Mohamad bader AA

AI_ASSIST.md

@@ -4,31 +4,102 @@ Document one place you used an LLM during this assignment.
 
 ## The problem
 
-<!-- TODO: describe the specific problem you asked an LLM about.
-     Example: "My Streamlit KPI panel kept re-querying Postgres on every
-     sidebar interaction even though I wrapped run_query in @st.cache_data." -->
-
-TODO
+Generated side panel for streamlit dashboard using claude
 
 ## The prompt
 
-<!-- TODO: paste the exact prompt you sent to the LLM. -->
+"""NYC Taxi metrics dashboard — Week 11 assignment starter.
+Reads the Week 10 dbt mart ``fct_trips`` from Azure Postgres. The page
+setup and the ``run_query`` caching helper are already wired up (same
+pattern taught in "Building a Metrics Dashboard"). Your job: implement the
+three TODO-stubbed KPI queries below with your own SQL, then (Required tier)
+add the hour-of-day trend, freshness panel, and payment-type filter
+described in the assignment brief.
+"""
+import os
+import pandas as pd
+import sqlalchemy
+import streamlit as st
+from dotenv import load_dotenv
+load_dotenv()  # reads .env file if present
+POSTGRES_URL = os.environ["POSTGRES_URL"]
+DB_SCHEMA = os.environ.get("DB_SCHEMA", "dev_yourname")
+st.set_page_config(page_title="NYC Taxi Metrics", layout="wide")
+st.title("NYC Taxi Metrics")
+def get_engine() -> sqlalchemy.engine.Engine:
+    return sqlalchemy.create_engine(POSTGRES_URL)
+@st.cache_data(ttl=300)
+def run_query(sql: str) -> pd.DataFrame:
+    with get_engine().connect() as conn:
+        return pd.read_sql(sql, conn)
+    st.subheader("Headline KPIs")
+kpis = run_query(f"""
+    SELECT COUNT(*)          AS trip_count,
+           AVG(fare_amount)  AS avg_fare,
+           SUM(fare_amount)  AS total_fare
+    FROM {DB_SCHEMA}.fct_trips
+""").iloc[0]
+col1, col2, col3 = st.columns(3)
+col1.metric("Total trips", f"{int(kpis['trip_count']):,}")
+col2.metric("Average fare", f"${kpis['avg_fare']:.2f}")
+col3.metric("Total revenue", f"${kpis['total_fare']:,.0f}")
+st.subheader("Trips by Hour of Day")
+hour_df = run_query(f"""
+    select
+        extract(hour from pickup_datetime)::int as pickup_hour,
+        count(*) as trip_count
+    from {DB_SCHEMA}.fct_trips
+    {where_clause}
+    group by 1
+    order by 1
+""")
+if hour_df.empty:
+    st.info("No trips found for this filter.")
+else:
+    hour_df = hour_df.set_index("pickup_hour").reindex(range(24), fill_value=0)
+    st.line_chart(hour_df["trip_count"])
 
-TODO
+st.subheader("Data freshness")
+fresh = run_query(f"""
+    SELECT COUNT(*)              AS row_count,
+           MAX(pickup_datetime)  AS last_pickup
+    FROM {DB_SCHEMA}.fct_trips
+""").iloc[0]
+col1, col2 = st.columns(2)
+col1.metric("Row count", f"{int(fresh['row_count']):,}")
+col2.metric(
+    "Last pickup", str(fresh["last_pickup"])[:16] if fresh["last_pickup"] else "unknown"
+)
+# TODO: query total trip count, average trip_distance, and average
+# fare_per_mile from {DB_SCHEMA}.fct_trips through run_query(), then
+# render three tiles side by side with st.columns(3) and .metric().
+# This is deliberately not the total-trips/avg-fare/total-revenue trio
+# from the chapter: trip_distance and fare_per_mile are different columns,
+# so copying the chapter's SQL verbatim will not answer this.
 
-## The response
+i want toAdd a sidebar 
+st.selectbox
+ to filter every panel by 
+payment_type_label
+ (not 
+pickup_borough
+, which Building a Metrics Dashboard already covers).
 
-<!-- TODO: summarise or paste what the LLM returned. -->
 
-TODO
+## The response
+
+LLM added alittle bit of fluff to the file with extra comments and lines
+added every single todo in the file as it is following instructions from the code itself.
+and also added a side panel that works 
+then gave extra information about flags that i might want to change
 
 ## Reflection
 
-<!-- TODO: what did you change, keep, or discard after reviewing the LLM's answer?
-     Be specific: "I kept the cache_data suggestion but changed ttl from 60 to 300
-     to match the mart's once-a-day refresh cadence." -->
+discarded most of what AI suggested most importantly all the fluff around it. 
+used the panel that AI suggested and it worked out of the box as all the information was mentioned inside the code.
+ kept some of the comments as they make the code look much nicer
 
-TODO
+ 
 
 ---
 

README.md

@@ -48,13 +48,10 @@ Fill in `week11-streamlit/metric_definitions.md`: a five-field definition (name,
 
 ## My submission
 
-<!-- TODO: 1) Save your Metabase dashboard into the shared "Week 11 Submissions" collection.
-     2) Paste its link below, plus screenshots or a PDF export in this repo.
-     3) Paste your 5-minute presentation recording link (keep it PRIVATE). -->
+1) Metabase dashboard : https://metabase-hyf.politepebble-abd3ebc2.westeurope.azurecontainerapps.io/dashboard/45-nyc-taxi-analytics-bader
+     2) PDF export and screenshots: In docs folder
+     3) Presentation recording: https://hyfstoragedev.blob.core.windows.net/student-submissions/week-11/mohamad-bader-almsaddi-alzin.mp4?sp=r&st=2026-07-16T19:27:11Z&se=2026-07-23T03:42:11Z&skoid=93f1bc1c-033f-4ed7-bcef-086bdff0302a&sktid=07a14c4e-d88c-42f7-83b3-13af7e57ff3d&skt=2026-07-16T19:27:11Z&ske=2026-07-23T03:42:11Z&sks=b&skv=2026-02-06&spr=https&sv=2026-02-06&sr=b&sig=KhzhGPWxB7ZdMpJI15KGTaf%2BrCGivOwB5wyfLzDojIA%3D.
 
-- Metabase dashboard (in the **Week 11 Submissions** collection): TODO
-- Screenshots / PDF export: TODO
-- Presentation recording (private, hosted in the Azure `student-submissions` container): TODO
 
 > ⚠️ **Keep the recording private.** It shows your screen and voice. Never make it public and never commit the `.mp4` (git history is forever). Check the frame for passwords, `.env` contents, or connection strings before uploading.
 

week11-streamlit/app.py

@@ -24,22 +24,88 @@
 st.title("NYC Taxi Metrics")
 
 
+def get_engine() -> sqlalchemy.engine.Engine:
+    return sqlalchemy.create_engine(POSTGRES_URL)
+
+
 @st.cache_data(ttl=300)
 def run_query(sql: str) -> pd.DataFrame:
-    engine = sqlalchemy.create_engine(POSTGRES_URL)
-    with engine.connect() as conn:
+    with get_engine().connect() as conn:
         return pd.read_sql(sql, conn)
 
+    # Sidebar filter — applies to every panel below
+
+
+# ---------------------------------------------------------------------------
+payment_options = ["All"] + run_query(f"""
+    select distinct payment_type_label
+    from {DB_SCHEMA}.fct_trips
+    where payment_type_label is not null
+    order by payment_type_label
+""")["payment_type_label"].tolist()
+
+selected_payment = st.sidebar.selectbox("Payment type", payment_options)
+
+where_clause = (
+    ""
+    if selected_payment == "All"
+    else f"where payment_type_label = '{selected_payment}'"
+)
+
 
 st.subheader("Headline KPIs")
+kpis = run_query(f"""
+    SELECT COUNT(*)          AS trip_count,
+           AVG(fare_amount)  AS avg_fare,
+           SUM(fare_amount)  AS total_fare
+    FROM {DB_SCHEMA}.fct_trips
+""").iloc[0]
+
+col1, col2, col3 = st.columns(3)
+col1.metric("Total trips", f"{int(kpis['trip_count']):,}")
+col2.metric("Average fare", f"${kpis['avg_fare']:.2f}")
+col3.metric("Total revenue", f"${kpis['total_fare']:,.0f}")
+
+st.subheader("Trips by Hour of Day")
+
+hour_df = run_query(f"""
+    select
+        extract(hour from pickup_datetime)::int as pickup_hour,
+        count(*) as trip_count
+    from {DB_SCHEMA}.fct_trips
+    {where_clause}
+    group by 1
+    order by 1
+""")
+
+if hour_df.empty:
+    st.info("No trips found for this filter.")
+else:
+    hour_df = hour_df.set_index("pickup_hour").reindex(range(24), fill_value=0)
+    st.line_chart(hour_df["trip_count"])
+
+
+# ---------------------------------------------------------------------------
+# Freshness panel
+# ---------------------------------------------------------------------------
+
+st.subheader("Data freshness")
+fresh = run_query(f"""
+    SELECT COUNT(*)              AS row_count,
+           MAX(pickup_datetime)  AS last_pickup
+    FROM {DB_SCHEMA}.fct_trips
+""").iloc[0]
+
+col1, col2 = st.columns(2)
+col1.metric("Row count", f"{int(fresh['row_count']):,}")
+col2.metric(
+    "Last pickup", str(fresh["last_pickup"])[:16] if fresh["last_pickup"] else "unknown"
+)
+
 
 # TODO: query total trip count, average trip_distance, and average
 # fare_per_mile from {DB_SCHEMA}.fct_trips through run_query(), then
 # render three tiles side by side with st.columns(3) and .metric().
 # This is deliberately not the total-trips/avg-fare/total-revenue trio
 # from the chapter: trip_distance and fare_per_mile are different columns,
 # so copying the chapter's SQL verbatim will not answer this.
-raise NotImplementedError(
-    "TODO: implement the headline KPIs panel (total trips, avg trip "
-    "distance, avg fare per mile) from fct_trips."
-)

week11-streamlit/metric_definitions.md

@@ -0,0 +1,60 @@
+# Metric definitions
+
+Five fields per metric: Name, Description, Calculation, Data source, Refresh frequency. One block per panel. Copy this file to `metric_definitions.md` inside your `week11-streamlit/` folder and fill it in.
+
+## Metabase panels
+
+
+### Panel 1: Trip count
+
+- **Name**: Trip count by payment type
+- **Description**: bar chart to understand which payment methods are most popular among riders.
+Y axis is trip count, X axis is payment type
+- **Calculation**: `COUNT(*)` grouped by `payment_type_label` where `payment_type_label IS NOT NULL`
+- **Data source**: dev_bader.fct_trips
+- **Refresh frequency**: once per day
+
+### Panel 2: Average fare
+
+- **Name**: Average Fare per Mile by Dropoff Borough
+- **Description**: Identify which destinations have the highest average fares per mile
+X is dropoff borough and Y is average fare per mile
+- **Calculation**: `AVG(fare_per_mile)` grouped by `dropoff_borough` where `dropoff_borough IS NOT NULL`
+- **Data source**: dev_bader.fct_trips
+- **Refresh frequency**: once per day
+
+### Panel 3: Average trip duration
+
+- **Name**: Average Trip Duration by hour of day
+- **Description**: Track how the average trip length in minutes during hours of the day
+X axis and Y are hours of the day,  Line is the average trip duration in minutes
+- **Calculation**: `AVG((dropoff_datetime - pickup_datetime) in minutes)` grouped by `EXTRACT(HOUR FROM pickup_datetime)`
+- **Data source**: dev_bader.fct_trips
+- **Refresh frequency**: once per day
+
+## Streamlit panels
+
+
+### Panel 1: Headline KPIs
+
+- **Name**: Total Trips, Average Trip Distance, Average Fare per Mile
+- **Description**: Count of trips, mean trip distance, and mean fare per mile
+- **Calculation**: `count(*)`, `avg(trip_distance)`, `avg(fare_per_mile)`
+- **Data source**: `dev_bader.fct_trips`
+- **Refresh frequency**: 5 min cache (`ttl=300`)
+
+### Panel 2: Trips by Hour of Day
+
+- **Name**: Trip Count by Pickup Hour
+- **Description**: Trip count grouped by hour of day
+- **Calculation**: `count(*) group by extract(hour from pickup_datetime)`
+- **Data source**: `dev_bader.fct_trips`
+- **Refresh frequency**: 5 min cache (`ttl=300`)
+
+### Panel 3: Data Freshness
+
+- **Name**: Row Count, Latest Pickup Timestamp
+- **Description**: Total rows and most recent pickup timestamp
+- **Calculation**: `count(*)`, `max(pickup_datetime)`
+- **Data source**: `dev_bader.fct_trips`
+- **Refresh frequency**: 5 min cache (`ttl=300`)