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| """ | |
| Example script demonstrating the Hybrid LLM Hallucination Detection System | |
| """ | |
| from analyzer import HallucinationAnalyzer | |
| def run_example(): | |
| """ | |
| Run a simple example analysis. | |
| """ | |
| print("Initializing Hybrid LLM Hallucination Detection System...") | |
| print("This may take a moment on first run (downloading models)...\n") | |
| # Initialize analyzer — ground truth is loaded automatically from TruthfulQA | |
| analyzer = HallucinationAnalyzer( | |
| model_name="gpt2", | |
| semantic_threshold=0.80 # adjust if prompts don't match dataset questions | |
| ) | |
| # Example prompt from ground truth | |
| prompt = "What is the capital of France?" | |
| # Run analysis | |
| results = analyzer.analyze( | |
| prompt=prompt, | |
| num_responses=5, | |
| max_length=30, | |
| temperature=0.8 | |
| ) | |
| # Print summary | |
| analyzer.print_summary(results) | |
| # Show entropy plot | |
| print("\nDisplaying entropy curve...") | |
| plt = analyzer.plot_entropy_curve(results['entropy']['entropy_curve']) | |
| plt.savefig('example_entropy_curve.png', dpi=300, bbox_inches='tight') | |
| print("Entropy curve saved to 'example_entropy_curve.png'") | |
| print("\n" + "="*80) | |
| print("Example complete!") | |
| print("="*80) | |
| print("\nTo run the Streamlit UI, use: streamlit run app.py") | |
| print("To run custom prompts, use: python main.py --prompt \"Your question here\"") | |
| if __name__ == "__main__": | |
| run_example() | |