discourse/analysis/demo.py

#!/usr/bin/env python3
"""
Demo script for testing the VoxPop AI Analysis Service.
"""

import requests
import json
import time
import argparse
from pprint import pprint

def main():
    """Run the demo."""
    parser = argparse.ArgumentParser(description="Test the AI Analysis Service")
    parser.add_argument("--host", default="http://localhost:8000", help="Service host")
    args = parser.parse_args()
    
    host = args.host
    
    print("VoxPop AI Analysis Service Demo")
    print("===============================")
    print(f"Connecting to {host}")
    
    # Check service status
    try:
        response = requests.get(f"{host}/status")
        response.raise_for_status()
        status = response.json()
        print("\nService Status:")
        print(f"  Status: {status.get('status')}")
        print(f"  Version: {status.get('version')}")
        print(f"  Last Analysis: {status.get('last_analysis')}")
        
        # Print scheduled jobs
        print("\nScheduled Jobs:")
        for job_id, job in status.get("scheduled_jobs", {}).items():
            print(f"  {job_id}: Runs={job.get('runs')}, Errors={job.get('errors')}")
    except Exception as e:
        print(f"Error checking status: {e}")
        return
    
    # Trigger an analysis run
    print("\nTriggering analysis run...")
    try:
        response = requests.post(f"{host}/run-now")
        response.raise_for_status()
        print(f"  Analysis triggered at: {response.json().get('timestamp')}")
    except Exception as e:
        print(f"Error triggering analysis: {e}")
        return
    
    # Wait for analysis to complete
    print("  Waiting for analysis to complete (10 seconds)...")
    time.sleep(10)
    
    # Get analysis results
    try:
        response = requests.get(f"{host}/analyze")
        response.raise_for_status()
        analysis = response.json()
        
        # Print summary
        perspectives = analysis.get("perspectives", [])
        insights = analysis.get("insights", [])
        
        print("\nAnalysis Results:")
        print(f"  Analyzed {len(perspectives)} perspectives")
        print(f"  Generated {len(insights)} insights")
        
        # Print sentiment distribution
        sentiments = [p.get("sentiment", "unknown") for p in perspectives]
        sentiment_counts = {
            "positive": sentiments.count("positive"),
            "negative": sentiments.count("negative"),
            "neutral": sentiments.count("neutral"),
            "unknown": sentiments.count("unknown")
        }
        
        print("\nSentiment Distribution:")
        for sentiment, count in sentiment_counts.items():
            if count > 0:
                percentage = (count / len(perspectives)) * 100
                print(f"  {sentiment.capitalize()}: {count} ({percentage:.1f}%)")
        
        # Print insights
        print("\nInsights:")
        for i, insight in enumerate(insights):
            print(f"  {i+1}. {insight.get('summary')} (Confidence: {insight.get('confidence'):.2f})")
        
        # Print a few example perspectives
        print("\nExample Perspectives:")
        for i, perspective in enumerate(perspectives[:5]):
            print(f"  {i+1}. [{perspective.get('sentiment', 'unknown')}] {perspective.get('text')[:100]}...")
        
    except Exception as e:
        print(f"Error getting analysis results: {e}")
        return
    
    # Get consolidated insights
    try:
        response = requests.get(f"{host}/insights")
        response.raise_for_status()
        consolidated = response.json()
        
        insights = consolidated.get("consolidated_insights", [])
        metadata = consolidated.get("metadata", {})
        
        print("\nConsolidated Insights:")
        print(f"  Period: {metadata.get('period_days')} days")
        print(f"  Total insights: {metadata.get('total_insights')}")
        print(f"  Consolidated count: {metadata.get('consolidated_count')}")
        
        # Print top insights
        print("\nTop Insights:")
        for i, insight in enumerate(insights[:5]):
            print(f"  {i+1}. {insight.get('summary')} (Confidence: {insight.get('confidence'):.2f}, Count: {insight.get('count')})")
        
    except Exception as e:
        print(f"Error getting consolidated insights: {e}")
    
    print("\nDemo completed!")

if __name__ == "__main__":
    main()