Update app.py

app.py

@@ -28,7 +28,6 @@ def load_models_data():
         else:
             df['params'] = -1
         
-        # Ensure createdAt is in datetime format, coercing errors
         if 'createdAt' in df.columns:
             df['createdAt'] = pd.to_datetime(df['createdAt'], errors='coerce')
 
@@ -46,7 +45,7 @@ def get_param_range_values(param_range_labels):
     max_val = float('inf') if '>' in max_label else float(max_label.replace('B', ''))
     return min_val, max_val
 
-def make_treemap_data(df, count_by, top_k=25, tag_filter=None, pipeline_filter=None, param_range=None, skip_orgs=None, include_unknown_param_size=True, created_after_date: datetime.datetime = None):
+def make_treemap_data(df, count_by, top_k=25, tag_filter=None, pipeline_filter=None, param_range=None, skip_orgs=None, include_unknown_param_size=True, created_after_date: float = None):
     if df is None or df.empty: return pd.DataFrame()
     filtered_df = df.copy()
 
@@ -65,13 +64,16 @@ def make_treemap_data(df, count_by, top_k=25, tag_filter=None, pipeline_filter=N
             if min_params is not None: filtered_df = filtered_df[filtered_df['params'] >= min_params]
             if max_params is not None and max_params != float('inf'): filtered_df = filtered_df[filtered_df['params'] < max_params]
 
-    # --- CORRECTED DATE FILTER LOGIC FOR datetime OBJECT ---
+    # --- CORRECTED DATE FILTER LOGIC FOR FLOAT TIMESTAMP ---
     if created_after_date is not None and 'createdAt' in filtered_df.columns:
-        # Drop rows where 'createdAt' could not be parsed
+        # Drop rows where 'createdAt' could not be parsed to avoid errors
         filtered_df = filtered_df.dropna(subset=['createdAt'])
-        # The 'created_after_date' is a datetime object from gr.DateTime.
+        
+        # Convert the Unix timestamp (float) from the UI into a Python date object
+        filter_date = datetime.datetime.fromtimestamp(created_after_date).date()
+        
         # Compare its date part with the date part of the 'createdAt' column.
-        filtered_df = filtered_df[filtered_df['createdAt'].dt.date > created_after_date.date()]
+        filtered_df = filtered_df[filtered_df['createdAt'].dt.date > filter_date]
 
     if skip_orgs and len(skip_orgs) > 0 and "organization" in filtered_df.columns:
         filtered_df = filtered_df[~filtered_df["organization"].isin(skip_orgs)]
@@ -131,7 +133,6 @@ with gr.Blocks(title="🤗 ModelVerse Explorer", fill_width=True, css=custom_css
                 param_range_display = gr.Markdown(f"Range: `{PARAM_CHOICES[0]}` to `{PARAM_CHOICES[-1]}`")
                 include_unknown_params_checkbox = gr.Checkbox(label="Include models with unknown parameter size", value=True)
             
-            # --- CORRECTED COMPONENT BASED ON DOCUMENTATION ---
             created_after_datepicker = gr.DateTime(label="Created After")
             
             top_k_dropdown = gr.Dropdown(label="Number of Top Organizations", choices=TOP_K_CHOICES, value=25)
@@ -190,7 +191,7 @@ with gr.Blocks(title="🤗 ModelVerse Explorer", fill_width=True, css=custom_css
             "downloads", "None", None, None, PARAM_CHOICES_DEFAULT_INDICES, 25, 
             "TheBloke,MaziyarPanahi,unsloth,modularai,Gensyn,bartowski", True, None, current_df, progress
         )
-        return current_df, load_success_flag, data_info_text, initial_status, initial_plot
+        return current_д, load_success_flag, data_info_text, initial_status, initial_plot
 
     def ui_generate_plot_controller(metric_choice, filter_type, tag_choice, pipeline_choice, 
                                    param_range_indices, k_orgs, skip_orgs_input, include_unknown_param_size_flag,
@@ -229,4 +230,4 @@ with gr.Blocks(title="🤗 ModelVerse Explorer", fill_width=True, css=custom_css
 
 if __name__ == "__main__":
     print(f"Application starting...")
-    demo.queue().launch()
+    demo.queue().launch()```