Features/fastapi backend

backend.ppk

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backend/api.py

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+import fastapi
+import boto3
+import os
+import datetime
+import time
+import random
+from typing import List
+from pydantic import BaseModel
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+'''
+To-Do:
+- [ ] Connect to S3 w/ model
+- [ ] Create a function to load the model from S3
+- [ ] Create a function to predict using the model
+'''
+
+## Helper Functions
+def load_model_from_s3(model_name: str):
+    """
+    Download and load an ML model file from S3 into memory without persisting it to disk.
+
+    Uses AWS credentials from environment variables if available:
+    - AWS_ACCESS_KEY_ID
+    - AWS_SECRET_ACCESS_KEY
+    - AWS_SESSION_TOKEN (optional, for temporary credentials)
+
+    Falls back to IAM roles (if running on EC2, Lambda, ECS, etc.) or ~/.aws/credentials
+
+    Required environment variables:
+    - S3_MODEL_BUCKET: S3 bucket name
+    - AWS_REGION: AWS region (optional, defaults to us-east-1)
+
+    Args:
+        model_name (str): The key/path of the model file in the S3 bucket.
+
+    Returns:
+        The loaded model object.
+    """
+    s3_bucket = os.environ.get("S3_MODEL_BUCKET")
+    if not s3_bucket:
+        raise ValueError("S3_MODEL_BUCKET environment variable not set.")
+
+    region = os.environ.get("AWS_REGION", "us-east-1")
+
+    # Get AWS credentials from environment variables
+    aws_access_key_id = os.environ.get("AWS_ACCESS_KEY_ID")
+    aws_secret_access_key = os.environ.get("AWS_SECRET_ACCESS_KEY")
+    aws_session_token = os.environ.get("AWS_SESSION_TOKEN")
+
+    # Create boto3 client with explicit credentials if available
+    if aws_access_key_id and aws_secret_access_key:
+        s3 = boto3.client(
+            "s3",
+            aws_access_key_id=aws_access_key_id,
+            aws_secret_access_key=aws_secret_access_key,
+            aws_session_token=aws_session_token,
+            region_name=region
+        )
+    else:
+        # Fall back to default credential chain (IAM roles, ~/.aws/credentials, etc.)
+        s3 = boto3.client("s3", region_name=region)
+
+    # Download model object as bytes into memory
+    response = s3.get_object(Bucket=s3_bucket, Key=model_name)
+    model_bytes = response['Body'].read()
+    import pickle
+    model = pickle.loads(model_bytes)
+    return model
+
+def predict_using_model(model, data):
+    pass
+
+def serialize_for_dynamodb(data):
+    """
+    Recursively serialize data for DynamoDB.
+    Converts datetime objects to ISO format strings.
+    """
+    if isinstance(data, datetime.datetime):
+        return data.isoformat()
+    elif isinstance(data, dict):
+        return {k: serialize_for_dynamodb(v) for k, v in data.items()}
+    elif isinstance(data, list):
+        return [serialize_for_dynamodb(item) for item in data]
+    else:
+        return data
+
+def get_dynamodb_table():
+    """
+    Get a DynamoDB table resource with proper credentials.
+
+    Returns:
+        boto3 DynamoDB Table resource
+    """
+    table_name = os.environ.get("DDB_TABLE")
+    if not table_name:
+        raise ValueError("DDB_TABLE environment variable not set.")
+
+    region = os.environ.get("AWS_REGION", "us-east-1")
+
+    # Get AWS credentials from environment variables
+    aws_access_key_id = os.environ.get("AWS_ACCESS_KEY_ID")
+    aws_secret_access_key = os.environ.get("AWS_SECRET_ACCESS_KEY")
+    aws_session_token = os.environ.get("AWS_SESSION_TOKEN")
+
+    # Create boto3 session with explicit credentials if available
+    if aws_access_key_id and aws_secret_access_key:
+        session = boto3.Session(
+            aws_access_key_id=aws_access_key_id,
+            aws_secret_access_key=aws_secret_access_key,
+            aws_session_token=aws_session_token,
+            region_name=region
+        )
+        dynamodb = session.resource("dynamodb")
+    else:
+        # Fall back to default credential chain (IAM roles, ~/.aws/credentials, etc.)
+        dynamodb = boto3.resource("dynamodb", region_name=region)
+
+    return dynamodb.Table(table_name)
+
+def get_random_item_from_ddb():
+    """
+    Retrieve a random item from DynamoDB table.
+
+    Returns:
+        dict: A random item from the table, or None if table is empty
+    """
+    table = get_dynamodb_table()
+
+    # Scan the table to get all items
+    # Note: For very large tables, this could be expensive.
+    # Consider optimizing with pagination or sampling if needed.
+    response = table.scan()
+    items = response.get('Items', [])
+
+    # Handle pagination if there are more items
+    while 'LastEvaluatedKey' in response:
+        response = table.scan(ExclusiveStartKey=response['LastEvaluatedKey'])
+        items.extend(response.get('Items', []))
+
+    if not items:
+        return None
+
+    # Return a random item
+    return random.choice(items)
+
+def save_to_ddb(data):
+    """
+    Save or update a dictionary of data to DynamoDB.
+    Uses user_id (integer) as the primary key. If user_id already exists, the item will be updated.
+
+    Uses AWS credentials from environment variables if available:
+    - AWS_ACCESS_KEY_ID
+    - AWS_SECRET_ACCESS_KEY
+    - AWS_SESSION_TOKEN (optional, for temporary credentials)
+
+    Falls back to IAM roles (if running on EC2, Lambda, ECS, etc.) or ~/.aws/credentials
+
+    Required environment variables:
+    - DDB_TABLE: DynamoDB table name
+    - AWS_REGION: AWS region (optional, defaults to us-east-1)
+
+    Args:
+        data: Dictionary containing user_id (int) and other fields. user_id is used as primary key.
+    """
+    table = get_dynamodb_table()
+
+    # Serialize data for DynamoDB (convert datetime objects, etc.)
+    serialized_data = serialize_for_dynamodb(data)
+
+    # Ensure user_id exists (required as primary key)
+    if 'user_id' not in serialized_data:
+        raise ValueError("user_id is required in the request body")
+
+    # Map user_id to pred-id (the table's primary key field name)
+    # Keep user_id in the data as well for reference
+    serialized_data['pred-id'] = serialized_data['user_id']
+
+    # DynamoDB put_item will create a new item if pred-id doesn't exist,
+    # or update/replace the existing item if pred-id already exists
+    response = table.put_item(Item=serialized_data)
+    return response
+
+## API
+class MyFavorites(BaseModel):
+    items: List[str]
+
+class PredictionResponse(BaseModel):
+    user_id: int
+    req: MyFavorites
+    prediction: str
+
+app = fastapi.FastAPI()
+
+@app.get("/health")
+def health_check():
+    return {"status": "ok"}
+
+@app.get("/random")
+def get_random():
+    """
+    Get a random item from the DynamoDB table.
+
+    Returns:
+        dict: A random item from the table
+    """
+    random_item = get_random_item_from_ddb()
+    if random_item is None:
+        raise fastapi.HTTPException(status_code=404, detail="No items found in table")
+    return random_item
+
+@app.post("/predict")
+def predict(request: PredictionResponse):
+    # Automatically add current timestamp
+    data = request.model_dump()
+    data['timestamp'] = datetime.datetime.now(datetime.timezone.utc)
+    save_to_ddb(data)
+    return {"status": "ok"}