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| import gradio as gr | |
| import json | |
| import pandas as pd | |
| import plotly.express as px | |
| import plotly.graph_objects as go | |
| from plotly.subplots import make_subplots | |
| def create_ngram_visualization(analysis_results): | |
| """ | |
| Create visualizations for n-gram analysis results | |
| Args: | |
| analysis_results (dict): Analysis results from the n-gram analysis | |
| 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 N-gram analysis if available | |
| if "ngram_analysis" in analyses: | |
| ngram_results = analyses["ngram_analysis"] | |
| # Check if there's an error in the analysis | |
| if "error" in ngram_results: | |
| output_components.append(gr.Markdown(f"**Error in N-gram analysis:** {ngram_results['error']}")) | |
| continue | |
| # Show models being compared | |
| models = ngram_results.get("models", []) | |
| ngram_size = ngram_results.get("ngram_size", 2) | |
| size_name = "Unigrams" if ngram_size == 1 else f"{ngram_size}-grams" | |
| if len(models) >= 2: | |
| output_components.append( | |
| gr.Markdown(f"### {size_name} Analysis: Comparing responses from {models[0]} and {models[1]}")) | |
| # Get important n-grams for each model | |
| important_ngrams = ngram_results.get("important_ngrams", {}) | |
| # Display important n-grams for each model | |
| if important_ngrams: | |
| for model_name, ngrams in important_ngrams.items(): | |
| output_components.append(gr.Markdown(f"#### Top {size_name} Used by {model_name}")) | |
| if ngrams: | |
| # Create a formatted list of n-grams for display | |
| ngram_list = [f"**{item['ngram']}** ({item['count']})" for item in ngrams[:10]] | |
| output_components.append(gr.Markdown(", ".join(ngram_list))) | |
| else: | |
| output_components.append(gr.Markdown("No significant n-grams found.")) | |
| # Only if we have enough data, create a bar chart | |
| if len(ngrams) >= 3: | |
| try: | |
| df = pd.DataFrame(ngrams) | |
| # Create bar chart for top n-grams | |
| fig = px.bar(df[:10], x='ngram', y='count', | |
| title=f"Top {size_name} Used by {model_name}", | |
| labels={'ngram': 'N-gram', 'count': 'Frequency'}, | |
| height=400) | |
| # Improve layout | |
| fig.update_layout( | |
| xaxis_title="N-gram", | |
| yaxis_title="Frequency", | |
| xaxis={'categoryorder': 'total descending'} | |
| ) | |
| output_components.append(gr.Plot(value=fig)) | |
| except Exception as e: | |
| output_components.append(gr.Markdown(f"Visualization error: {str(e)}")) | |
| # Visualize differential n-grams (n-grams with biggest frequency difference) | |
| diff_ngrams = ngram_results.get("differential_ngrams", []) | |
| ngram_matrix = ngram_results.get("ngram_count_matrix", {}) | |
| if diff_ngrams and ngram_matrix and len(diff_ngrams) > 0: | |
| output_components.append(gr.Markdown(f"### {size_name} with Biggest Frequency Differences")) | |
| # Create dataframe for plotting | |
| model1, model2 = models[0], models[1] | |
| diff_data = [] | |
| for ngram in diff_ngrams[:10]: # Limit to top 10 for readability | |
| if ngram in ngram_matrix: | |
| counts = ngram_matrix[ngram] | |
| model1_count = counts.get(model1, 0) | |
| model2_count = counts.get(model2, 0) | |
| # Only include if there's a meaningful difference | |
| if abs(model1_count - model2_count) > 0: | |
| output_components.append(gr.Markdown( | |
| f"- **{ngram}**: {model1}: {model1_count}, {model2}: {model2_count}" | |
| )) | |
| # Add similarity comparison if available | |
| if "comparisons" in ngram_results: | |
| output_components.append(gr.Markdown("### N-gram Similarity Metrics")) | |
| comparison_key = f"{models[0]} vs {models[1]}" | |
| if comparison_key in ngram_results["comparisons"]: | |
| metrics = ngram_results["comparisons"][comparison_key] | |
| common_count = metrics.get("common_ngram_count", 0) | |
| metrics_text = f""" | |
| - **Common {size_name}**: {common_count} {size_name.lower()} appear in both responses | |
| """ | |
| output_components.append(gr.Markdown(metrics_text)) | |
| # If no components were added other than header, show a message | |
| if len(output_components) <= 1: | |
| output_components.append(gr.Markdown(f"No detailed N-gram analysis found in results.")) | |
| return output_components | |
| def process_and_visualize_ngram_analysis(analysis_results): | |
| """ | |
| Process the n-gram 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 n-gram analysis results") | |
| return create_ngram_visualization(analysis_results) | |
| except Exception as e: | |
| import traceback | |
| error_msg = f"N-gram visualization error: {str(e)}\n{traceback.format_exc()}" | |
| print(error_msg) | |
| return [gr.Markdown(f"**Error during n-gram visualization:**\n\n```\n{error_msg}\n```")] | |