Update app.py

app.py

@@ -1,4 +1,4 @@
-# --- START OF FULLY CORRECTED FILE app.py ---
+# --- START OF FULLY CORRECTED AND IMPROVED FILE app.py ---
 
 import gradio as gr
 import pandas as pd
@@ -8,7 +8,6 @@ from datasets import load_dataset # Import the datasets library
 
 # --- Constants ---
 PARAM_CHOICES = ['< 1B', '1B', '5B', '12B', '32B', '64B', '128B', '256B', '> 500B']
-# --- NEW: Define the default value using indices ---
 PARAM_CHOICES_DEFAULT_INDICES = [0, len(PARAM_CHOICES) - 1]
 
 HF_DATASET_ID = "evijit/orgstats_daily_data"
@@ -63,7 +62,6 @@ def make_treemap_data(df, count_by, top_k=25, tag_filter=None, pipeline_filter=N
         else: print(f"Warning: 'pipeline_tag' column not found for filtering.")
     if param_range:
         min_params, max_params = get_param_range_values(param_range)
-        # --- MODIFIED: Check against labels, not indices ---
         is_default_range = (param_range[0] == PARAM_CHOICES[0] and param_range[1] == PARAM_CHOICES[-1])
         if not is_default_range and 'params' in filtered_df.columns:
             if min_params is not None: filtered_df = filtered_df[filtered_df['params'] >= min_params]
@@ -106,25 +104,17 @@ with gr.Blocks(title="ModelVerse Explorer", fill_width=True) as demo:
             tag_filter_dropdown = gr.Dropdown(label="Select Tag", choices=TAG_FILTER_CHOICES, value=None, visible=False)
             pipeline_filter_dropdown = gr.Dropdown(label="Select Pipeline Tag", choices=PIPELINE_TAGS, value=None, visible=False)
             
-            # --- MODIFIED: Replaced RangeSlider with Slider and a dynamic Markdown label ---
             with gr.Group():
                 with gr.Row():
-                    gr.Markdown("<div style='padding-top: 10px; font-weight: 500;'>Parameters</div>")
+                    param_label_display = gr.Markdown("<div style='font-weight: 500;'>Parameters</div>")
                     reset_params_button = gr.Button("🔄 Reset", visible=False, size="sm", min_width=80)
                 
-                # This markdown will show the selected text labels
-                param_label_display = gr.Markdown(f"Range: **{PARAM_CHOICES[0]}** to **{PARAM_CHOICES[-1]}**")
-
                 param_slider = gr.Slider(
-                    minimum=0,
-                    maximum=len(PARAM_CHOICES) - 1,
-                    step=1,
+                    minimum=0, maximum=len(PARAM_CHOICES) - 1, step=1,
                     value=PARAM_CHOICES_DEFAULT_INDICES,
-                    label=None, # Label is handled by components above
-                    show_label=False
+                    label="Parameter Range", show_label=False # Use a hidden label for accessibility
                 )
-            # --- END OF MODIFICATION ---
-            
+
             top_k_slider = gr.Slider(label="Number of Top Organizations", minimum=5, maximum=50, value=25, step=5)
             skip_orgs_textbox = gr.Textbox(label="Organizations to Skip (comma-separated)", value="TheBloke,MaziyarPanahi,unsloth,modularai,Gensyn,bartowski")
             generate_plot_button = gr.Button(value="Generate Plot", variant="primary", interactive=False)
@@ -134,37 +124,40 @@ with gr.Blocks(title="ModelVerse Explorer", fill_width=True) as demo:
             status_message_md = gr.Markdown("Initializing...")
             data_info_md = gr.Markdown("")
 
-    # --- NEW AND MODIFIED: Event handlers for the new Slider ---
+    # --- MODIFIED: More robust event handlers for the slider ---
     def _update_slider_ui_elements(current_range_indices):
-        """Updates the label and reset button visibility based on the slider's state."""
+        """Updates the labels above the slider and the reset button visibility."""
+        if not isinstance(current_range_indices, list) or len(current_range_indices) != 2:
+            # This is a defensive check to prevent crashes if the input is malformed.
+            return gr.update(), gr.update()
+            
         min_idx, max_idx = int(current_range_indices[0]), int(current_range_indices[1])
-        min_label = PARAM_CHOICES[min_idx]
-        max_label = PARAM_CHOICES[max_idx]
+        min_label, max_label = PARAM_CHOICES[min_idx], PARAM_CHOICES[max_idx]
         
-        is_default = (min_idx == PARAM_CHOICES_DEFAULT_INDICES[0] and max_idx == PARAM_CHOICES_DEFAULT_INDICES[1])
+        # Using HTML for bold is more reliable in gr.Markdown
+        label_md = f"<div style='font-weight: 500;'>Parameters <span style='float: right; font-weight: normal; color: #555;'>{min_label} to {max_label}</span></div>"
         
-        label_md = f"Range: **{min_label}** to **{max_label}**"
+        is_default = (min_idx == 0 and max_idx == len(PARAM_CHOICES) - 1)
         button_visibility = gr.update(visible=not is_default)
         
         return label_md, button_visibility
 
     def _reset_param_slider_and_ui():
         """Resets the slider to default and updates the UI elements accordingly."""
-        default_indices = PARAM_CHOICES_DEFAULT_INDICES
-        default_label = f"Range: **{PARAM_CHOICES[default_indices[0]]}** to **{PARAM_CHOICES[default_indices[1]]}**"
-        
-        return gr.update(value=default_indices), default_label, gr.update(visible=False)
+        default_label = "<div style='font-weight: 500;'>Parameters</div>"
+        return gr.update(value=PARAM_CHOICES_DEFAULT_INDICES), default_label, gr.update(visible=False)
 
-    param_slider.release(fn=_update_slider_ui_elements, inputs=param_slider, outputs=[param_label_display, reset_params_button])
+    # Use .change() for better reliability
+    param_slider.change(fn=_update_slider_ui_elements, inputs=param_slider, outputs=[param_label_display, reset_params_button])
     reset_params_button.click(fn=_reset_param_slider_and_ui, outputs=[param_slider, param_label_display, reset_params_button])
-    # --- END OF NEW EVENT HANDLERS ---
-
+    
     def _update_button_interactivity(is_loaded_flag): return gr.update(interactive=is_loaded_flag)
     loading_complete_state.change(fn=_update_button_interactivity, inputs=loading_complete_state, outputs=generate_plot_button)
 
     def _toggle_filters_visibility(choice): return gr.update(visible=choice == "Tag Filter"), gr.update(visible=choice == "Pipeline Filter")
     filter_choice_radio.change(fn=_toggle_filters_visibility, inputs=filter_choice_radio, outputs=[tag_filter_dropdown, pipeline_filter_dropdown])
 
+    # --- MODIFIED: Fixed the timezone handling logic ---
     def ui_load_data_controller(progress=gr.Progress()):
         progress(0, desc=f"Loading dataset '{HF_DATASET_ID}' from Hugging Face Hub...")
         status_msg_ui, data_info_text, load_success_flag = "Loading data...", "", False
@@ -172,8 +165,16 @@ with gr.Blocks(title="ModelVerse Explorer", fill_width=True) as demo:
             current_df, load_success_flag, status_msg_from_load = load_models_data()
             if load_success_flag:
                 progress(0.9, desc="Processing loaded data...")
-                ts = pd.to_datetime(current_df['data_download_timestamp'].iloc[0]).tz_localize('UTC') if 'data_download_timestamp' in current_df.columns and not current_df.empty and pd.notna(current_df['data_download_timestamp'].iloc[0]) else None
-                date_display = ts.strftime('%B %d, %Y, %H:%M:%S %Z') if ts else "Pre-processed (date unavailable)"
+                date_display = "Pre-processed (date unavailable)"
+                if 'data_download_timestamp' in current_df.columns and not current_df.empty and pd.notna(current_df['data_download_timestamp'].iloc[0]):
+                    timestamp = pd.to_datetime(current_df['data_download_timestamp'].iloc[0])
+                    # Check if tz-aware. If so, convert. If not, localize.
+                    if timestamp.tzinfo is None:
+                        ts = timestamp.tz_localize('UTC')
+                    else:
+                        ts = timestamp.tz_convert('UTC')
+                    date_display = ts.strftime('%B %d, %Y, %H:%M:%S %Z')
+
                 param_count = (current_df['params'] > 0).sum() if 'params' in current_df.columns else 0
                 data_info_text = f"### Data Information\n- Source: `{HF_DATASET_ID}`\n- Status: {status_msg_from_load}\n- Total models loaded: {len(current_df):,}\n- Models with parameter counts: {param_count:,}\n- Data as of: {date_display}\n"
                 status_msg_ui = "Data loaded successfully. Ready to generate plot."
@@ -186,7 +187,6 @@ with gr.Blocks(title="ModelVerse Explorer", fill_width=True) as demo:
             print(f"Critical error in ui_load_data_controller: {e}")
         return current_df, load_success_flag, data_info_text, status_msg_ui
 
-    # --- MODIFIED: Controller now takes slider indices and converts them to labels ---
     def ui_generate_plot_controller(metric_choice, filter_type, tag_choice, pipeline_choice, 
                                    param_range_indices, k_orgs, skip_orgs_input, df_current_models, progress=gr.Progress()):
         if df_current_models is None or df_current_models.empty:
@@ -196,7 +196,6 @@ with gr.Blocks(title="ModelVerse Explorer", fill_width=True) as demo:
         pipeline_to_use = pipeline_choice if filter_type == "Pipeline Filter" else None
         orgs_to_skip = [org.strip() for org in skip_orgs_input.split(',') if org.strip()] if skip_orgs_input else []
         
-        # Convert indices from slider back to text labels for filtering logic
         min_label = PARAM_CHOICES[int(param_range_indices[0])]
         max_label = PARAM_CHOICES[int(param_range_indices[1])]
         param_labels_for_filtering = [min_label, max_label]
@@ -221,7 +220,7 @@ with gr.Blocks(title="ModelVerse Explorer", fill_width=True) as demo:
     generate_plot_button.click(
         fn=ui_generate_plot_controller,
         inputs=[count_by_dropdown, filter_choice_radio, tag_filter_dropdown, pipeline_filter_dropdown,
-                param_slider, top_k_slider, skip_orgs_textbox, models_data_state], # MODIFIED: Use param_slider
+                param_slider, top_k_slider, skip_orgs_textbox, models_data_state],
         outputs=[plot_output, status_message_md]
     )
 
@@ -229,4 +228,4 @@ if __name__ == "__main__":
     print(f"Application starting. Data will be loaded from Hugging Face dataset: {HF_DATASET_ID}")
     demo.queue().launch()
 
-# --- END OF FULLY CORRECTED FILE app.py ---
+# --- END OF FULLY CORRECTED AND IMPROVED FILE app.py ---