Update app.py

app.py

@@ -5,7 +5,6 @@ import plotly.express as px
 from stravalib.client import Client
 import google.generativeai as genai
 from dotenv import load_dotenv
-from datetime import datetime, timedelta
 
 # Load environment variables
 load_dotenv()
@@ -104,10 +103,9 @@ if 'access_token' in st.session_state:
         df = pd.DataFrame([{
             'name': activity.name,
             'distance': float(activity.distance) if activity.distance is not None else 0,
-            'moving_time': activity.moving_time.total_seconds() / 3600 if isinstance(activity.moving_time, timedelta) else float(activity.moving_time) / 3600 if activity.moving_time is not None else 0,
+            'moving_time': float(activity.moving_time) if activity.moving_time is not None else 0,
             'elevation_gain': float(activity.total_elevation_gain) if activity.total_elevation_gain is not None else 0,
             'type': str(activity.type),
-            'start_date': activity.start_date.replace(tzinfo=None) if activity.start_date is not None else None
         } for activity in activities])
         return df
 
@@ -128,7 +126,7 @@ if 'access_token' in st.session_state:
     # Basic stats
     st.subheader("🏅 Activity Stats")
     total_distance = df['distance'].sum() / 1000  # Convert to km
-    total_time = df['moving_time'].sum()
+    total_time = df['moving_time'].sum() / 3600  # Convert to hours
     total_elevation = df['elevation_gain'].sum()
     
     col1, col2, col3 = st.columns(3)
@@ -139,24 +137,12 @@ if 'access_token' in st.session_state:
     # Visualizations
     st.subheader("📈 Activity Visualizations")
     
-    col1, col2 = st.columns(2)
-    
-    with col1:
-        # Distance by activity type
-        fig_distance = px.bar(df.groupby('type').sum().reset_index(), 
-                              x='type', y='distance', 
-                              title="Total Distance by Activity Type")
-        fig_distance.update_layout(xaxis_title="Activity Type", yaxis_title="Distance (m)")
-        st.plotly_chart(fig_distance, use_container_width=True)
-
-    with col2:
-        # Activity count over time
-        df['start_date'] = pd.to_datetime(df['start_date'])
-        activity_counts = df.resample('D', on='start_date').size().reset_index(name='count')
-        fig_timeline = px.bar(activity_counts, x='start_date', y='count', 
-                              title="Activity Count Over Time")
-        fig_timeline.update_layout(xaxis_title="Date", yaxis_title="Number of Activities")
-        st.plotly_chart(fig_timeline, use_container_width=True)
+    # Distance by activity type
+    fig_distance = px.bar(df.groupby('type').sum().reset_index(), 
+                          x='type', y='distance', 
+                          title="Total Distance by Activity Type")
+    fig_distance.update_layout(xaxis_title="Activity Type", yaxis_title="Distance (m)")
+    st.plotly_chart(fig_distance, use_container_width=True)
 
     # Training Plan Generation
     st.subheader("🏋️‍♀️ Generate Training Plan")
@@ -179,51 +165,12 @@ if 'access_token' in st.session_state:
             except Exception as e:
                 st.markdown(f'<div class="error-message">Error generating training plan: {str(e)}. Please try again later.</div>', unsafe_allow_html=True)
 
-    # Natural Language Insights
-    st.subheader("💡 Get Insights")
-    user_question = st.text_input("Ask a question about your running data:")
-    if user_question:
-        with st.spinner("Analyzing your data..."):
-            try:
-                context = f"""Based on the runner's data: 
-                Total distance: {total_distance:.2f} km
-                Total time: {total_time:.2f} hours
-                Total elevation gain: {total_elevation:.0f} m
-                Number of activities: {len(df)}
-                Date range: {df['start_date'].min().date()} to {df['start_date'].max().date()}
-                """
-                prompt = f"{context}\n\nUser question: {user_question}\n\nProvide a concise, insightful answer:"
-                response = model.generate_content(prompt)
-                st.write(response.text)
-            except Exception as e:
-                st.markdown(f'<div class="error-message">Error generating insights: {str(e)}. Please try again later.</div>', unsafe_allow_html=True)
-
-    # Performance Analysis
-    st.subheader("📊 Performance Analysis")
-    
-    # Calculate pace
-    df['pace'] = df['moving_time'] / (df['distance'] / 1000)  # hours/km
-    df['pace'] = df['pace'] * 60  # convert to minutes/km
-    
-    # Filter for runs only
-    runs_df = df[df['type'] == 'Run']
-    
-    if not runs_df.empty:
-        fig_pace = px.scatter(runs_df, x='start_date', y='pace', 
-                              title="Running Pace Over Time",
-                              labels={'pace': 'Pace (min/km)', 'start_date': 'Date'})
-        fig_pace.update_layout(yaxis_title="Pace (min/km)")
-        st.plotly_chart(fig_pace, use_container_width=True)
-        
-        # Weekly mileage
-        weekly_mileage = runs_df.resample('W', on='start_date')['distance'].sum() / 1000
-        fig_weekly = px.bar(weekly_mileage.reset_index(), x='start_date', y='distance', 
-                            title="Weekly Running Mileage",
-                            labels={'distance': 'Distance (km)', 'start_date': 'Week'})
-        fig_weekly.update_layout(xaxis_title="Week", yaxis_title="Distance (km)")
-        st.plotly_chart(fig_weekly, use_container_width=True)
-    else:
-        st.info("No running data available for performance analysis.")
+    # Simple Insights
+    st.subheader("💡 Activity Insights")
+    activity_types = df['type'].value_counts()
+    st.write(f"You have {len(df)} recorded activities.")
+    st.write(f"Your most common activity type is {activity_types.index[0]} with {activity_types.iloc[0]} activities.")
+    st.write(f"Your average activity distance is {df['distance'].mean() / 1000:.2f} km.")
 
 else:
     st.info("Please complete the authorization process to view your Strava data and analytics.")