Update app.py

app.py

@@ -5,6 +5,7 @@ import time
 from datasets import load_dataset
 # Using the stable, community-built RangeSlider component
 from gradio_rangeslider import RangeSlider
+import datetime # Import the datetime module
 
 # --- Constants ---
 PARAM_CHOICES = ['< 1B', '1B', '5B', '12B', '32B', '64B', '128B', '256B', '> 500B']
@@ -45,7 +46,7 @@ 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=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: datetime.datetime = None):
     if df is None or df.empty: return pd.DataFrame()
     filtered_df = df.copy()
 
@@ -64,13 +65,13 @@ 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]
 
-    # Filter by creation date
+    # --- CORRECTED DATE FILTER LOGIC FOR datetime OBJECT ---
     if created_after_date is not None and 'createdAt' in filtered_df.columns:
-        # Filter out rows where createdAt is NaT (Not a Time) before comparison
+        # Drop rows where 'createdAt' could not be parsed
         filtered_df = filtered_df.dropna(subset=['createdAt'])
-        # The 'createdAt' column is already datetime from the loading function
-        filtered_df = filtered_df[filtered_df['createdAt'].dt.date > created_after_date]
-
+        # The 'created_after_date' is a datetime object from gr.DateTime.
+        # 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()]
 
     if skip_orgs and len(skip_orgs) > 0 and "organization" in filtered_df.columns:
         filtered_df = filtered_df[~filtered_df["organization"].isin(skip_orgs)]
@@ -130,8 +131,8 @@ 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 LINE ---
-            created_after_datepicker = gr.Date(label="Created After", value=None)
+            # --- 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)
             skip_orgs_textbox = gr.Textbox(label="Organizations to Skip (comma-separated)", value="TheBloke,MaziyarPanahi,unsloth,modularai,Gensyn,bartowski")
@@ -150,7 +151,10 @@ with gr.Blocks(title="🤗 ModelVerse Explorer", fill_width=True, css=custom_css
     def _toggle_unknown_params_checkbox(param_range_indices):
         min_idx, max_idx = int(param_range_indices[0]), int(param_range_indices[1])
         is_default_range = (min_idx == PARAM_CHOICES_DEFAULT_INDICES[0] and max_idx == PARAM_CHOICES_DEFAULT_INDICES[1])
-        return gr.update(interactive=is_default_range, value=True if is_default_range else False)
+        if not is_default_range:
+            return gr.update(interactive=False, value=False)
+        else:
+            return gr.update(interactive=True)
             
     param_range_slider.change(update_param_display, param_range_slider, param_range_display)
     param_range_slider.change(_toggle_unknown_params_checkbox, param_range_slider, include_unknown_params_checkbox)