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| import gradio as gr | |
| import logging | |
| # Set up logging | |
| logger = logging.getLogger('gradio_app.processors.ngram') | |
| def process_ngram_analysis(analysis_results, prompt, analyses): | |
| """ | |
| Process N-gram 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 | |
| """ | |
| visualization_area_visible = True | |
| ngram_results = analyses["ngram_analysis"] | |
| 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: | |
| from analysis_runner import default_no_visualization | |
| return default_no_visualization(analysis_results) | |
| prompt_title_visible = True | |
| prompt_title_value = f"## Analysis of Prompt: \"{prompt[:100]}...\"" | |
| models_compared_visible = True | |
| models_compared_value = f"### {size_name} Analysis: Comparing responses from {models[0]} and {models[1]}" | |
| # Extract and format information for display | |
| model1_name = models[0] | |
| model2_name = models[1] | |
| # Format important n-grams for each model | |
| important_ngrams = ngram_results.get("important_ngrams", {}) | |
| model1_title_visible = False | |
| model1_title_value = "" | |
| model1_words_visible = False | |
| model1_words_value = "" | |
| if model1_name in important_ngrams: | |
| model1_title_visible = True | |
| model1_title_value = f"#### Top {size_name} Used by {model1_name}" | |
| ngram_list = [f"**{item['ngram']}** ({item['count']})" for item in important_ngrams[model1_name][:10]] | |
| model1_words_visible = True | |
| model1_words_value = ", ".join(ngram_list) | |
| model2_title_visible = False | |
| model2_title_value = "" | |
| model2_words_visible = False | |
| model2_words_value = "" | |
| if model2_name in important_ngrams: | |
| model2_title_visible = True | |
| model2_title_value = f"#### Top {size_name} Used by {model2_name}" | |
| ngram_list = [f"**{item['ngram']}** ({item['count']})" for item in important_ngrams[model2_name][:10]] | |
| model2_words_visible = True | |
| model2_words_value = ", ".join(ngram_list) | |
| similarity_title_visible = False | |
| similarity_metrics_visible = False | |
| similarity_metrics_value = "" | |
| # Format similarity metrics if available | |
| if "comparisons" in ngram_results: | |
| comparison_key = f"{model1_name} vs {model2_name}" | |
| if comparison_key in ngram_results["comparisons"]: | |
| metrics = ngram_results["comparisons"][comparison_key] | |
| common_count = metrics.get("common_ngram_count", 0) | |
| similarity_title_visible = True | |
| similarity_metrics_visible = True | |
| similarity_metrics_value = f""" | |
| - **Common {size_name}**: {common_count} {size_name.lower()} appear in both responses | |
| """ | |
| return ( | |
| analysis_results, # analysis_results_state | |
| False, # analysis_output visibility | |
| True, # visualization_area_visible | |
| gr.update(visible=True), # analysis_title | |
| gr.update(visible=prompt_title_visible, value=prompt_title_value), # prompt_title | |
| gr.update(visible=models_compared_visible, value=models_compared_value), # models_compared | |
| gr.update(visible=model1_title_visible, value=model1_title_value), # model1_title | |
| gr.update(visible=model1_words_visible, value=model1_words_value), # model1_words | |
| gr.update(visible=model2_title_visible, value=model2_title_value), # model2_title | |
| gr.update(visible=model2_words_visible, value=model2_words_value), # model2_words | |
| gr.update(visible=similarity_title_visible), # similarity_metrics_title | |
| gr.update(visible=similarity_metrics_visible, value=similarity_metrics_value), # similarity_metrics | |
| False, # status_message_visible | |
| gr.update(visible=False), # status_message | |
| gr.update(visible=False) # bias_visualizations - Not visible for N-gram analysis | |
| ) |