Update app.py

app.py

@@ -100,13 +100,11 @@ def initialize_phi_model():
 def initialize_gpt_model():
     return ChatOpenAI(api_key=os.environ['OPENAI_API_KEY'], temperature=0, model='gpt-4o')
     
-def initialize_gpt_mini_model():
-    return ChatOpenAI(api_key=os.environ['OPENAI_API_KEY'], temperature=0, model='gpt-4o-mini')
+
 
 # Initialize all models
 phi_pipe = initialize_phi_model()
 gpt_model = initialize_gpt_model()
-gpt_mini_model = initialize_gpt_mini_model()
 
 
 
@@ -114,9 +112,8 @@ gpt_mini_model = initialize_gpt_mini_model()
 
 
 
-# Initialize all models
-phi_pipe = initialize_phi_model()
-gpt_model = initialize_gpt_model()
+
+
 
 
 
@@ -351,34 +348,12 @@ Sure! Here's the information you requested:
 """
 
 
-# def generate_bot_response(history, choice, retrieval_mode, model_choice):
-#     if not history:
-#         return
-
-#     # Select the model
-#     selected_model = chat_model if model_choice == "LM-1" else phi_pipe
-
-#     response, addresses = generate_answer(history[-1][0], choice, retrieval_mode, selected_model)
-#     history[-1][1] = ""
-
-#     for character in response:
-#         history[-1][1] += character
-#         yield history  # Stream each character as it is generated
-#         time.sleep(0.05)  # Add a slight delay to simulate streaming
-
-#     yield history  # Final yield with the complete response
-
 def generate_bot_response(history, choice, retrieval_mode, model_choice):
     if not history:
         return
 
-    # Select the model based on user choice
-    if model_choice == "LM-1":
-        selected_model = gpt_model
-    elif model_choice == "LM-2":
-        selected_model = phi_pipe
-    elif model_choice == "LM-3":
-        selected_model = gpt_mini_model
+    # Select the model
+    selected_model = chat_model if model_choice == "LM-1" else phi_pipe
 
     response, addresses = generate_answer(history[-1][0], choice, retrieval_mode, selected_model)
     history[-1][1] = ""
@@ -394,6 +369,8 @@ def generate_bot_response(history, choice, retrieval_mode, model_choice):
 
 
 
+
+
 def generate_tts_response(response, tts_choice):
     with concurrent.futures.ThreadPoolExecutor() as executor:
         if tts_choice == "Alpha":
@@ -492,113 +469,11 @@ 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}, 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
-#         if 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 == 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": 400,
-#                     "return_full_text": True,
-#                     "temperature": 0.7,
-#                     "do_sample": True,
-#                 })
-
-#                 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):
     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 (LM-2)
-    if selected_model == phi_pipe:
+    # Logic for disabling options for Phi-3.5
+    if selected_model == "LM-2":
         choice = None
         retrieval_mode = None
 
@@ -611,11 +486,32 @@ def generate_answer(message, choice, retrieval_mode, selected_model):
         else:
             prompt_template = QA_CHAIN_PROMPT_1  # Fallback to template1
 
-        # VDB retrieval mode
+        # 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 in [gpt_model, gpt_mini_model]:  # Handle both LM-1 and LM-3
-                logging.debug(f"Selected model: {'LM-1' if selected_model == gpt_model else 'LM-3'}")
+            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}")
@@ -624,22 +520,23 @@ def generate_answer(message, choice, retrieval_mode, selected_model):
                 logging.debug(f"Generated prompt: {prompt}")
 
                 qa_chain = RetrievalQA.from_chain_type(
-                    llm=selected_model,
+                    llm=chat_model,
                     chain_type="stuff",
                     retriever=retriever,
                     chain_type_kwargs={"prompt": prompt_template}
                 )
                 response = qa_chain({"query": message})
-                logging.debug(f"LM-1 or LM-3 response: {response}")
+                logging.debug(f"LM-1 response: {response}")
                 return response['result'], extract_addresses(response['result'])
 
-            elif selected_model == phi_pipe:  # LM-2 specific logic
+            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}")
 
@@ -659,13 +556,11 @@ def generate_answer(message, choice, retrieval_mode, selected_model):
                     logging.error("LM-2 did not return any response.")
                     return "No response generated.", []
 
-        # KGF retrieval mode
         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.", []
@@ -679,6 +574,8 @@ def generate_answer(message, choice, retrieval_mode, selected_model):
 
 
 
+
+
 def add_message(history, message):
     history.append((message, None))
     return history, gr.Textbox(value="", interactive=True, show_label=False)
@@ -1152,24 +1049,12 @@ def handle_retrieval_mode_change(choice):
 
 
 
-# def handle_model_choice_change(selected_model):
-#     if selected_model == "LM-2":
-#         # Disable retrieval mode and select style when LM-2 is selected
-#         return gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=False)
-#     elif selected_model == "LM-1":
-#         # Enable retrieval mode and select style for LM-1
-#         return gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True)
-#     else:
-#         # Default case: allow interaction
-#         return gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True)
-
-
 def handle_model_choice_change(selected_model):
     if selected_model == "LM-2":
         # Disable retrieval mode and select style when LM-2 is selected
         return gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=False)
-    elif selected_model in ["LM-1", "LM-3"]:
-        # Enable retrieval mode and select style for LM-1 and LM-3
+    elif selected_model == "LM-1":
+        # Enable retrieval mode and select style for LM-1
         return gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True)
     else:
         # Default case: allow interaction
@@ -1179,6 +1064,9 @@ def handle_model_choice_change(selected_model):
 
 
 
+
+
+
 def format_restaurant_hotel_info(name, link, location, phone, rating, reviews, snippet):
     return f"""
     {name}