Update app.py

app.py

@@ -101,8 +101,6 @@ 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')
 
 
 
@@ -112,7 +110,7 @@ def initialize_gpt_mini_model():
 # Initialize all models
 phi_pipe = initialize_phi_model()
 gpt_model = initialize_gpt_model()
-gpt_mini_model = initialize_gpt_mini_model()
+
 
 
 # Existing embeddings and vector store for GPT-4o
@@ -125,10 +123,7 @@ phi_embeddings = OpenAIEmbeddings(api_key=os.environ['OPENAI_API_KEY'])
 phi_vectorstore = PineconeVectorStore(index_name="phivector08252024", embedding=phi_embeddings)
 phi_retriever = phi_vectorstore.as_retriever(search_kwargs={'k': 5})
 
-#Existing embeddings and vector store for GPT-4o-mini
-gpt_mini_embeddings = OpenAIEmbeddings(api_key=os.environ['OPENAI_API_KEY'])
-gpt_mini_vectorstore = PineconeVectorStore(index_name="radarfinaldata08192024", embedding=gpt_mini_embeddings)
-gpt_mini_retriever = gpt_mini_vectorstore.as_retriever(search_kwargs={'k': 5})
+
 
 
 
@@ -349,36 +344,12 @@ 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 the user's choice
-    if model_choice == "LM-1":
-        selected_model = chat_model
-    elif model_choice == "LM-2":
-        selected_model = phi_pipe
-    elif model_choice == "LM-3":
-        selected_model = gpt_mini_model
-    else:
-        selected_model = chat_model  # Fallback to GPT-4o
+    # 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] = ""
@@ -393,6 +364,8 @@ 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":
@@ -491,112 +464,11 @@ 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
-    if selected_model == phi_pipe:
+    if selected_model == "LM-2":
         choice = None
         retrieval_mode = None
 
@@ -609,19 +481,21 @@ def generate_answer(message, choice, retrieval_mode, selected_model):
         else:
             prompt_template = QA_CHAIN_PROMPT_1  # Fallback to template1
 
-        # Handle hotel, restaurant, and flight-related queries as before
+        # 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()
@@ -631,22 +505,51 @@ def generate_answer(message, choice, retrieval_mode, selected_model):
         # Retrieval-based response
         if retrieval_mode == "VDB":
             logging.debug("Using VDB retrieval mode")
-            context = retriever.get_relevant_documents(message)
-            logging.debug(f"Retrieved context: {context}")
-
-            prompt_template = QA_CHAIN_PROMPT_1 if choice == "Details" else QA_CHAIN_PROMPT_2
-            prompt = prompt_template.format(context=context, question=message)
-            logging.debug(f"Generated prompt: {prompt}")
-
-            qa_chain = RetrievalQA.from_chain_type(
-                llm=selected_model,
-                chain_type="stuff",
-                retriever=retriever,
-                chain_type_kwargs={"prompt": prompt_template}
-            )
-            response = qa_chain({"query": message})
-            logging.debug(f"Response: {response}")
-            return response['result'], extract_addresses(response['result'])
+            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")
@@ -664,6 +567,7 @@ 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)
@@ -1376,7 +1280,7 @@ with gr.Blocks(theme='Pijush2023/scikit-learn-pijush') as demo:
             chatbot = gr.Chatbot([], elem_id="RADAR:Channel 94.1", bubble_full_width=False)
             choice = gr.Radio(label="Select Style", choices=["Details", "Conversational"], value="Conversational")
             retrieval_mode = gr.Radio(label="Retrieval Mode", choices=["VDB", "KGF"], value="VDB")
-            model_choice = gr.Dropdown(label="Choose Model", choices=["LM-1", "LM-2", "LM-3"], value="LM-1")
+            model_choice = gr.Dropdown(label="Choose Model", choices=["LM-1", "LM-2"], value="LM-1")
 
             # Link the dropdown change to handle_model_choice_change
             model_choice.change(fn=handle_model_choice_change, inputs=model_choice, outputs=[retrieval_mode, choice, choice])