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| """ | |
| Visualization for topic modeling analysis results | |
| """ | |
| from visualization.ngram_visualizer import create_ngram_visualization | |
| import gradio as gr | |
| import json | |
| import numpy as np | |
| import pandas as pd | |
| import plotly.express as px | |
| import plotly.graph_objects as go | |
| from plotly.subplots import make_subplots | |
| def create_topic_visualization(analysis_results): | |
| """ | |
| Create visualizations for topic modeling analysis results | |
| Args: | |
| analysis_results (dict): Analysis results from the topic modeling analysis | |
| Returns: | |
| list: List of gradio components with visualizations | |
| """ | |
| # Initialize output components list | |
| 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 Topic Modeling analysis if available | |
| if "topic_modeling" in analyses: | |
| topic_results = analyses["topic_modeling"] | |
| # Show method and number of topics | |
| method = topic_results.get("method", "lda").upper() | |
| n_topics = topic_results.get("n_topics", 3) | |
| output_components.append(gr.Markdown(f"## Topic Modeling Analysis ({method}, {n_topics} topics)")) | |
| # Show models being compared | |
| models = topic_results.get("models", []) | |
| if len(models) >= 2: | |
| output_components.append(gr.Markdown(f"### Comparing responses from {models[0]} and {models[1]}")) | |
| # Visualize topics | |
| topics = topic_results.get("topics", []) | |
| if topics: | |
| output_components.append(gr.Markdown("### Discovered Topics")) | |
| for topic in topics: | |
| topic_id = topic.get("id", 0) | |
| words = topic.get("words", []) | |
| weights = topic.get("weights", []) | |
| # Create topic word bar chart | |
| if words and weights and len(words) == len(weights): | |
| # Create dataframe for plotting | |
| df = pd.DataFrame({ | |
| 'word': words, | |
| 'weight': weights | |
| }) | |
| # Sort by weight | |
| df = df.sort_values('weight', ascending=False) | |
| # Create bar chart | |
| fig = px.bar( | |
| df, x='word', y='weight', | |
| title=f"Topic {topic_id+1} Top Words", | |
| labels={'word': 'Word', 'weight': 'Weight'}, | |
| height=300 | |
| ) | |
| output_components.append(gr.Plot(value=fig)) | |
| # Visualize topic distributions for each model | |
| model_topics = topic_results.get("model_topics", {}) | |
| if model_topics and all(model in model_topics for model in models): | |
| output_components.append(gr.Markdown("### Topic Distribution by Model")) | |
| # Create multi-model topic distribution comparison | |
| fig = go.Figure() | |
| for model in models: | |
| if model in model_topics: | |
| distribution = model_topics[model] | |
| fig.add_trace(go.Bar( | |
| x=[f"Topic {i+1}" for i in range(len(distribution))], | |
| y=distribution, | |
| name=model | |
| )) | |
| fig.update_layout( | |
| title="Topic Distributions Comparison", | |
| xaxis_title="Topic", | |
| yaxis_title="Weight", | |
| barmode='group', | |
| height=400 | |
| ) | |
| output_components.append(gr.Plot(value=fig)) | |
| # Visualize topic differences | |
| comparisons = topic_results.get("comparisons", {}) | |
| if comparisons: | |
| output_components.append(gr.Markdown("### Topic Distribution Differences")) | |
| for comparison_key, comparison_data in comparisons.items(): | |
| js_divergence = comparison_data.get("js_divergence", 0) | |
| topic_differences = comparison_data.get("topic_differences", []) | |
| output_components.append(gr.Markdown( | |
| f"**{comparison_key}** - Jensen-Shannon Divergence: {js_divergence:.4f}" | |
| )) | |
| if topic_differences: | |
| # Create DataFrame for plotting | |
| model1, model2 = comparison_key.split(" vs ") | |
| df_diff = pd.DataFrame(topic_differences) | |
| # Create bar chart for topic differences | |
| fig = go.Figure() | |
| fig.add_trace(go.Bar( | |
| x=[f"Topic {d['topic_id']+1}" for d in topic_differences], | |
| y=[d["model1_weight"] for d in topic_differences], | |
| name=model1 | |
| )) | |
| fig.add_trace(go.Bar( | |
| x=[f"Topic {d['topic_id']+1}" for d in topic_differences], | |
| y=[d["model2_weight"] for d in topic_differences], | |
| name=model2 | |
| )) | |
| fig.update_layout( | |
| title="Topic Weight Comparison", | |
| xaxis_title="Topic", | |
| yaxis_title="Weight", | |
| barmode='group', | |
| height=400 | |
| ) | |
| output_components.append(gr.Plot(value=fig)) | |
| # If no components were added, show a message | |
| if len(output_components) <= 1: | |
| output_components.append(gr.Markdown("No detailed Topic Modeling analysis found in results.")) | |
| return output_components | |
| def process_and_visualize_topic_analysis(analysis_results): | |
| """ | |
| Process the topic modeling 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 topic modeling analysis results") | |
| return create_topic_visualization(analysis_results) | |
| except Exception as e: | |
| import traceback | |
| error_msg = f"Topic modeling visualization error: {str(e)}\n{traceback.format_exc()}" | |
| print(error_msg) | |
| return [gr.Markdown(f"**Error during topic modeling visualization:**\n\n```\n{error_msg}\n```")] | |