Update app.py

app.py

@@ -42,7 +42,6 @@ def fetch_stats():
         except Exception as e:
             print(f"Error fetching {model_id}: {str(e)}")
     
-    # Fetch derivative models - using the tag format that works
     model_types = ["adapter", "finetune", "merge", "quantized"]
     base_models = [
         "DeepSeek-R1",
@@ -60,7 +59,6 @@ def fetch_stats():
     for base_model in base_models:
         for model_type in model_types:
             try:
-                # Get models for this type
                 models = list(api.list_models(
                     filter=f"base_model:{model_type}:deepseek-ai/{base_model}",
                     full=True
@@ -88,31 +86,27 @@ 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:
-        # Group by model_type and sum downloads
-        type_dist = derivative_df.groupby('model_type')['downloads_30d'].sum().reset_index()
+        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)
         
-        # Format model types to be more readable
-        type_dist['model_type'] = type_dist['model_type'].str.capitalize()
-        
-        # Create bar chart with better formatting
-        fig_types = px.bar(
-            type_dist, 
-            x='model_type',    
-            y='downloads_30d', 
-            title='Total Downloads by Model Type',
-            labels={'downloads_30d': 'Downloads', 'model_type': 'Model Type'},
-            text=type_dist['downloads_30d'].apply(format_number)
-        )
+        fig_types = go.Figure(data=[
+            go.Bar(
+                x=list(type_dist['model_type']),
+                y=list(type_dist['downloads_30d'].values),
+                marker_color='rgb(55, 83, 109)'
+            )
+        ])
         
-        # Update layout for log scale and better appearance
         fig_types.update_layout(
             yaxis=dict(
                 type='log',
@@ -120,8 +114,8 @@ def create_stats_html():
             ),
             showlegend=False,
             plot_bgcolor='white',
-            autosize=True,      # Enable autosize
-            margin=dict(        # Adjust margins
+            autosize=True,
+            margin=dict(
                 l=50,
                 r=50,
                 t=100,
@@ -130,13 +124,11 @@ def create_stats_html():
             )
         )
         
-        # Update bars and text
         fig_types.update_traces(
             textposition='outside',
             cliponaxis=False
         )
         
-        # Configure for responsive behavior
         fig_types.update_layout(
             {
                 'xaxis': {'automargin': True},
@@ -152,7 +144,7 @@ def create_stats_html():
     if len(derivative_df) > 0:
         top_models = derivative_df.nlargest(10, 'downloads_30d')[
             ['model_id', 'model_type', 'downloads_30d', 'likes']
-        ].copy()  # Create a copy to avoid SettingWithCopyWarning
+        ].copy()  
         
         # Capitalize model types in the table
         top_models['model_type'] = top_models['model_type'].str.capitalize()