examples/feedback_loop_example.py

"""
Feedback Loop Example
Demonstrates collecting and learning from user feedback
"""

from feedback import get_feedback_collector, get_feedback_analyzer, FeedbackCategory


def example_collect_ratings():
    """Example: Collect user ratings"""
    print("=" * 60)
    print("COLLECTING USER RATINGS")
    print("=" * 60)
    
    collector = get_feedback_collector()
    
    # Example 1: High rating (5 stars)
    print("\n✅ Example 1: User loves the response")
    feedback_id = collector.collect_rating(
        user_id="user123",
        agent_name="nutrition_agent",
        user_message="Tôi muốn giảm cân, nên ăn gì?",
        agent_response="Để giảm cân hiệu quả, bạn nên ăn nhiều rau xanh, protein...",
        rating=5,
        category=FeedbackCategory.HELPFULNESS,
        comment="Rất hữu ích và chi tiết!"
    )
    print(f"   Feedback ID: {feedback_id}")
    print(f"   Rating: 5/5 ⭐⭐⭐⭐⭐")
    
    # Example 2: Low rating (2 stars)
    print("\n❌ Example 2: User unhappy with response")
    feedback_id = collector.collect_rating(
        user_id="user456",
        agent_name="nutrition_agent",
        user_message="Tôi bị tiểu đường, ăn gì được?",
        agent_response="Bạn nên ăn ít đường.",
        rating=2,
        category=FeedbackCategory.COMPLETENESS,
        comment="Quá chung chung, không cụ thể"
    )
    print(f"   Feedback ID: {feedback_id}")
    print(f"   Rating: 2/5 ⭐⭐")
    
    # Example 3: Thumbs up
    print("\n👍 Example 3: Quick thumbs up")
    feedback_id = collector.collect_thumbs(
        user_id="user789",
        agent_name="exercise_agent",
        user_message="Tập gì để giảm mỡ bụng?",
        agent_response="Bạn nên tập plank, crunches, và cardio...",
        is_positive=True,
        comment="Hay!"
    )
    print(f"   Feedback ID: {feedback_id}")
    print(f"   Thumbs: 👍")


def example_collect_corrections():
    """Example: Collect user corrections"""
    print("\n" + "=" * 60)
    print("COLLECTING USER CORRECTIONS")
    print("=" * 60)
    
    collector = get_feedback_collector()
    
    # Example: User corrects wrong information
    print("\n📝 User corrects incorrect BMI calculation")
    feedback_id = collector.collect_correction(
        user_id="user123",
        agent_name="nutrition_agent",
        user_message="Tôi 70kg, 175cm, BMI của tôi là bao nhiêu?",
        agent_response="BMI của bạn là 24.5",  # Wrong!
        corrected_response="BMI của bạn là 22.9 (70 / 1.75²)",
        correction_reason="calculation_error"
    )
    print(f"   Correction ID: {feedback_id}")
    print(f"   Original: BMI = 24.5 ❌")
    print(f"   Corrected: BMI = 22.9 ✅")


def example_report_issue():
    """Example: Report problematic response"""
    print("\n" + "=" * 60)
    print("REPORTING ISSUES")
    print("=" * 60)
    
    collector = get_feedback_collector()
    
    # Example: Report harmful advice
    print("\n⚠️ User reports harmful medical advice")
    report_id = collector.report_issue(
        user_id="user999",
        agent_name="symptom_agent",
        user_message="Tôi bị đau ngực dữ dội",
        agent_response="Bạn nên nghỉ ngơi, uống nước",
        issue_type="harmful",
        description="Đau ngực dữ dội cần đi bệnh viện ngay, không nên chỉ nghỉ ngơi",
        severity="critical"
    )
    print(f"   Report ID: {report_id}")
    print(f"   Severity: CRITICAL 🚨")


def example_analyze_feedback():
    """Example: Analyze feedback to find patterns"""
    print("\n" + "=" * 60)
    print("ANALYZING FEEDBACK")
    print("=" * 60)
    
    collector = get_feedback_collector()
    
    # Add more sample data
    print("\n📊 Adding sample feedback data...")
    for i in range(10):
        collector.collect_rating(
            user_id=f"user{i}",
            agent_name="nutrition_agent",
            user_message=f"Question {i}",
            agent_response=f"Response {i}",
            rating=4 if i % 2 == 0 else 3,
            category=FeedbackCategory.HELPFULNESS
        )
    
    # Get statistics
    print("\n📈 Feedback Statistics:")
    stats = collector.get_feedback_stats(agent_name="nutrition_agent")
    print(f"   Total ratings: {stats['total_ratings']}")
    print(f"   Average rating: {stats['average_rating']:.1f}/5.0")
    print(f"   Rating distribution:")
    for rating in [5, 4, 3, 2, 1]:
        count = stats['rating_distribution'][rating]
        print(f"      {rating} stars: {count}")
    
    # Analyze performance
    print("\n🔍 Performance Analysis:")
    analyzer = get_feedback_analyzer(collector)
    analysis = analyzer.analyze_agent_performance("nutrition_agent")
    
    print(f"   Overall rating: {analysis['overall_rating']:.1f}/5.0")
    
    if analysis['strengths']:
        print(f"\n   Strengths:")
        for strength in analysis['strengths']:
            print(f"      ✅ {strength}")
    
    if analysis['weaknesses']:
        print(f"\n   Weaknesses:")
        for weakness in analysis['weaknesses']:
            print(f"      ⚠️ {weakness}")


def example_get_insights():
    """Example: Get actionable insights"""
    print("\n" + "=" * 60)
    print("ACTIONABLE INSIGHTS")
    print("=" * 60)
    
    collector = get_feedback_collector()
    analyzer = get_feedback_analyzer(collector)
    
    # Get insights
    insights = analyzer.get_actionable_insights("nutrition_agent", limit=3)
    
    if insights:
        print("\n💡 Top Improvement Opportunities:")
        for i, insight in enumerate(insights, 1):
            print(f"\n   {i}. [{insight['priority'].upper()}] {insight['category']}")
            print(f"      Issue: {insight['issue']}")
            print(f"      Action: {insight['action']}")
            if insight['examples']:
                print(f"      Examples: {', '.join(insight['examples'][:2])}")
    else:
        print("\n   No insights available yet. Collect more feedback!")


def example_generate_report():
    """Example: Generate improvement report"""
    print("\n" + "=" * 60)
    print("IMPROVEMENT REPORT")
    print("=" * 60)
    
    collector = get_feedback_collector()
    analyzer = get_feedback_analyzer(collector)
    
    # Generate report
    report = analyzer.generate_improvement_report("nutrition_agent")
    print(report)


def example_export_for_training():
    """Example: Export feedback for fine-tuning"""
    print("\n" + "=" * 60)
    print("EXPORT FOR FINE-TUNING")
    print("=" * 60)
    
    collector = get_feedback_collector()
    
    # Export high-quality feedback
    print("\n📦 Exporting high-quality feedback (rating >= 4)...")
    output_file = collector.export_for_fine_tuning(
        agent_name="nutrition_agent",
        min_rating=4,
        include_corrections=True
    )
    
    print(f"   ✅ Exported to: {output_file}")
    print(f"   Ready for fine-tuning!")


def example_compare_agents():
    """Example: Compare agent performance"""
    print("\n" + "=" * 60)
    print("AGENT COMPARISON")
    print("=" * 60)
    
    collector = get_feedback_collector()
    
    # Add feedback for different agents
    print("\n📊 Adding feedback for multiple agents...")
    agents = ["nutrition_agent", "exercise_agent", "symptom_agent"]
    
    for agent in agents:
        for i in range(5):
            rating = 5 if agent == "nutrition_agent" else (4 if agent == "exercise_agent" else 3)
            collector.collect_rating(
                user_id=f"user{i}",
                agent_name=agent,
                user_message=f"Question for {agent}",
                agent_response=f"Response from {agent}",
                rating=rating
            )
    
    # Compare
    analyzer = get_feedback_analyzer(collector)
    comparison = analyzer.compare_agents()
    
    print(f"\n🏆 Agent Rankings:")
    for i, agent in enumerate(comparison['agents'], 1):
        print(f"   {i}. {agent['agent']}: {agent['average_rating']:.1f}/5.0 ({agent['total_feedback']} feedback)")
    
    if comparison['best_agent']:
        print(f"\n   Best: {comparison['best_agent']['agent']} 🥇")
    
    if comparison['worst_agent']:
        print(f"   Needs improvement: {comparison['worst_agent']['agent']} ⚠️")


if __name__ == '__main__':
    example_collect_ratings()
    example_collect_corrections()
    example_report_issue()
    example_analyze_feedback()
    example_get_insights()
    example_generate_report()
    example_export_for_training()
    example_compare_agents()
    
    print("\n" + "=" * 60)
    print("✅ FEEDBACK LOOP DEMO COMPLETE!")
    print("=" * 60)
    print("\nNext steps:")
    print("1. Integrate feedback collection into your UI")
    print("2. Review feedback regularly")
    print("3. Use insights to improve agents")
    print("4. Export high-quality feedback for fine-tuning")
    print("5. Monitor trends and act on critical issues")