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| import gradio as gr | |
| import plotly.graph_objects as go | |
| import plotly.express as px | |
| import pandas as pd | |
| def create_bias_visualization(analysis_results): | |
| """ | |
| Create visualizations for bias detection analysis results | |
| Args: | |
| analysis_results (dict): Analysis results from the bias detection | |
| Returns: | |
| list: List of gradio components with visualizations | |
| """ | |
| output_components = [] | |
| # Check if we have valid results | |
| if not analysis_results or "analyses" not in analysis_results: | |
| return [gr.Markdown("No analysis results found.")] | |
| # Process each prompt | |
| for prompt, analyses in analysis_results["analyses"].items(): | |
| # Process Bias Detection analysis if available | |
| if "bias_detection" in analyses: | |
| bias_results = analyses["bias_detection"] | |
| # Show models being compared | |
| models = bias_results.get("models", []) | |
| if len(models) >= 2: | |
| output_components.append(gr.Markdown(f"### Bias Analysis: Comparing responses from {models[0]} and {models[1]}")) | |
| # Check if there's an error | |
| if "error" in bias_results: | |
| output_components.append(gr.Markdown(f"**Error in bias detection:** {bias_results['error']}")) | |
| continue | |
| model1_name, model2_name = models[0], models[1] | |
| # Comparative results | |
| if "comparative" in bias_results: | |
| comparative = bias_results["comparative"] | |
| output_components.append(gr.Markdown("#### Comparative Bias Analysis")) | |
| # Create summary table | |
| summary_html = f""" | |
| <table style="width:100%; border-collapse: collapse; margin-bottom: 20px;"> | |
| <tr> | |
| <th style="border: 1px solid #ddd; padding: 8px; text-align: left; background-color: #f2f2f2;">Bias Category</th> | |
| <th style="border: 1px solid #ddd; padding: 8px; text-align: left; background-color: #f2f2f2;">{model1_name}</th> | |
| <th style="border: 1px solid #ddd; padding: 8px; text-align: left; background-color: #f2f2f2;">{model2_name}</th> | |
| <th style="border: 1px solid #ddd; padding: 8px; text-align: left; background-color: #f2f2f2;">Significant Difference?</th> | |
| </tr> | |
| """ | |
| # Sentiment row | |
| if "sentiment" in comparative: | |
| sent_sig = comparative["sentiment"].get("significant", False) | |
| summary_html += f""" | |
| <tr> | |
| <td style="border: 1px solid #ddd; padding: 8px;">Sentiment Bias</td> | |
| <td style="border: 1px solid #ddd; padding: 8px;">{comparative["sentiment"].get(model1_name, "N/A").title()}</td> | |
| <td style="border: 1px solid #ddd; padding: 8px;">{comparative["sentiment"].get(model2_name, "N/A").title()}</td> | |
| <td style="border: 1px solid #ddd; padding: 8px; font-weight: bold; color: {'red' if sent_sig else 'green'}">{"Yes" if sent_sig else "No"}</td> | |
| </tr> | |
| """ | |
| # Partisan row | |
| if "partisan" in comparative: | |
| part_sig = comparative["partisan"].get("significant", False) | |
| summary_html += f""" | |
| <tr> | |
| <td style="border: 1px solid #ddd; padding: 8px;">Partisan Leaning</td> | |
| <td style="border: 1px solid #ddd; padding: 8px;">{comparative["partisan"].get(model1_name, "N/A").title()}</td> | |
| <td style="border: 1px solid #ddd; padding: 8px;">{comparative["partisan"].get(model2_name, "N/A").title()}</td> | |
| <td style="border: 1px solid #ddd; padding: 8px; font-weight: bold; color: {'red' if part_sig else 'green'}">{"Yes" if part_sig else "No"}</td> | |
| </tr> | |
| """ | |
| # Framing row | |
| if "framing" in comparative: | |
| frame_diff = comparative["framing"].get("different_frames", False) | |
| summary_html += f""" | |
| <tr> | |
| <td style="border: 1px solid #ddd; padding: 8px;">Dominant Frame</td> | |
| <td style="border: 1px solid #ddd; padding: 8px;">{comparative["framing"].get(model1_name, "N/A").title().replace('_', ' ')}</td> | |
| <td style="border: 1px solid #ddd; padding: 8px;">{comparative["framing"].get(model2_name, "N/A").title().replace('_', ' ')}</td> | |
| <td style="border: 1px solid #ddd; padding: 8px; font-weight: bold; color: {'red' if frame_diff else 'green'}">{"Yes" if frame_diff else "No"}</td> | |
| </tr> | |
| """ | |
| # Overall row | |
| if "overall" in comparative: | |
| overall_sig = comparative["overall"].get("significant_bias_difference", False) | |
| summary_html += f""" | |
| <tr> | |
| <td style="border: 1px solid #ddd; padding: 8px; font-weight: bold;">Overall Bias Difference</td> | |
| <td colspan="2" style="border: 1px solid #ddd; padding: 8px; text-align: center;">{comparative["overall"].get("difference", 0):.2f} / 1.0</td> | |
| <td style="border: 1px solid #ddd; padding: 8px; font-weight: bold; color: {'red' if overall_sig else 'green'}">{"Yes" if overall_sig else "No"}</td> | |
| </tr> | |
| """ | |
| summary_html += "</table>" | |
| # Add the HTML table to the components | |
| output_components.append(gr.HTML(summary_html)) | |
| # Create detailed visualizations for each model if available | |
| for model_name in [model1_name, model2_name]: | |
| if model_name in bias_results: | |
| model_data = bias_results[model_name] | |
| # Sentiment visualization | |
| if "sentiment" in model_data: | |
| sentiment = model_data["sentiment"] | |
| if "sentiment_scores" in sentiment: | |
| # Create sentiment score chart | |
| sentiment_df = pd.DataFrame({ | |
| 'Score': [ | |
| sentiment["sentiment_scores"]["pos"], | |
| sentiment["sentiment_scores"]["neg"], | |
| sentiment["sentiment_scores"]["neu"] | |
| ], | |
| 'Category': ['Positive', 'Negative', 'Neutral'] | |
| }) | |
| fig = px.bar( | |
| sentiment_df, | |
| x='Category', | |
| y='Score', | |
| title=f"Sentiment Analysis for {model_name}", | |
| height=300 | |
| ) | |
| output_components.append(gr.Plot(value=fig)) | |
| # Partisan leaning visualization | |
| if "partisan" in model_data: | |
| partisan = model_data["partisan"] | |
| if "liberal_count" in partisan and "conservative_count" in partisan: | |
| # Create partisan terms chart | |
| partisan_df = pd.DataFrame({ | |
| 'Count': [partisan["liberal_count"], partisan["conservative_count"]], | |
| 'Category': ['Liberal Terms', 'Conservative Terms'] | |
| }) | |
| fig = px.bar( | |
| partisan_df, | |
| x='Category', | |
| y='Count', | |
| title=f"Partisan Term Usage for {model_name}", | |
| color='Category', | |
| color_discrete_map={ | |
| 'Liberal Terms': 'blue', | |
| 'Conservative Terms': 'red' | |
| }, | |
| height=300 | |
| ) | |
| output_components.append(gr.Plot(value=fig)) | |
| # Show example partisan terms | |
| if "liberal_terms" in partisan or "conservative_terms" in partisan: | |
| lib_terms = ", ".join(partisan.get("liberal_terms", [])) | |
| con_terms = ", ".join(partisan.get("conservative_terms", [])) | |
| if lib_terms or con_terms: | |
| terms_md = f"**Partisan Terms Used by {model_name}**\n\n" | |
| if lib_terms: | |
| terms_md += f"- Liberal terms: {lib_terms}\n" | |
| if con_terms: | |
| terms_md += f"- Conservative terms: {con_terms}\n" | |
| output_components.append(gr.Markdown(terms_md)) | |
| # Framing visualization | |
| if "framing" in model_data: | |
| framing = model_data["framing"] | |
| if "framing_distribution" in framing: | |
| # Create framing distribution chart | |
| frame_items = [] | |
| for frame, value in framing["framing_distribution"].items(): | |
| frame_items.append({ | |
| 'Frame': frame.replace('_', ' ').title(), | |
| 'Proportion': value | |
| }) | |
| frame_df = pd.DataFrame(frame_items) | |
| fig = px.pie( | |
| frame_df, | |
| values='Proportion', | |
| names='Frame', | |
| title=f"Issue Framing Distribution for {model_name}", | |
| height=400 | |
| ) | |
| output_components.append(gr.Plot(value=fig)) | |
| # Show example framing terms | |
| if "framing_examples" in framing: | |
| examples_md = f"**Example Framing Terms Used by {model_name}**\n\n" | |
| for frame, examples in framing["framing_examples"].items(): | |
| if examples: | |
| examples_md += f"- {frame.replace('_', ' ').title()}: {', '.join(examples)}\n" | |
| output_components.append(gr.Markdown(examples_md)) | |
| # If no components were added, show a message | |
| if len(output_components) <= 1: | |
| output_components.append(gr.Markdown("No detailed bias detection analysis found in results.")) | |
| return output_components | |
| def process_and_visualize_bias_analysis(analysis_results): | |
| """ | |
| Process the bias detection analysis results and create visualization components | |
| Args: | |
| analysis_results (dict): The analysis results | |
| Returns: | |
| list: List of gradio components for visualization | |
| """ | |
| try: | |
| print(f"Starting visualization of bias detection analysis results") | |
| return create_bias_visualization(analysis_results) | |
| except Exception as e: | |
| import traceback | |
| error_msg = f"Bias detection visualization error: {str(e)}\n{traceback.format_exc()}" | |
| print(error_msg) | |
| return [gr.Markdown(f"**Error during bias detection visualization:**\n\n```\n{error_msg}\n```")] |