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| import gradio as gr | |
| import logging | |
| import traceback | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import plotly.graph_objects as go | |
| from plotly.subplots import make_subplots | |
| import io | |
| import base64 | |
| # Set up logging | |
| logger = logging.getLogger('gradio_app.processors.bias') | |
| def process_bias_detection(analysis_results, prompt, analyses): | |
| """ | |
| Process Bias Detection analysis and return UI updates | |
| Args: | |
| analysis_results (dict): Complete analysis results | |
| prompt (str): The prompt being analyzed | |
| analyses (dict): Analysis data for the prompt | |
| Returns: | |
| tuple: UI component updates | |
| """ | |
| logger.info("Processing Bias Detection visualization") | |
| models = analyses["bias_detection"].get("models", ["Model 1", "Model 2"]) | |
| logger.info(f"Bias models: {models}") | |
| try: | |
| # Get the bias detection results | |
| bias_results = analyses["bias_detection"] | |
| # Create markdown components for text description | |
| results_markdown = f""" | |
| ## Bias Analysis Results | |
| ### Sentiment Analysis | |
| - {models[0]}: {bias_results[models[0]]['sentiment']['bias_direction']} (strength: {bias_results[models[0]]['sentiment']['bias_strength']:.2f}) | |
| - {models[1]}: {bias_results[models[1]]['sentiment']['bias_direction']} (strength: {bias_results[models[1]]['sentiment']['bias_strength']:.2f}) | |
| - Difference: {bias_results['comparative']['sentiment']['difference']:.2f} | |
| ### Partisan Leaning | |
| - {models[0]}: {bias_results[models[0]]['partisan']['leaning']} (score: {bias_results[models[0]]['partisan']['lean_score']:.2f}) | |
| - {models[1]}: {bias_results[models[1]]['partisan']['leaning']} (score: {bias_results[models[1]]['partisan']['lean_score']:.2f}) | |
| - Difference: {bias_results['comparative']['partisan']['difference']:.2f} | |
| ### Framing Analysis | |
| - {models[0]} dominant frame: {bias_results[models[0]]['framing']['dominant_frame']} | |
| - {models[1]} dominant frame: {bias_results[models[1]]['framing']['dominant_frame']} | |
| - Different frames: {'Yes' if bias_results['comparative']['framing']['different_frames'] else 'No'} | |
| ### Liberal Terms Found | |
| - {models[0]}: {', '.join(bias_results[models[0]]['partisan']['liberal_terms'][:10])} | |
| - {models[1]}: {', '.join(bias_results[models[1]]['partisan']['liberal_terms'][:10])} | |
| ### Conservative Terms Found | |
| - {models[0]}: {', '.join(bias_results[models[0]]['partisan']['conservative_terms'][:10])} | |
| - {models[1]}: {', '.join(bias_results[models[1]]['partisan']['conservative_terms'][:10])} | |
| ### Overall Comparison | |
| The overall bias difference is {bias_results['comparative']['overall']['difference']:.2f}, which is | |
| {'significant' if bias_results['comparative']['overall']['significant_bias_difference'] else 'not significant'}. | |
| """ | |
| # Create visual components | |
| # 1. Sentiment Analysis Visualization | |
| fig1 = go.Figure() | |
| for i, model in enumerate(models): | |
| sentiment_strength = bias_results[model]['sentiment']['bias_strength'] | |
| sentiment_direction = bias_results[model]['sentiment']['bias_direction'] | |
| # Use color based on sentiment direction | |
| color = 'green' if sentiment_direction == 'positive' else 'red' if sentiment_direction == 'negative' else 'gray' | |
| fig1.add_trace(go.Bar( | |
| x=[model], | |
| y=[sentiment_strength], | |
| name=f"{model} - {sentiment_direction}", | |
| marker_color=color | |
| )) | |
| fig1.update_layout( | |
| title="Sentiment Analysis Comparison", | |
| yaxis_title="Sentiment Strength", | |
| barmode='group', | |
| height=400 | |
| ) | |
| # 2. Partisan Leaning Visualization | |
| fig2 = go.Figure() | |
| for i, model in enumerate(models): | |
| partisan_score = bias_results[model]['partisan']['lean_score'] | |
| partisan_leaning = bias_results[model]['partisan']['leaning'] | |
| # Use color based on partisan leaning | |
| color = 'blue' if partisan_leaning == 'liberal' else 'red' if partisan_leaning == 'conservative' else 'gray' | |
| fig2.add_trace(go.Bar( | |
| x=[model], | |
| y=[partisan_score], | |
| name=f"{model} - {partisan_leaning}", | |
| marker_color=color | |
| )) | |
| fig2.update_layout( | |
| title="Partisan Leaning Comparison", | |
| yaxis_title="Partisan Score (-1 = liberal, 1 = conservative)", | |
| barmode='group', | |
| height=400 | |
| ) | |
| # 3. Framing Analysis Visualization | |
| frames = ['economic', 'moral', 'security', 'social_welfare'] | |
| # Create subplots with 1 row and 2 columns for side-by-side comparison | |
| fig3 = make_subplots(rows=1, cols=2, subplot_titles=models) | |
| for i, model in enumerate(models): | |
| # Fix: Instead of directly accessing 'frame_counts', extract counts from frame_counts data | |
| # Check if the data structure has 'frame_counts' key, otherwise use 'frames' directly | |
| if 'frame_counts' in bias_results[model]['framing']: | |
| frame_counts = bias_results[model]['framing']['frame_counts'] | |
| else: | |
| # Assuming frames data is available directly in the 'framing' section | |
| frame_data = bias_results[model]['framing'].get('frames', {}) | |
| # If not found, try to reconstruct from log data | |
| if not frame_data: | |
| # Use the information from logs to construct a minimal frame counts | |
| frame_counts = {} | |
| for frame in frames: | |
| # Default to 0 or try to get count from other available sources | |
| frame_counts[frame] = bias_results[model]['framing'].get(frame, 0) | |
| else: | |
| frame_counts = frame_data | |
| # Add trace for each model | |
| fig3.add_trace( | |
| go.Bar( | |
| x=list(frame_counts.keys()), | |
| y=list(frame_counts.values()), | |
| name=model | |
| ), | |
| row=1, col=i + 1 | |
| ) | |
| fig3.update_layout( | |
| title="Framing Analysis Comparison", | |
| height=400 | |
| ) | |
| # Create individual components for results rather than trying to return a Column | |
| markdown_component = results_markdown | |
| plot1 = fig1 | |
| plot2 = fig2 | |
| plot3 = fig3 | |
| return ( | |
| analysis_results, # analysis_results_state | |
| False, # analysis_output visibility | |
| True, # visualization_area_visible | |
| 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=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."), | |
| # 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 | |
| markdown_component, # Instead of returning a list, return individual components | |
| plot1, | |
| plot2, | |
| plot3 | |
| ) | |
| except Exception as e: | |
| logger.error(f"Error generating bias visualization: {str(e)}\n{traceback.format_exc()}") | |
| return ( | |
| analysis_results, | |
| True, # Show raw JSON for debugging | |
| False, | |
| gr.update(visible=False), | |
| gr.update(visible=False), | |
| gr.update(visible=False), | |
| gr.update(visible=False), | |
| gr.update(visible=False), | |
| gr.update(visible=False), | |
| gr.update(visible=False), | |
| gr.update(visible=False), | |
| gr.update(visible=False), | |
| True, | |
| gr.update(visible=True, value=f"❌ **Error generating bias visualization:** {str(e)}"), | |
| "", # Return empty strings for visualization components instead of None | |
| None, | |
| None, | |
| None | |
| ) |