Update app.py

app.py

@@ -593,7 +593,186 @@ Detailed Answer:
 
 import traceback
 
-def generate_answer(message, choice, retrieval_mode, selected_model):
+# 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.", []
+
+# # def generate_answer(message, choice, retrieval_mode, selected_model, selected_file):
+# #     # Ensure a file is selected
+# #     if not selected_file:
+# #         return "Please choose a file to proceed."
+
+# #     # Modify the Phi-3.5 prompt to include the selected file
+# #     if 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.", []
+
+
+
+def generate_answer(message, choice, retrieval_mode, selected_model, selected_file):
+    # Ensure a file is selected
+    if not selected_file:
+        return "Please choose a file to proceed."
+
     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
@@ -612,27 +791,6 @@ def generate_answer(message, choice, retrieval_mode, selected_model):
         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")
@@ -673,9 +831,7 @@ def generate_answer(message, choice, retrieval_mode, selected_model):
                 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)
@@ -695,36 +851,6 @@ def generate_answer(message, choice, retrieval_mode, selected_model):
                     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")
@@ -740,33 +866,6 @@ def generate_answer(message, choice, retrieval_mode, selected_model):
         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
-#     if not selected_file:
-#         return "Please choose a file to proceed."
-
-#     # Modify the Phi-3.5 prompt to include the selected file
-#     if 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.", []
-
-