Update app.py

app.py

@@ -86,70 +86,60 @@ def create_stats_html():
     """Create HTML for displaying statistics"""
     original_df, derivative_df = fetch_stats()
     
+    # Create summary statistics
     total_originals = len(original_df)
     total_derivatives = len(derivative_df)
     total_downloads_orig = original_df['downloads_30d'].sum()
     total_downloads_deriv = derivative_df['downloads_30d'].sum()
     
+    # Create derivative type distribution chart
     if len(derivative_df) > 0:
+        # Create distribution by model type
         type_dist = derivative_df.groupby('model_type').agg({
             'model_id': 'count',
             'downloads_30d': 'sum'
         }).reset_index()
-        type_dist['model_type'] = type_dist['model_type'].str.capitalize()        
-        type_dist = type_dist.sort_values('downloads_30d', ascending=True)
         
+        type_dist = derivative_df.groupby('model_type').agg({
+            'model_id': 'count',
+            'downloads_30d': 'sum'
+        }).reset_index()
+
+        type_dist['model_type'] = type_dist['model_type'].str.capitalize()
+
+        type_dist = type_dist.sort_values('downloads_30d', ascending=True)
+
         fig_types = go.Figure(data=[
             go.Bar(
-                x=list(type_dist['model_type']),
-                y=list(type_dist['downloads_30d'].values),
+                x=list(type_dist['model_type']),  # Convert to list
+                y=list(type_dist['downloads_30d'].values),  # Convert series to list of values
                 marker_color='rgb(55, 83, 109)'
             )
         ])
-        
+
         fig_types.update_layout(
-            yaxis=dict(
-                type='log',
-                title='Downloads (log scale)'
-            ),
-            showlegend=False,
+            title='Downloads by Model Type',
+            #xaxis_title='Model Type',
+            yaxis_title='Downloads',
             plot_bgcolor='white',
-            autosize=True,
-            margin=dict(
-                l=50,
-                r=50,
-                t=100,
-                b=50,
-                pad=4
-            )
+            showlegend=False,
+            bargap=0.3
         )
-        
+
         fig_types.update_traces(
-            textposition='outside',
-            cliponaxis=False
+            text=type_dist['downloads_30d'].apply(format_number),
+            textposition='outside'
         )
-        
-        fig_types.update_layout(
-            {
-                'xaxis': {'automargin': True},
-                'yaxis': {'automargin': True},
-                'autosize': True,
-            }
-        )
-        
     else:
         fig_types = px.bar(title='No data available')
-        
-    # Create top models table
+    
     if len(derivative_df) > 0:
         top_models = derivative_df.nlargest(10, 'downloads_30d')[
             ['model_id', 'model_type', 'downloads_30d', 'likes']
-        ].copy()  
+        ].copy()  # Create a copy to avoid SettingWithCopyWarning
         
-        # Capitalize model types in the table
         top_models['model_type'] = top_models['model_type'].str.capitalize()
         
-        # Format download numbers
         top_models['downloads_30d'] = top_models['downloads_30d'].apply(format_number)
     else:
         top_models = pd.DataFrame(columns=['model_id', 'model_type', 'downloads_30d', 'likes'])