Upload 2 files

app.py

@@ -1,80 +1,33 @@
+
 import openai
 import gradio as gr
 from full_chain import get_response
 import os
-import logging
 
-# Configure logging
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - %(levelname)s - %(message)s',
-    handlers=[
-        logging.FileHandler('app.log'),
-        logging.StreamHandler()
-    ]
-)
-logger = logging.getLogger(__name__)
+api_key = os.getenv("OPENAI_API_KEY")
+client = openai.OpenAI(api_key=api_key)
 
-# Initialize OpenAI client
-try:
-    api_key = os.getenv("OPENAI_API_KEY")
-    if not api_key:
-        raise ValueError("OPENAI_API_KEY environment variable not set")
-    client = openai.OpenAI(api_key=api_key)
-    logger.info("OpenAI client initialized successfully")
-except Exception as e:
-    logger.error(f"Failed to initialize OpenAI client: {str(e)}")
-    raise
 
 def create_hyperlink(url, title, domain):
-    """Create HTML hyperlink with domain information."""
-    return f"<a href='{url}'>{title}</a> ({domain})"
+    return f"<a href='{url}'>{title}</a>" + " (" + domain + ")"
+
 
 def predict(message, history):
-    """Process user message and return response with source links."""
-    try:
-        logger.info(f"Processing new query: {message}")
-        
-        # Get response from the chain
-        responder, links, titles, domains = get_response(message, rerank_type="crossencoder")
-        logger.info(f"Received response with {len(links)} sources")
-        
-        # Create hyperlinks for sources
-        formatted_links = [create_hyperlink(link, title, domain) 
-                         for link, title, domain in zip(links, titles, domains)]
-        
-        # Combine response with sources
-        out = responder + "\n" + "\n".join(formatted_links)
-        
-        logger.info("Response generated successfully")
-        return out
+    print("get_responses: ")
+    # print(get_response(message, rerank_type="crossencoder"))
+    responder, links, titles, domains = get_response(message, rerank_type="crossencoder")
+    for i in range(len(links)):
+        links[i] = create_hyperlink(links[i], titles[i], domains[i])
 
-    except Exception as e:
-        error_msg = f"Error processing query: {str(e)}"
-        logger.error(error_msg)
-        return f"An error occurred while processing your request: {str(e)}"
+    out = responder + "\n" + "\n".join(links)
 
-# Define example queries
-EXAMPLE_QUERIES = [
-    "How many Americans Smoke?",
-    "What are some measures taken by the Indian Government to reduce the smoking population?",
-    "Does smoking negatively affect my health?"
-]
+    return out
 
-# Initialize and launch Gradio interface
-def main():
-    try:
-        interface = gr.ChatInterface(
-            predict,
-            examples=EXAMPLE_QUERIES,
-            title="Tobacco Information Assistant",
-            description="Ask questions about tobacco-related topics and get answers with reliable sources."
-        )
-        logger.info("Starting Gradio interface")
-        interface.launch()
-    except Exception as e:
-        logger.error(f"Failed to launch Gradio interface: {str(e)}")
-        raise
 
-if __name__ == "__main__":
-    main()
+gr.ChatInterface(predict,
+                examples = [
+                    "How many Americans Smoke?",
+                    "What are some measures taken by the Indian Government to reduce the smoking population?",
+                    "Does smoking negatively affect my health?"
+                ]
+                ).launch()

full_chain.py

@@ -9,29 +9,16 @@ from feed_to_llm_v2 import feed_articles_to_gpt_with_links
 
 
 def get_response(question, rerank_type="crossencoder", llm_type="chat"):
-    
-    try:
-        
-        csv_path = save_solr_articles_full(question, keyword_type="rake")
-        
-        reranked_out = crossencoder_rerank_answer(csv_path, question)
-       
-        
-        # Prepare source metadata for citations
-        citations = [
-            {"title": article["title"], "url": article["url"], "source": article["source"]}
-            for article in reranked_out
-        ]
-        
-   
-        result = feed_articles_to_gpt_with_links(reranked_out, question, citations)
-
-        
-        return result
-    except Exception as e:
-        return "", [], [], []
+    csv_path = save_solr_articles_full(question, keyword_type="rake")
+    reranked_out = crossencoder_rerank_answer(csv_path, question)
+    return feed_articles_to_gpt_with_links(reranked_out, question)
 
 
+    # save_path = save_solr_articles_full(question)
+    # information = crossencoder_rerank_answer(save_path, question)
+    # response, links, titles = feed_articles_to_gpt_with_links(information, question)
+    #
+    # return response, links, titles