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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()
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