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| """ | |
| Example script demonstrating the Hybrid LLM Hallucination Detection System. | |
| """ | |
| import matplotlib.pyplot as plt | |
| 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") | |
| analyzer = HallucinationAnalyzer( | |
| model_name="gpt2", | |
| semantic_threshold=0.80, | |
| ) | |
| prompt = "What is the capital of France?" | |
| results = analyzer.analyze( | |
| prompt=prompt, | |
| num_responses=5, | |
| max_length=30, | |
| temperature=0.8, | |
| ) | |
| analyzer.print_summary(results) | |
| print("\nDisplaying eigenvalue spectrum...") | |
| eigenvalues = results["eigen"].get("eigenvalues", []) | |
| if eigenvalues: | |
| analyzer.plot_eigenvalue_spectrum( | |
| eigenvalues, | |
| save_path="example_eigenvalue_spectrum.png", | |
| ) | |
| plt.close("all") | |
| print("Eigenvalue spectrum saved to 'example_eigenvalue_spectrum.png'") | |
| else: | |
| print("No eigenvalues available to plot.") | |
| 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() | |