Update app.py

app.py

@@ -311,260 +311,15 @@ chain_neo4j = (
 
 
 
-
-
-
-# def generate_answer(message, choice, retrieval_mode, selected_model):
-#     logging.debug(f"generate_answer called with choice: {choice} and retrieval_mode: {retrieval_mode}")
-
-#     try:
-#         # Handle hotel-related queries
-#         if "hotel" in message.lower() or "hotels" in message.lower() and "birmingham" in message.lower():
-#             response = fetch_google_hotels()
-#             return response, extract_addresses(response)
-        
-#         # Handle restaurant-related queries
-#         if "restaurant" in message.lower() or "restaurants" in message.lower() and "birmingham" in message.lower():
-#             response = fetch_yelp_restaurants()
-#             return response, extract_addresses(response)
-
-#         # Handle flight-related queries
-#         if "flight" in message.lower() or "flights" in message.lower() and "birmingham" in message.lower():
-#             response = fetch_google_flights()
-#             return response, extract_addresses(response)
-
-#         if retrieval_mode == "VDB":
-#             if selected_model == chat_model:
-#                 # Use GPT-4o with its vector store and template
-#                 retriever = gpt_retriever
-#                 prompt_template = QA_CHAIN_PROMPT_1 if choice == "Details" else QA_CHAIN_PROMPT_2
-#                 context = retriever.get_relevant_documents(message)
-#                 prompt = prompt_template.format(context=context, question=message)
-
-#                 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})
-#                 return response['result'], extract_addresses(response['result'])
-
-#             elif selected_model == phi_pipe:
-#                 # Use Phi-3.5 with its vector store and a simplified prompt
-#                 retriever = phi_retriever
-#                 context = retriever.get_relevant_documents(message)
-#                 prompt = f"""
-#                 Here is the information based on the documents provided:
-#                 {context}
-
-#                 {message}
-#                 """
-
-#                 logging.debug(f"Phi-3.5 Prompt: {prompt}")
-
-#                 response = selected_model(prompt, **{
-#                     "max_new_tokens": 300,
-#                     "return_full_text": False,
-#                     "temperature": 0.5,
-#                     "do_sample": False,
-#                 })
-
-#                 if response:
-#                     generated_text = response[0]['generated_text']
-#                     logging.debug(f"Phi-3.5 Response: {generated_text}")
-#                     return generated_text, extract_addresses(generated_text)
-#                 else:
-#                     logging.error("Phi-3.5 did not return any response.")
-#                     return "No response generated.", []
-
-#         elif retrieval_mode == "KGF":
-#             response = chain_neo4j.invoke({"question": message})
-#             return response, extract_addresses(response)
-#         else:
-#             return "Invalid retrieval mode selected.", []
-
-#     except Exception as e:
-#         logging.error(f"Error in generate_answer: {e}")
-#         return "Sorry, I encountered an error while processing your request.", []
-
-
-# Short Prompt Template for Phi-3.5 Proprietary Model
-
-# phi_short_template = f"""
-# As an expert on Birmingham, Alabama, I will provide concise, accurate, and informative responses to your queries based on 128 token limit . Given the sunny weather today, {current_date}, feel free to ask me anything you need to know about the city.
-# Provide only the direct answer to the question without any follow-up questions.
-# {{context}}
-# Question: {{question}}
-# Answer:
+# # Define the custom template for Phi-3.5
+# phi_custom_template = """
+# <|system|>
+# You are a helpful assistant.<|end|>
+# <|user|>
+# {context}
+# {question}<|end|>
+# <|assistant|>
 # """
-
-
-
-# def generate_answer(message, choice, retrieval_mode, selected_model):
-#     logging.debug(f"generate_answer called with choice: {choice} and retrieval_mode: {retrieval_mode}")
-
-#     try:
-#         if "hotel" in message.lower() or "hotels" in message.lower() and "birmingham" in message.lower():
-#             response = fetch_google_hotels()
-#             return response, extract_addresses(response)
-        
-#         if "restaurant" in message.lower() or "restaurants" in message.lower() and "birmingham" in message.lower():
-#             response = fetch_yelp_restaurants()
-#             return response, extract_addresses(response)
-
-#         if "flight" in message.lower() or "flights" in message.lower() and "birmingham" in message.lower():
-#             response = fetch_google_flights()
-#             return response, extract_addresses(response)
-
-#         if retrieval_mode == "VDB":
-#             if selected_model == chat_model:
-#                 retriever = gpt_retriever
-#                 prompt_template = QA_CHAIN_PROMPT_1 if choice == "Details" else QA_CHAIN_PROMPT_2
-#                 context = retriever.get_relevant_documents(message)
-#                 prompt = prompt_template.format(context=context, question=message)
-
-#                 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})
-#                 return response['result'], extract_addresses(response['result'])
-
-#             elif selected_model == phi_pipe:
-#                 retriever = phi_retriever
-#                 context = retriever.get_relevant_documents(message)
-#                 prompt = phi_short_template.format(context=context, question=message)
-
-#                 logging.debug(f"Phi-3.5 Prompt: {prompt}")
-
-#                 response = selected_model(prompt, **{
-#                     "max_new_tokens": 160,  # Increased to handle longer responses
-#                     "return_full_text": True,
-#                     "temperature": 0.7,  # Adjusted to avoid cutting off
-#                     "do_sample": True,   # Allow sampling to increase response diversity
-#                 })
-
-#                 if response:
-#                     generated_text = response[0]['generated_text']
-#                     logging.debug(f"Phi-3.5 Response: {generated_text}")
-#                     cleaned_response = clean_response(generated_text)
-#                     return cleaned_response, extract_addresses(cleaned_response)
-#                 else:
-#                     logging.error("Phi-3.5 did not return any response.")
-#                     return "No response generated.", []
-
-#         elif retrieval_mode == "KGF":
-#             response = chain_neo4j.invoke({"question": message})
-#             return response, extract_addresses(response)
-#         else:
-#             return "Invalid retrieval mode selected.", []
-
-#     except Exception as e:
-#         logging.error(f"Error in generate_answer: {e}")
-#         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} and retrieval_mode: {retrieval_mode}")
-
-#     try:
-#         # Handle hotel-related queries
-#         if "hotel" in message.lower() or "hotels" in message.lower() and "birmingham" in message.lower():
-#             response = fetch_google_hotels()
-#             return response, extract_addresses(response)
-        
-#         # Handle restaurant-related queries
-#         if "restaurant" in message.lower() or "restaurants" in message.lower() and "birmingham" in message.lower():
-#             response = fetch_yelp_restaurants()
-#             return response, extract_addresses(response)
-
-#         # Handle flight-related queries
-#         if "flight" in message.lower() or "flights" in message.lower() and "birmingham" in message.lower():
-#             response = fetch_google_flights()
-#             return response, extract_addresses(response)
-
-#         if retrieval_mode == "VDB":
-#             if selected_model == chat_model:
-#                 retriever = gpt_retriever
-#                 prompt_template = QA_CHAIN_PROMPT_1 if choice == "Details" else QA_CHAIN_PROMPT_2
-#                 context = retriever.get_relevant_documents(message)
-#                 prompt = prompt_template.format(context=context, question=message)
-
-#                 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})
-#                 return response['result'], extract_addresses(response['result'])
-
-#             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_short_template.format(context=context, question=message)
-
-#                 logging.debug(f"Phi-3.5 Prompt: {prompt}")
-
-#                 response = selected_model(prompt, **{
-#                     "max_new_tokens": 160,  # Increased to handle longer responses
-#                     "return_full_text": True,
-#                     "temperature": 0.7,  # Adjusted to avoid cutting off
-#                     "do_sample": True,   # Allow sampling to increase response diversity
-#                 })
-
-#                 if response:
-#                     generated_text = response[0]['generated_text']
-#                     logging.debug(f"Phi-3.5 Response: {generated_text}")
-#                     cleaned_response = clean_response(generated_text)
-#                     return cleaned_response, extract_addresses(cleaned_response)
-#                 else:
-#                     logging.error("Phi-3.5 did not return any response.")
-#                     return "No response generated.", []
-
-#         elif retrieval_mode == "KGF":
-#             response = chain_neo4j.invoke({"question": message})
-#             return response, extract_addresses(response)
-#         else:
-#             return "Invalid retrieval mode selected.", []
-
-#     except Exception as e:
-#         logging.error(f"Error in generate_answer: {e}")
-#         return "Sorry, I encountered an error while processing your request.", []
-
-
-
-# import re
-
-# def clean_response(response_text):
-#     # Remove any metadata-like information and focus on the main content
-#     # Removes "Document(metadata=...)" and other similar patterns
-#     cleaned_response = re.sub(r'Document\(metadata=.*?\),?\s*', '', response_text, flags=re.DOTALL)
-#     cleaned_response = re.sub(r'page_content=".*?"\),?', '', cleaned_response, flags=re.DOTALL)
-#     cleaned_response = re.sub(r'\[.*?\]', '', cleaned_response, flags=re.DOTALL)  # Remove content in brackets
-#     cleaned_response = re.sub(r'\s+', ' ', cleaned_response).strip()
-#     # Remove any unwanted follow-up questions or unnecessary text
-#     cleaned_response = re.sub(r'Question:.*\nAnswer:', '', cleaned_response, flags=re.DOTALL).strip()
-#     return cleaned_response
-
-
-
-
-# Define the custom template for Phi-3.5
-phi_custom_template = """
-<|system|>
-You are a helpful assistant.<|end|>
-<|user|>
-{context}
-{question}<|end|>
-<|assistant|>
-"""
 # import traceback
 
 # def generate_answer(message, choice, retrieval_mode, selected_model):
@@ -661,60 +416,44 @@ You are a helpful assistant.<|end|>
 
 
 
-# def bot(history, choice, tts_choice, retrieval_mode, model_choice):
-#     if not history:
-#         return history
+def bot(history, choice, tts_choice, retrieval_mode, model_choice):
+    if not history:
+        return history
     
-#     # Select the model
-#     selected_model = chat_model if model_choice == "GPT-4o" else phi_pipe
+    # Select the model
+    selected_model = chat_model if model_choice == "GPT-4o" else phi_pipe
     
-#     response, addresses = generate_answer(history[-1][0], choice, retrieval_mode, selected_model)
-#     history[-1][1] = ""
+    response, addresses = generate_answer(history[-1][0], choice, retrieval_mode, selected_model)
+    history[-1][1] = ""
 
-#     with concurrent.futures.ThreadPoolExecutor() as executor:
-#         if tts_choice == "Alpha":
-#             audio_future = executor.submit(generate_audio_elevenlabs, response)
-#         elif tts_choice == "Beta":
-#             audio_future = executor.submit(generate_audio_parler_tts, response)
-#         # elif tts_choice == "Gamma":
-#         #     audio_future = executor.submit(generate_audio_mars5, response)
+    with concurrent.futures.ThreadPoolExecutor() as executor:
+        if tts_choice == "Alpha":
+            audio_future = executor.submit(generate_audio_elevenlabs, response)
+        elif tts_choice == "Beta":
+            audio_future = executor.submit(generate_audio_parler_tts, response)
+        # elif tts_choice == "Gamma":
+        #     audio_future = executor.submit(generate_audio_mars5, response)
 
-#         for character in response:
-#             history[-1][1] += character
-#             time.sleep(0.05)
-#             yield history, None
+        for character in response:
+            history[-1][1] += character
+            time.sleep(0.05)
+            yield history, None
 
-#         audio_path = audio_future.result()
-#         yield history, audio_path
+        audio_path = audio_future.result()
+        yield history, audio_path
 
-#     history.append([response, None])
+    history.append([response, None])
 
 
-# phi_custom_template = """
-# <|system|>
-# You are a helpful assistant who provides clear, organized, and conversational responses.<|end|>
-# <|user|>
-# {context}
-# Question: {question}<|end|>
-# <|assistant|>
-# Sure! Here's the information you requested:
-# """
-import re
-
-def clean_response(response_text):
-    # Remove system and user tags
-    response_text = re.sub(r'<\|system\|>.*?<\|end\|>', '', response_text, flags=re.DOTALL)
-    response_text = re.sub(r'<\|user\|>.*?<\|end\|>', '', response_text, flags=re.DOTALL)
-    response_text = re.sub(r'<\|assistant\|>', '', response_text, flags=re.DOTALL)
-    
-    # Clean up the text by removing extra whitespace
-    cleaned_response = response_text.strip()
-    cleaned_response = re.sub(r'\s+', ' ', cleaned_response)
-    
-    # Ensure the response is conversational and organized
-    cleaned_response = cleaned_response.replace('1.', '\n1.').replace('2.', '\n2.').replace('3.', '\n3.').replace('4.', '\n4.').replace('5.', '\n5.')
-    
-    return cleaned_response
+phi_custom_template = """
+<|system|>
+You are a helpful assistant who provides clear, organized, and conversational responses.<|end|>
+<|user|>
+{context}
+Question: {question}<|end|>
+<|assistant|>
+Sure! Here's the information you requested:
+"""
 
 
 import traceback