Feature/monitoring dashboard

monitoring/Dockerfile

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+# Use Python 3.11 slim image as base
+FROM python:3.11-slim
+
+# Set working directory
+WORKDIR /app
+
+# Copy requirements file
+COPY requirements.txt .
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY app.py .
+
+# Expose Streamlit port
+EXPOSE 8501
+
+# Run Streamlit app
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+

monitoring/app.py

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+import streamlit as st
+import boto3
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+s3 = boto3.client('s3')
+dynamodb = boto3.client('dynamodb')
+
+# Helper functions
+def get_data_from_dynamodb(table_name):
+    response = dynamodb.scan(TableName=table_name)
+    return response['Items']
+
+def convert_to_df(items):
+    return pd.DataFrame(items)
+
+
+df = convert_to_df(get_data_from_dynamodb('prediction-logs'))
+
+# ---------------------------- Streamlit app ----------------------------
+st.title("Monitoring Dashboard")
+
+df = convert_to_df(get_data_from_dynamodb('prediction-logs'))
+
+# Convert columns to proper types
+df['datetime'] = pd.to_datetime(df['datetime'], errors='coerce')
+df['user_id'] = pd.to_numeric(df['user_id'], errors='coerce')
+df['prediction'] = df['prediction'].astype(str)
+if 'req' in df:
+    df['req'] = df['req'].astype(str)
+
+st.header('Prediction Latency Over Time')
+
+if 'latency' in df.columns:
+    # Plot latency over time if available
+    fig1, ax1 = plt.subplots()
+    sns.lineplot(x='datetime', y='latency', data=df, ax=ax1)
+    ax1.set_title('Prediction Latency Over Time')
+    st.pyplot(fig1)
+else:
+    st.info("No latency field present in data. Please ensure the backend logs the latency of predictions.")
+
+st.header('Prediction Distribution (Target Drift)')
+
+fig2, ax2 = plt.subplots()
+sns.countplot(x='prediction', data=df, ax=ax2)
+ax2.set_title('Distribution of Predicted Classes')
+st.pyplot(fig2)
+
+st.header('Collect User Feedback')
+
+st.write("Click below to rate the most recent model prediction and help track accuracy.")
+
+user_id_input = st.text_input("User ID", "")
+recent = None
+if user_id_input:
+    try:
+        uid = int(user_id_input)
+        cur_user_rows = df[df['user_id'] == uid]
+        if not cur_user_rows.empty:
+            recent = cur_user_rows.sort_values('datetime', ascending=False).iloc[0]  # get latest
+            st.write(f"Last prediction for User {user_id_input}:")
+            st.code(dict(recent), language='json')
+    except Exception:
+        st.warning("Please enter a valid numeric user ID.")
+
+if recent is not None:
+    feedback = st.radio("Are these recommendations relevant to you?", ['Yes', 'No'])
+    feedback_submitted = st.button("Submit Feedback")
+    if feedback_submitted:
+
+        feedback_table = 'prediction-feedback'
+        record = {
+            'user_id': {'N': str(recent['user_id'])},
+            'datetime': {'S': str(recent['datetime'])},
+            'prediction': {'S': str(recent['prediction'])},
+            'feedback': {'S': feedback}
+        }
+        try:
+            dynamodb.put_item(TableName=feedback_table, Item=record)
+            st.success("Thank you for your feedback!")
+        except Exception as e:
+            st.error(f"Failed to submit feedback: {e}")
+
+# Calculate live accuracy from feedback
+    feedback_items = convert_to_df(get_data_from_dynamodb('prediction-feedback'))
+    feedback_items['feedback'] = feedback_items['feedback'].astype(str)
+    if not feedback_items.empty:
+        acc = (feedback_items['feedback'] == 'Yes').mean()
+        st.metric("Live Model Accuracy (from feedback)", f"{acc:.2%}")
+    else:
+        st.info("No feedback yet; accuracy cannot be computed.")
+
+
+
+
+

monitoring/requirements.txt

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+streamlit
+boto3
+pandas
+matplotlib.pyplot
+seaborn