update

app.py

@@ -9,6 +9,22 @@ import logging
 import sys
 import traceback
 
+def create_bias_visualization_tab(analysis_results):
+    """
+    Create a specialized tab for bias visualization
+    
+    Args:
+        analysis_results (dict): The analysis results
+        
+    Returns:
+        gradio.Tab: A tab containing the bias visualization
+    """
+    with gr.Tab("Bias Analysis") as tab:
+        # Let the bias visualizer create all needed components
+        components = process_and_visualize_bias_analysis(analysis_results)
+        
+    return tab
+
 # Set up logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
 logger = logging.getLogger('gradio_app')
@@ -506,10 +522,11 @@ def create_app():
                     # Add visualization handling for Bias Detection
                     elif selected_analysis == "Bias Detection" and "bias_detection" in analyses:
                         models = analyses["bias_detection"].get("models", ["Model 1", "Model 2"])
-                        # Use the bias visualization function to create components
-                        visualization_components = process_and_visualize_bias_analysis(analysis_results)
                         
-                        # Set up visibility and components for display
+                        # Set up minimal visibility for the main tab
+                        # Instead of trying to create visualizations here, we'll flag that we need
+                        # to show the bias visualization on another tab
+                        
                         return (
                             analysis_results,  # analysis_results_state
                             False,  # analysis_output visibility  
@@ -517,15 +534,15 @@ def create_app():
                             gr.update(visible=True),  # analysis_title
                             gr.update(visible=True, value=f"## Analysis of Prompt: \"{prompt[:100]}...\""),  # prompt_title
                             gr.update(visible=True, value=f"### Comparing responses from {models[0]} and {models[1]}"),  # models_compared
-                            gr.update(visible=False),  # model1_title
-                            gr.update(visible=False),  # model1_words
+                            gr.update(visible=True, value="#### Bias detection visualization is available below"),  # model1_title
+                            gr.update(visible=True, value="The detailed bias analysis includes sentiment analysis, partisan term detection, and framing analysis. Scroll down to see the visualizations."),  # model1_words
                             gr.update(visible=False),  # model2_title
                             gr.update(visible=False),  # model2_words
                             gr.update(visible=False),  # similarity_metrics_title
                             gr.update(visible=False),  # similarity_metrics
                             False,  # status_message_visible
                             gr.update(visible=False)  # status_message
-                        )                    
+    )                    
 
                     # If we don't have visualization data from any analysis
                     if not visualization_area_visible: