Update app.py

app.py

@@ -391,27 +391,24 @@ chain_neo4j = (
 
 # 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:
+# 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:
+# """
+
+phi_custom_template = """
+<|system|>
+You are a helpful assistant.<|end|>
+<|user|>
+Context: {context}
+Question: {question}<|end|>
+<|assistant|>
 """
 
 
-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:', '', response_text, flags=re.DOTALL).strip()
-    return cleaned_response
     
 
 import re
@@ -495,6 +492,78 @@ def clean_response(response_text):
 
 
 
+# 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.", []
+
+
+
 def generate_answer(message, choice, retrieval_mode, selected_model):
     logging.debug(f"generate_answer called with choice: {choice} and retrieval_mode: {retrieval_mode}")
 
@@ -535,7 +604,8 @@ def generate_answer(message, choice, retrieval_mode, selected_model):
                 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)
+                # Integrating the custom context and question into the base prompt template
+                prompt = phi_base_template.format(context=context, question=message)
 
                 logging.debug(f"Phi-3.5 Prompt: {prompt}")
 
@@ -570,8 +640,6 @@ def generate_answer(message, choice, retrieval_mode, selected_model):
 
 
 
-
-
 def bot(history, choice, tts_choice, retrieval_mode, model_choice):
     if not history:
         return history