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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 text for bias analysis results | |
| 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'}. | |
| """ | |
| # Return the expected components | |
| 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 | |
| gr.update(visible=True, value=results_markdown) # bias_visualizations - Pass markdown content | |
| ) | |
| 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)}"), | |
| gr.update(visible=False) # bias_visualizations | |
| ) |