Update app.py

app.py

@@ -567,30 +567,42 @@ def clean_response(response_text):
 import traceback
 
 def generate_answer(message, choice, retrieval_mode, selected_model):
-    logging.debug(f"generate_answer called with choice: {choice} and retrieval_mode: {retrieval_mode}")
+    logging.debug(f"generate_answer called with choice: {choice}, retrieval_mode: {retrieval_mode}, and selected_model: {selected_model}")
 
     try:
         # Handle hotel-related queries
         if "hotel" in message.lower() or "hotels" in message.lower() and "birmingham" in message.lower():
+            logging.debug("Handling hotel-related query")
             response = fetch_google_hotels()
+            logging.debug(f"Hotel response: {response}")
             return response, extract_addresses(response)
         
         # Handle restaurant-related queries
         if "restaurant" in message.lower() or "restaurants" in message.lower() and "birmingham" in message.lower():
+            logging.debug("Handling restaurant-related query")
             response = fetch_yelp_restaurants()
+            logging.debug(f"Restaurant response: {response}")
             return response, extract_addresses(response)
 
         # Handle flight-related queries
         if "flight" in message.lower() or "flights" in message.lower() and "birmingham" in message.lower():
+            logging.debug("Handling flight-related query")
             response = fetch_google_flights()
+            logging.debug(f"Flight response: {response}")
             return response, extract_addresses(response)
 
+        # Retrieval-based response
         if retrieval_mode == "VDB":
+            logging.debug("Using VDB retrieval mode")
             if selected_model == chat_model:
+                logging.debug("Selected model: GPT-4o")
                 retriever = gpt_retriever
                 prompt_template = QA_CHAIN_PROMPT_1 if choice == "Details" else QA_CHAIN_PROMPT_2
                 context = retriever.get_relevant_documents(message)
+                logging.debug(f"Retrieved context: {context}")
+
                 prompt = prompt_template.format(context=context, question=message)
+                logging.debug(f"Generated prompt: {prompt}")
 
                 qa_chain = RetrievalQA.from_chain_type(
                     llm=chat_model,
@@ -599,17 +611,18 @@ def generate_answer(message, choice, retrieval_mode, selected_model):
                     chain_type_kwargs={"prompt": prompt_template}
                 )
                 response = qa_chain({"query": message})
+                logging.debug(f"GPT-4o response: {response}")
                 return response['result'], extract_addresses(response['result'])
 
             elif selected_model == phi_pipe:
+                logging.debug("Selected model: Phi-3.5")
                 retriever = phi_retriever
                 context_documents = retriever.get_relevant_documents(message)
                 context = "\n".join([doc.page_content for doc in context_documents])
+                logging.debug(f"Retrieved context for Phi-3.5: {context}")
 
-                # Integrating the custom context and question into the base prompt template
                 prompt = phi_base_template.format(context=context, question=message)
-
-                logging.debug(f"Phi-3.5 Prompt: {prompt}")
+                logging.debug(f"Generated Phi-3.5 prompt: {prompt}")
 
                 response = selected_model(prompt, **{
                     "max_new_tokens": 160,  # Increased to handle longer responses
@@ -628,13 +641,15 @@ def generate_answer(message, choice, retrieval_mode, selected_model):
                     return "No response generated.", []
 
         elif retrieval_mode == "KGF":
+            logging.debug("Using KGF retrieval mode")
             response = chain_neo4j.invoke({"question": message})
+            logging.debug(f"KGF response: {response}")
             return response, extract_addresses(response)
         else:
+            logging.error("Invalid retrieval mode selected.")
             return "Invalid retrieval mode selected.", []
 
     except Exception as e:
-        # Log the error details
         logging.error(f"Error in generate_answer: {str(e)}")
         logging.error(traceback.format_exc())
         return "Sorry, I encountered an error while processing your request.", []