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"""
Improved visualization for topic modeling analysis results
"""
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
import logging
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger('topic_visualizer')
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:
logger.warning("No valid analysis results found")
return [gr.Markdown("No analysis results found.")]
try:
# 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"]
# Check for errors in the analysis
if "error" in topic_results:
error_msg = topic_results.get("error", "Unknown error in topic modeling")
logger.warning(f"Topic modeling error: {error_msg}")
output_components.append(gr.Markdown(f"**Error in topic modeling analysis:** {error_msg}"))
continue
# Show method and number of topics
method = topic_results.get("method", "lda").upper()
n_topics = topic_results.get("n_topics", 3)
logger.info(f"Creating visualization for {method} with {n_topics} topics")
# Get models being compared
models = topic_results.get("models", [])
if not models or len(models) < 2:
logger.warning("Not enough models found in results")
output_components.append(gr.Markdown("Topic modeling requires at least two models to compare."))
continue
output_components.append(gr.Markdown(f"### Topic Modeling Analysis ({method}, {n_topics} topics)"))
output_components.append(gr.Markdown(f"Comparing responses from **{models[0]}** and **{models[1]}**"))
# Visualize discovered topics
topics = topic_results.get("topics", [])
if topics:
output_components.append(gr.Markdown("#### Discovered Topics"))
# Display topics in a more readable format
for i, topic in enumerate(topics):
topic_id = topic.get("id", i)
words = topic.get("words", [])
if words:
topic_words = ", ".join(words[:5]) # Show top 5 words
output_components.append(gr.Markdown(f"**Topic {topic_id+1}**: {topic_words}"))
# 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"))
# Display topic distributions in a readable format
for model in models:
if model in model_topics:
dist = model_topics[model]
# Format the distribution
dist_str = ", ".join([f"Topic {i+1}: {v:.2f}" for i, v in enumerate(dist[:n_topics])])
output_components.append(gr.Markdown(f"**{model}**: {dist_str}"))
# Create multi-model topic distribution visualization
try:
# Prepare data for visualization
model_data = []
for model in models:
if model in model_topics:
dist = model_topics[model]
for i, weight in enumerate(dist[:n_topics]):
model_data.append({
"Model": model,
"Topic": f"Topic {i+1}",
"Weight": weight
})
if model_data:
df = pd.DataFrame(model_data)
# Create grouped bar chart
fig = px.bar(
df,
x="Topic",
y="Weight",
color="Model",
title="Topic Distribution Comparison",
barmode="group",
height=400
)
fig.update_layout(
xaxis_title="Topic",
yaxis_title="Weight",
legend_title="Model"
)
output_components.append(gr.Plot(value=fig))
except Exception as e:
logger.error(f"Error creating topic distribution plot: {str(e)}")
output_components.append(gr.Markdown(f"*Error creating visualization: {str(e)}*"))
# Display similarity metrics
comparisons = topic_results.get("comparisons", {})
if comparisons:
output_components.append(gr.Markdown("#### Similarity Metrics"))
for comparison_key, comparison_data in comparisons.items():
js_div = comparison_data.get("js_divergence", 0)
# Jensen-Shannon divergence interpretation
similarity_text = ""
if js_div < 0.2:
similarity_text = "very similar"
elif js_div < 0.4:
similarity_text = "somewhat similar"
elif js_div < 0.6:
similarity_text = "moderately different"
else:
similarity_text = "very different"
output_components.append(gr.Markdown(
f"**Topic Distribution Divergence**: {js_div:.4f} - Topic distributions are {similarity_text}"
))
# Explain what the metric means
output_components.append(gr.Markdown(
"*Lower divergence values indicate more similar topic distributions between models*"
))
except Exception as e:
logger.error(f"Error in create_topic_visualization: {str(e)}")
output_components.append(gr.Markdown(f"**Error creating topic visualization:** {str(e)}"))
# If no components were added, show a message
if len(output_components) == 0:
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:
logger.info(f"Starting visualization of topic modeling analysis results")
# Debug output - print the structure of analysis_results
if "analyses" in analysis_results:
for prompt, analyses in analysis_results["analyses"].items():
if "topic_modeling" in analyses:
topic_results = analyses["topic_modeling"]
logger.info(f"Found topic_modeling results with keys: {topic_results.keys()}")
if "models" in topic_results:
logger.info(f"Models: {topic_results['models']}")
if "topics" in topic_results:
logger.info(f"Found {len(topic_results['topics'])} topics")
if "model_topics" in topic_results:
logger.info(f"Model_topics keys: {topic_results['model_topics'].keys()}")
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()}"
logger.error(error_msg)
return [gr.Markdown(f"**Error during topic modeling visualization:**\n\n```\n{error_msg}\n```")]
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