Baraah A.

AI_ASSIST.md

@@ -8,27 +8,43 @@ Document one place you used an LLM during this assignment.
      Example: "My Streamlit KPI panel kept re-querying Postgres on every
      sidebar interaction even though I wrapped run_query in @st.cache_data." -->
 
-TODO
+While building the Streamlit dashboard, my "Trips by Hour of Day" chart was not displaying correctly. I wanted to check whether my SQL query and the data format passed to st.line_chart() were correct.
 
 ## The prompt
 
 <!-- TODO: paste the exact prompt you sent to the LLM. -->
 
-TODO
+I have this Streamlit code for a line chart. The query runs successfully, but I want to make sure the data is in the correct format for st.line_chart(). Is there anything I should change?
+
+st.subheader("Trips by Hour of Day")
+
+hour_query = f"""
+SELECT
+    EXTRACT(HOUR FROM pickup_datetime) AS pickup_hour,
+    COUNT(*) AS trip_count
+FROM {DB_SCHEMA}.fct_trips
+{payment_filter}
+GROUP BY pickup_hour
+ORDER BY pickup_hour;
+"""
+
+hour_df = run_query(hour_query)
+
+st.line_chart(hour_df)
 
 ## The response
 
 <!-- TODO: summarise or paste what the LLM returned. -->
 
-TODO
+The LLM explained that st.line_chart() works best when the x-axis is used as the DataFrame index. It suggested converting pickup_hour to an integer and setting it as the index before creating the chart.
 
 ## 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." -->
 
-TODO
+I kept the suggestion to convert pickup_hour to an integer and used it as the DataFrame index before calling st.line_chart(). After making these changes, the chart displayed correctly and the hours appeared in the correct order.
 
 ---
 

README.md

@@ -53,9 +53,12 @@ Fill in `week11-streamlit/metric_definitions.md`: a five-field definition (name,
      3) Paste your 5-minute presentation recording link (keep it PRIVATE). -->
 
 - Metabase dashboard (in the **Week 11 Submissions** collection): TODO
-- Screenshots / PDF export: TODO
+https://metabase-hyf.politepebble-abd3ebc2.westeurope.azurecontainerapps.io/dashboard/42-nyc-taxi-analytics-baraah
+- Screenshots / PDF export: TODO Metabase_dashboard_baraah.png 
 - Presentation recording (private, hosted in the Azure `student-submissions` container): TODO
 
+https://hyfstoragedev.blob.core.windows.net/student-submissions/week-11/Baraah.mp4?sp=r&st=2026-07-16T15:33:03Z&se=2026-07-22T23:48:03Z&skoid=8bfd6dc9-8735-4bc5-8362-05ebddd6526b&sktid=07a14c4e-d88c-42f7-83b3-13af7e57ff3d&skt=2026-07-16T15:33:03Z&ske=2026-07-22T23:48:03Z&sks=b&skv=2026-02-06&spr=https&sv=2026-02-06&sr=b&sig=GRcWIO6iheJqLzk0s8zeJNoQLaZaSRw%2FGOD9scBdsYQ%3D
+
 > ⚠️ **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.
 
 Host the recording in Azure: upload the `.mp4` to the shared `student-submissions` blob container (teachers get read access, nothing is public) and put the read-only link in your PR. The container is shared, so name your file after yourself: `week-11/<your-name>.mp4` (e.g. `week-11/jane-doe.mp4`). See "Host the recording on Azure Blob Storage" in the Week 11 Assignment chapter for the `az` CLI and Portal steps.

week11-streamlit/app.py

@@ -18,7 +18,7 @@
 load_dotenv()  # reads .env file if present
 
 POSTGRES_URL = os.environ["POSTGRES_URL"]
-DB_SCHEMA = os.environ.get("DB_SCHEMA", "dev_yourname")
+DB_SCHEMA = os.environ.get("DB_SCHEMA", "dev_baraah")
 
 st.set_page_config(page_title="NYC Taxi Metrics", layout="wide")
 st.title("NYC Taxi Metrics")
@@ -30,6 +30,27 @@ def run_query(sql: str) -> pd.DataFrame:
     with engine.connect() as conn:
         return pd.read_sql(sql, conn)
 
+st.sidebar.header("Filters")
+
+payment_query = f"""
+SELECT DISTINCT payment_type_label
+FROM {DB_SCHEMA}.fct_trips
+ORDER BY payment_type_label;
+"""
+
+payment_df = run_query(payment_query)
+
+payment_options = ["All"] + payment_df["payment_type_label"].tolist()
+
+selected_payment = st.sidebar.selectbox(
+    "Payment Type",
+    payment_options
+)
+
+if selected_payment == "All":
+    payment_filter = ""
+else:
+    payment_filter = f"WHERE payment_type_label = '{selected_payment}'"
 
 st.subheader("Headline KPIs")
 
@@ -39,7 +60,88 @@ def run_query(sql: str) -> pd.DataFrame:
 # 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."
+# Query headline KPIs from fct_trips
+kpi_query = f"""
+SELECT
+    COUNT(*) AS total_trips,
+    AVG(trip_distance) AS avg_trip_distance,
+    AVG(fare_per_mile) AS avg_fare_per_mile
+FROM {DB_SCHEMA}.fct_trips
+{payment_filter};
+"""
+
+kpi_df = run_query(kpi_query)
+
+total_trips = int(kpi_df["total_trips"].iloc[0])
+avg_trip_distance = kpi_df["avg_trip_distance"].iloc[0]
+avg_fare_per_mile = kpi_df["avg_fare_per_mile"].iloc[0]
+
+
+col1, col2, col3 = st.columns(3)
+
+with col1:
+    st.metric(
+        "Total Trips",
+        f"{total_trips:,}"
+    )
+
+with col2:
+    st.metric(
+        "Average Trip Distance",
+        f"{avg_trip_distance:.2f} miles"
+    )
+
+with col3:
+    st.metric(
+        "Average Fare per Mile",
+        f"${avg_fare_per_mile:.2f}"
+    )
+
+st.subheader("Trips by Hour of Day")
+
+hour_query = f"""
+SELECT
+    EXTRACT(HOUR FROM pickup_datetime) AS pickup_hour,
+    COUNT(*) AS trip_count
+FROM {DB_SCHEMA}.fct_trips
+{payment_filter}
+GROUP BY pickup_hour
+ORDER BY pickup_hour;
+"""
+
+hour_df = run_query(hour_query)
+
+hour_df["pickup_hour"] = hour_df["pickup_hour"].astype(int)
+
+st.line_chart(
+    hour_df.set_index("pickup_hour")["trip_count"]
 )
+
+st.subheader("Data Freshness")
+
+freshness_query = f"""
+SELECT
+    COUNT(*) AS row_count,
+    MAX(pickup_datetime) AS latest_pickup_datetime
+FROM {DB_SCHEMA}.fct_trips
+{payment_filter};
+"""
+
+freshness_df = run_query(freshness_query)
+
+row_count = int(freshness_df["row_count"].iloc[0])
+latest_pickup = freshness_df["latest_pickup_datetime"].iloc[0]
+
+col1, col2 = st.columns(2)
+
+with col1:
+    st.metric(
+        "Rows in fct_trips",
+        f"{row_count:,}"
+    )
+
+with col2:
+    st.metric(
+        "Latest Pickup Datetime",
+        str(latest_pickup)
+    )

week11-streamlit/metric_definitions.md

@@ -0,0 +1,68 @@
+# 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
+
+<!-- One block per Question: trip count by payment type, average fare per
+     mile by dropoff borough, average trip duration by hour of day. -->
+
+### Panel 1: Trip Count by Payment Type
+
+- **Name**: trip_count_by_payment_type
+- **Description**: Total number of taxi trips grouped by payment type. This metric shows which payment methods are most commonly used by passengers.
+- **Calculation**: COUNT(*) grouped by payment_type_label.
+- **Data source**: dev_baraah.fct_trips (dbt mart)
+- **Refresh frequency**: Rebuilt once per day.
+
+### Panel 2: Average Fare per Mile by Dropoff Borough
+
+- **Name**: avg_fare_per_mile_by_dropoff_borough
+- **Description**: Average fare earned per mile for taxi trips grouped by dropoff borough. This metric compares fare efficiency across boroughs.
+- **Calculation**: AVG(fare_per_mile) grouped by dropoff_borough.
+- **Data source**: dev_baraah.fct_trips (dbt mart)
+- **Refresh frequency**: Rebuilt once per day.
+
+### Panel 3: Average Trip Duration by Hour of Day
+
+- **Name**: avg_trip_duration_by_hour
+- **Description**: Average trip duration in minutes grouped by pickup hour. This metric helps identify how trip duration changes throughout the day.
+- **Calculation**: AVG(EXTRACT(EPOCH FROM (dropoff_datetime - pickup_datetime)) / 60) grouped by EXTRACT(HOUR FROM pickup_datetime).
+- **Data source**: dev_baraah.fct_trips (dbt mart)
+- **Refresh frequency**: Rebuilt once per day.
+
+## Streamlit panels
+
+<!-- Headline KPIs panel: total trips, average trip distance, average
+     fare per mile. -->
+
+### Panel 1: Headline KPIs
+
+- **Name**: taxi_headline_kpis
+- **Description**: Displays the total trips, average trip distance, and average fare per mile.
+- **Calculation**: COUNT(*), AVG(trip_distance), and AVG(fare_per_mile).
+- **Data source**: dev_baraah.fct_trips (dbt mart)
+- **Refresh frequency**: Rebuilt once per day.
+
+
+### Panel 2: Trips by Hour of Day
+
+- **Name**: trips_by_hour
+- **Description**: Shows the number of taxi trips for each pickup hour.
+- **Calculation**: COUNT(*) grouped by EXTRACT(HOUR FROM pickup_datetime).
+- **Data source**: dev_baraah.fct_trips (dbt mart)
+- **Refresh frequency**: Rebuilt once per day.
+
+### Panel 3: Data Freshness
+
+- **Name**: taxi_data_freshness
+- **Description**: Shows the total number of rows and the latest pickup datetime to verify that the data is current.
+- **Calculation**: COUNT(*) and MAX(pickup_datetime).
+- **Data source**: dev_baraah.fct_trips (dbt mart)
+- **Refresh frequency**: Rebuilt once per day.
+
+
+
+<!-- Add more ### Panel blocks under either section if you build more (the Required tier adds
+     an hour-of-day trend and a freshness panel to Streamlit; a Metabase date filter is Extra,
+     bonus credit, not required). -->