Update app.py

app.py

@@ -1,4 +1,4 @@
-# app.py 
+# app.py (Corrected and Ready to Run)
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
@@ -30,6 +30,7 @@ class EnhancedAIvsRealGazeAnalyzer:
         self.model = None
         self.scaler = None
         self.feature_names = []
+        self.time_metrics = [] # Initialize here
 
     def _find_and_standardize_participant_col(self, df, filename):
         participant_col = next((c for c in df.columns if 'participant' in str(c).lower()), None)
@@ -84,7 +85,10 @@ class EnhancedAIvsRealGazeAnalyzer:
         self.combined_data['Answer_Correctness'] = self.combined_data['Correct'].map({True: 'Correct', False: 'Incorrect'})
         
         self.numeric_cols = self.combined_data.select_dtypes(include=np.number).columns.tolist()
-        self.time_ metrics = [c for c in self.numeric_cols if any(k in c.lower() for k in ['time', 'duration', 'fixation'])]
+        
+        # <<< FIX: Removed the space in the variable name here >>>
+        self.time_metrics = [c for c in self.numeric_cols if any(k in c.lower() for k in ['time', 'duration', 'fixation'])]
+        
         self.valid_playback_participants = sorted(list(self.valid_playback_trials.keys()))
         print(f"--- Data Loading Successful. Found {len(self.valid_playback_participants)} participants with fixation data. ---")
         return self
@@ -178,28 +182,22 @@ class EnhancedAIvsRealGazeAnalyzer:
         valid_questions = self.valid_playback_trials.get(participant, [])
         return gr.Dropdown(choices=sorted(valid_questions), interactive=True, value=None, label="2. Select a Question")
 
-    # <<< FIX START: Step 1 -> Add a new handler method for when a question is selected. >>>
-    # This function provides a clean, single entry point to reset the visualization.
     def handle_new_trial_selection(self, participant, question):
         """Called when a new trial is selected. Resets the view to the first fixation."""
         if not participant or not question:
-            # Return a default/empty state for all outputs if nothing is selected
             return "Select a trial to begin.", None, gr.Slider(value=0, interactive=False)
-        
-        # Start the playback at the very first fixation for a better user experience.
-        # You can change '1' to '0' if you prefer it to start completely empty.
         initial_fixation_num = 1
-        
-        # Call the main generation function with this fixed initial value
         return self.generate_gaze_playback(participant, question, initial_fixation_num)
-    # <<< FIX END >>>
 
 # --- DATA SETUP & GRADIO APP ---
 def setup_and_load_data():
     repo_url = "https://github.com/RextonRZ/GenAIEyeTrackingCleanedDataset"
     repo_dir = "GenAIEyeTrackingCleanedDataset"
-    if not os.path.exists(repo_dir): git.Repo.clone_from(repo_url, repo_dir)
-    else: print("Data repository already exists.")
+    if not os.path.exists(repo_dir): 
+        print(f"Cloning repository {repo_url}...")
+        git.Repo.clone_from(repo_url, repo_dir)
+    else: 
+        print("Data repository already exists.")
     base_path = repo_dir 
     response_file_path = os.path.join(repo_dir, "GenAI Response.xlsx")
     analyzer = EnhancedAIvsRealGazeAnalyzer().load_and_process_data(base_path, response_file_path)
@@ -248,35 +246,52 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     
     rq1_metric_dropdown.change(fn=analyzer.analyze_rq1_metric, inputs=rq1_metric_dropdown, outputs=[rq1_plot_output, rq1_summary_output])
     
-    rq2_test_size_slider.release(fn=analyzer.run_prediction_model, inputs=[rq2_test_size_slider, rq2_estimators_slider], outputs=outputs_rq2)
+    train_event = rq2_test_size_slider.release(fn=analyzer.run_prediction_model, inputs=[rq2_test_size_slider, rq2_estimators_slider], outputs=outputs_rq2)
     rq2_estimators_slider.release(fn=analyzer.run_prediction_model, inputs=[rq2_test_size_slider, rq2_estimators_slider], outputs=outputs_rq2)
     
-    # <<< FIX START: Step 2 -> Update the event wiring for Tab 3. >>>
-    # Chained dropdown logic for Tab 3
     playback_participant.change(
         fn=analyzer.update_question_dropdown, 
         inputs=playback_participant, 
         outputs=playback_question
     )
 
-    # When a question is selected, call the new handler to reset the entire playback view.
-    # This is much cleaner and more reliable than the previous .then() chain.
     playback_question.change(
         fn=analyzer.handle_new_trial_selection, 
         inputs=[playback_participant, playback_question], 
         outputs=outputs_playback
     )
     
-    # The slider event handler remains the same and works correctly for scrubbing through fixations.
     playback_slider.release(
         fn=analyzer.generate_gaze_playback, 
         inputs=[playback_participant, playback_question, playback_slider], 
         outputs=outputs_playback
     )
-    # <<< FIX END >>>
 
-    demo.load(fn=analyzer.analyze_rq1_metric, inputs=rq1_metric_dropdown, outputs=[rq1_plot_output, rq1_summary_output])
-    demo.load(fn=analyzer.run_prediction_model, inputs=[rq2_test_size_slider, rq2_estimators_slider], outputs=outputs_rq2)
+    # Pre-load the initial state of the dashboard
+    def initial_load():
+        # Load the first tab's content
+        rq1_fig, rq1_summary = analyzer.analyze_rq1_metric(analyzer.time_metrics[0] if analyzer.time_metrics else None)
+        
+        # Train the initial model for the second tab
+        model_summary, report_df, feature_fig, status_md = analyzer.run_prediction_model(0.3, 100)
+        
+        # Return all the values needed to populate the outputs on load
+        return {
+            rq1_plot_output: rq1_fig,
+            rq1_summary_output: rq1_summary,
+            rq2_summary_output: model_summary,
+            rq2_table_output: report_df,
+            rq2_plot_output: feature_fig,
+            rq2_status: status_md
+        }
+
+    demo.load(
+        fn=initial_load,
+        outputs=[
+            rq1_plot_output, rq1_summary_output, 
+            rq2_summary_output, rq2_table_output, rq2_plot_output, rq2_status
+        ]
+    )
 
 if __name__ == "__main__":
     demo.launch()