Update app.py

app.py

@@ -121,35 +121,35 @@ if 'access_token' in st.session_state:
     # Fetch running activities
     @st.cache_data(ttl=3600)
     def fetch_run_activities():
-    activities = list(client.get_activities(limit=200))
-    runs = []
-    for activity in activities:
-        if activity.type == 'Run':
-            run_data = {
-                'name': activity.name,
-                'distance': float(activity.distance.num) / 1000 if activity.distance else None,  # Convert to km
-                'moving_time': None,
-                'total_elevation_gain': float(activity.total_elevation_gain) if activity.total_elevation_gain else None,
-                'average_speed': float(activity.average_speed) * 3.6 if activity.average_speed else None,  # Convert to km/h
-                'average_heartrate': float(activity.average_heartrate) if activity.average_heartrate else None,
-                'start_date': activity.start_date.replace(tzinfo=None) if activity.start_date else None
-            }
-            
-            # Safely handle moving_time
-            try:
-                if activity.moving_time:
-                    run_data['moving_time'] = activity.moving_time.total_seconds() / 60  # Convert to minutes
-            except AttributeError:
-                pass  # If moving_time is not accessible, leave it as None
-            
-            runs.append(run_data)
-    
-    df = pd.DataFrame(runs)
-    
-    # Calculate pace only if both moving_time and distance are available
-    df['pace'] = df.apply(lambda row: row['moving_time'] / row['distance'] if row['moving_time'] and row['distance'] else None, axis=1)
-    
-    return df
+        activities = list(client.get_activities(limit=200))
+        runs = []
+        for activity in activities:
+            if activity.type == 'Run':
+                run_data = {
+                    'name': activity.name,
+                    'distance': float(activity.distance.num) / 1000 if activity.distance else None,  # Convert to km
+                    'moving_time': None,
+                    'total_elevation_gain': float(activity.total_elevation_gain) if activity.total_elevation_gain else None,
+                    'average_speed': float(activity.average_speed) * 3.6 if activity.average_speed else None,  # Convert to km/h
+                    'average_heartrate': float(activity.average_heartrate) if activity.average_heartrate else None,
+                    'start_date': activity.start_date.replace(tzinfo=None) if activity.start_date else None
+                }
+                
+                # Safely handle moving_time
+                try:
+                    if activity.moving_time:
+                        run_data['moving_time'] = activity.moving_time.total_seconds() / 60  # Convert to minutes
+                except AttributeError:
+                    pass  # If moving_time is not accessible, leave it as None
+                
+                runs.append(run_data)
+        
+        df = pd.DataFrame(runs)
+        
+        # Calculate pace only if both moving_time and distance are available
+        df['pace'] = df.apply(lambda row: row['moving_time'] / row['distance'] if row['moving_time'] and row['distance'] else None, axis=1)
+        
+        return df
 
     try:
         df = fetch_run_activities()