Update app.py

app.py

@@ -593,186 +593,7 @@ 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']) 
-#             #-----------------------------------------------------------------------------------------------------------------
-
-#             # 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."
-
+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
@@ -791,6 +612,27 @@ def generate_answer(message, choice, retrieval_mode, selected_model, selected_fi
         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")
@@ -833,23 +675,30 @@ def generate_answer(message, choice, retrieval_mode, selected_model, selected_fi
                 return response['result'], extract_addresses(response['result']) 
 
             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])
-        
-                prompt = phi_custom_template.format(context=context, question=message, document_name=selected_file)
+                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
+                    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":
@@ -869,8 +718,6 @@ def generate_answer(message, choice, retrieval_mode, selected_model, selected_fi
 
 
 
-
-
 def add_message(history, message):
     history.append((message, None))
     return history, gr.Textbox(value="", interactive=True, show_label=False)
@@ -1635,7 +1482,7 @@ files_list = [
 ]
 
 
-file_dropdown = gr.Dropdown(label="Select Document", choices=files_list, value=None)
+
 
 
 with gr.Blocks(theme='gradio/soft') as demo:
@@ -1646,7 +1493,7 @@ with gr.Blocks(theme='gradio/soft') as demo:
             state = gr.State()
 
             # File dropdown
-            file_dropdown = gr.Dropdown(label="Select Document", choices=files_list, value=None)
+            # file_dropdown = gr.Dropdown(label="Select Document", choices=files_list, value=None)
 
             chatbot = gr.Chatbot([], elem_id="RADAR:Channel 94.1", bubble_full_width=False)
             choice = gr.Radio(label="Select Style", choices=["Details", "Conversational"], value="Conversational",interactive=False,visible=False)