @@ export default function ResumeUpload({ resumes, onRefresh }) { } }; + const isMarkdown = (name) => /\.md$/i.test(name || ''); + const handleDownload = async (resume) => { setError(''); try { + if (!isMarkdown(resume.original_filename)) { + // Binary resumes (.pdf/.docx) are served via a GCS signed URL. + const res = await api.get(`/resumes/${resume.id}/preview`); + window.open(res.data.signed_url, '_blank', 'noopener'); + return; + } const res = await api.get(`/resumes/${resume.id}/content`); const blob = new Blob([res.data.content], { type: 'text/markdown' }); const url = URL.createObjectURL(blob); @@ -59,6 +67,21 @@ export default function ResumeUpload({ resumes, onRefresh }) { } }; + const handleView = async (resume) => { + setError(''); + if (isMarkdown(resume.original_filename)) { + setPreviewId(resume.id); + return; + } + // The markdown modal can't render binaries — open the signed URL instead. + try { + const res = await api.get(`/resumes/${resume.id}/preview`); + window.open(res.data.signed_url, '_blank', 'noopener'); + } catch (err) { + setError(err.response?.data?.detail || 'Preview failed.'); + } + }; + const handleDrop = (e) => { e.preventDefault(); setDragActive(false); @@ -91,22 +114,12 @@ export default function ResumeUpload({ resumes, onRefresh }) { {uploading ? 'Uploading...' : 'Drag & drop or click to upload'}

- Only .md files accepted + .md, .pdf and .docx files accepted

- handleUpload(e.target.files[0])} /> - {/* Conversion links */} - - {error &&

{error}

} {/* Resume list */} @@ -123,7 +136,7 @@ export default function ResumeUpload({ resumes, onRefresh }) { {r.original_filename} -

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