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| import gradio as gr | |
| import logging | |
| from generator.compute_rmse_auc_roc_metrics import compute_rmse_auc_roc_metrics | |
| def launch_gradio(vector_store, dataset, gen_llm, val_llm): | |
| """ | |
| Launch the Gradio app with pre-initialized objects. | |
| """ | |
| def answer_question_with_metrics(query): | |
| try: | |
| logging.info(f"Processing query: {query}") | |
| # Generate metrics using the passed objects | |
| from main import generate_metrics | |
| response, metrics = generate_metrics(gen_llm, val_llm, vector_store, query, 1) | |
| response_text = f"Response: {response}\n\n" | |
| metrics_text = "Metrics:\n" | |
| for key, value in metrics.items(): | |
| if key != 'response': | |
| metrics_text += f"{key}: {value}\n" | |
| return response_text, metrics_text | |
| except Exception as e: | |
| logging.error(f"Error processing query: {e}") | |
| return f"An error occurred: {e}" | |
| def compute_and_display_metrics(): | |
| try: | |
| # Call the function to compute metrics | |
| relevance_rmse, utilization_rmse, adherence_auc = compute_rmse_auc_roc_metrics( | |
| gen_llm, val_llm, dataset, vector_store, 10 | |
| ) | |
| # Format the result for display | |
| result = ( | |
| f"Relevance RMSE Score: {relevance_rmse}\n" | |
| f"Utilization RMSE Score: {utilization_rmse}\n" | |
| f"Overall Adherence AUC-ROC: {adherence_auc}\n" | |
| ) | |
| return result | |
| except Exception as e: | |
| logging.error(f"Error during metrics computation: {e}") | |
| return f"An error occurred: {e}" | |
| # Define Gradio Blocks layout | |
| with gr.Blocks() as interface: | |
| interface.title = "Real Time RAG Pipeline Q&A" | |
| gr.Markdown("### Real Time RAG Pipeline Q&A") # Heading | |
| gr.Markdown("Ask a question and get a response with metrics calculated from the RAG pipeline.") # Description | |
| with gr.Row(): | |
| query_input = gr.Textbox(label="Ask a question", placeholder="Type your query here") | |
| with gr.Row(): | |
| clear_query_button = gr.Button("Clear") # Clear button | |
| submit_button = gr.Button("Submit", variant="primary") # Submit button | |
| with gr.Row(): | |
| answer_output = gr.Textbox(label="Response", placeholder="Response will appear here") | |
| with gr.Row(): | |
| metrics_output = gr.Textbox(label="Metrics", placeholder="Metrics will appear here") | |
| with gr.Row(): | |
| compute_rmse_button = gr.Button("Compute RMSE & AU-ROC", variant="primary") | |
| rmse_output = gr.Textbox(label="RMSE & AU-ROC Score", placeholder="RMSE & AU-ROC score will appear here") | |
| # Define button actions | |
| submit_button.click(fn=answer_question_with_metrics, inputs=[query_input], outputs=[answer_output, metrics_output]) | |
| clear_query_button.click(fn=lambda: "", outputs=[query_input]) # Clear query input | |
| compute_rmse_button.click(fn=compute_and_display_metrics, outputs=[rmse_output]) | |
| interface.launch() | |