Update app.py

app.py

@@ -593,129 +593,152 @@ Detailed Answer:
 
 import traceback
 
-# def generate_answer(message, choice, retrieval_mode, selected_model):
-#     logging.debug(f"generate_answer called with choice: {choice}, retrieval_mode: {retrieval_mode}, and selected_model: {selected_model}")
-
-#     # Logic for disabling options for Phi-3.5
-#     if selected_model == "LM-2":
-#         choice = None
-#         retrieval_mode = None
-
-#     try:
-#         # Select the appropriate template based on the choice and model
-#         if choice == "Details" and selected_model == chat_model1:  # GPT-4o-mini
-#             prompt_template = PromptTemplate(input_variables=["context", "question"], template=gpt4o_mini_template_details)
-#         elif choice == "Details":
-#             prompt_template = QA_CHAIN_PROMPT_1
-#         elif choice == "Conversational":
-#             prompt_template = QA_CHAIN_PROMPT_2
-#         else:
-#             prompt_template = QA_CHAIN_PROMPT_1  # Fallback to template1
-
-#         # # 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: LM-1")
-#                 retriever = gpt_retriever
-#                 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,
-#                     chain_type="stuff",
-#                     retriever=retriever,
-#                     chain_type_kwargs={"prompt": prompt_template}
-#                 )
-#                 response = qa_chain({"query": message})
-#                 logging.debug(f"LM-1 response: {response}")
-#                 return response['result'], extract_addresses(response['result'])
-            
-#             elif selected_model == chat_model1:
-#                 logging.debug("Selected model: LM-3")
-#                 retriever = gpt_retriever
-#                 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_model1,
-#                     chain_type="stuff",
-#                     retriever=retriever,
-#                     chain_type_kwargs={"prompt": prompt_template}
-#                 )
-#                 response = qa_chain({"query": message})
-#                 logging.debug(f"LM-3 response: {response}")
-#                 return response['result'], extract_addresses(response['result']) 
+def generate_answer(message, choice, retrieval_mode, selected_model):
+    logging.debug(f"generate_answer called with choice: {choice}, retrieval_mode: {retrieval_mode}, and selected_model: {selected_model}")
+
+    # Logic for disabling options for Phi-3.5
+    if selected_model == "LM-2":
+        choice = None
+        retrieval_mode = None
 
+    try:
+        # Select the appropriate template based on the choice and model
+        if choice == "Details" and selected_model == chat_model1:  # GPT-4o-mini
+            prompt_template = PromptTemplate(input_variables=["context", "question"], template=gpt4o_mini_template_details)
+        elif choice == "Details":
+            prompt_template = QA_CHAIN_PROMPT_1
+        elif choice == "Conversational":
+            prompt_template = QA_CHAIN_PROMPT_2
+        else:
+            prompt_template = QA_CHAIN_PROMPT_1  # Fallback to template1
+
+        # # 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: LM-1")
+                retriever = gpt_retriever
+                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,
+                    chain_type="stuff",
+                    retriever=retriever,
+                    chain_type_kwargs={"prompt": prompt_template}
+                )
+                response = qa_chain({"query": message})
+                logging.debug(f"LM-1 response: {response}")
+                return response['result'], extract_addresses(response['result'])
             
+            elif selected_model == chat_model1:
+                logging.debug("Selected model: LM-3")
+                retriever = gpt_retriever
+                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_model1,
+                    chain_type="stuff",
+                    retriever=retriever,
+                    chain_type_kwargs={"prompt": prompt_template}
+                )
+                response = qa_chain({"query": message})
+                logging.debug(f"LM-3 response: {response}")
+                return response['result'], extract_addresses(response['result']) 
+            #-----------------------------------------------------------------------------------------------------------------
+
+            # Modify the Phi-3.5 prompt to include the selected file
+            elif selected_model == phi_pipe:
+                retriever = phi_retriever
+                context_documents = retriever.get_relevant_documents(message)
+                context = "\n".join([doc.page_content for doc in context_documents])
+        
+                prompt = phi_custom_template.format(context=context, question=message, document_name=selected_file)
+                response = selected_model(prompt, **{
+                    "max_new_tokens": 250,
+                    "return_full_text": True,
+                    "temperature": 0.0,
+                    "do_sample": False,
+                })
+        
+                if response:
+                    generated_text = response[0]['generated_text']
+                    cleaned_response = clean_response(generated_text)
+                    return cleaned_response
+                else:
+                    return "No response generated.", []
+            
+
             
-#             elif selected_model == phi_pipe:
-#                 logging.debug("Selected model: LM-2")
-#                 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 LM-2: {context}")
-
-#                 # Use the correct template variable
-#                 prompt = phi_custom_template.format(context=context, question=message)
-#                 logging.debug(f"Generated LM-2 prompt: {prompt}")
-
-#                 response = selected_model(prompt, **{
-#                     "max_new_tokens": 250,
-#                     "return_full_text": True,
-#                     "temperature": 0.0,
-#                     "do_sample": False,
-#                 })
-
-#                 if response:
-#                     generated_text = response[0]['generated_text']
-#                     logging.debug(f"LM-2 Response: {generated_text}")
-#                     cleaned_response = clean_response(generated_text)
-#                     return cleaned_response, extract_addresses(cleaned_response)
-#                 else:
-#                     logging.error("LM-2 did not return any response.")
-#                     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:
-#         logging.error(f"Error in generate_answer: {str(e)}")
-#         logging.error(traceback.format_exc())
-#         return "Sorry, I encountered an error while processing your request.", []
+            #------------------------------------------------------------------------------------------------------------
+            # elif selected_model == phi_pipe:
+            #     logging.debug("Selected model: LM-2")
+            #     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 LM-2: {context}")
+
+            #     # Use the correct template variable
+            #     prompt = phi_custom_template.format(context=context, question=message)
+            #     logging.debug(f"Generated LM-2 prompt: {prompt}")
+
+            #     response = selected_model(prompt, **{
+            #         "max_new_tokens": 250,
+            #         "return_full_text": True,
+            #         "temperature": 0.0,
+            #         "do_sample": False,
+            #     })
+
+            #     if response:
+            #         generated_text = response[0]['generated_text']
+            #         logging.debug(f"LM-2 Response: {generated_text}")
+            #         cleaned_response = clean_response(generated_text)
+            #         return cleaned_response, extract_addresses(cleaned_response)
+            #     else:
+            #         logging.error("LM-2 did not return any response.")
+            #         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:
+        logging.error(f"Error in generate_answer: {str(e)}")
+        logging.error(traceback.format_exc())
+        return "Sorry, I encountered an error while processing your request.", []
 
 def generate_answer(message, choice, retrieval_mode, selected_model, selected_file):
     # Ensure a file is selected