Update app.py

app.py

@@ -100,6 +100,13 @@ 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()
 
 
 
@@ -344,12 +351,34 @@ 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
-    selected_model = chat_model if model_choice == "LM-1" else phi_pipe
+    # 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
 
     response, addresses = generate_answer(history[-1][0], choice, retrieval_mode, selected_model)
     history[-1][1] = ""
@@ -365,7 +394,6 @@ 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":
@@ -464,11 +492,113 @@ 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 == "LM-2":
+    if selected_model == phi_pipe:
         choice = None
         retrieval_mode = None
 
@@ -481,32 +611,9 @@ 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")
-            if selected_model == chat_model:
-                logging.debug("Selected model: LM-1")
+            if selected_model in [gpt_model, gpt_mini_model]:
                 retriever = gpt_retriever
                 context = retriever.get_relevant_documents(message)
                 logging.debug(f"Retrieved context: {context}")
@@ -515,26 +622,22 @@ def generate_answer(message, choice, retrieval_mode, selected_model):
                 logging.debug(f"Generated prompt: {prompt}")
 
                 qa_chain = RetrievalQA.from_chain_type(
-                    llm=chat_model,
+                    llm=selected_model,
                     chain_type="stuff",
                     retriever=retriever,
                     chain_type_kwargs={"prompt": prompt_template}
                 )
                 response = qa_chain({"query": message})
-                logging.debug(f"LM-1 response: {response}")
+                logging.debug(f"LM-1 or LM-3 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,
@@ -544,7 +647,6 @@ def generate_answer(message, choice, retrieval_mode, selected_model):
 
                 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:
@@ -552,9 +654,7 @@ def generate_answer(message, choice, retrieval_mode, selected_model):
                     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.")
@@ -1265,14 +1365,7 @@ def insert_prompt(current_text, prompt):
 
 with gr.Blocks(theme='Pijush2023/scikit-learn-pijush') as demo:
 
-    # Custom CSS to change color on hover
-    demo.css = """
-    .gr-examples-list > div:hover {
-        color: red !important;
-        cursor: pointer;
-    }
-    """
-    
+   
     with gr.Row():
         with gr.Column():
             state = gr.State()
@@ -1280,7 +1373,7 @@ 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"], value="LM-1")
+            model_choice = gr.Dropdown(label="Choose Model", choices=["LM-1", "LM-2", "LM-3"], 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])