Update app.py

app.py

@@ -33,7 +33,7 @@ from langchain.chains.conversation.memory import ConversationBufferWindowMemory
 from langchain.agents import Tool, initialize_agent
 
 # Build prompt
-template1 = """You are an expert concierge who is helpful and a renowned guide for Omaha, Nebraska.Based on todays weather is sunny bright day and current date as {current_date} , Use the following pieces of context, 
+template1 = """You are an expert concierge who is helpful and a renowned guide for Omaha, Nebraska.Based on todays weather is sunny bright day and date is 17th june 2024 , Use the following pieces of context, 
 memory, and message history, along with your knowledge of perennial events in Omaha, Nebraska, to answer the question at the end.If you don't know the answer, just say "Homie, I need to get more data for this," and don't try to make up an answer. 
 Use fifteen sentences maximum. Keep the answer as detailed as possible. Always include the address, time, date, and
 event type and description. Always say "It was my pleasure!" at the end of the answer.
@@ -41,7 +41,7 @@ event type and description. Always say "It was my pleasure!" at the end of the a
 Question: {question}
 Helpful Answer:"""
 
-template2 = """You are an expert concierge who is helpful and a renowned guide for Omaha, Nebraska.Based on todays weather is sunny bright day and current date as {current_date} , Use the following pieces of context, 
+template2 = """You are an expert concierge who is helpful and a renowned guide for Omaha, Nebraska.Based on todays weather is sunny bright day and date is 17th june 2024 , Use the following pieces of context, 
 memory, and message history, along with your knowledge of perennial events in Omaha, Nebraska, to answer the question at the end.If you don't know the answer, just say "Homie, I need to get more data for this," and don't try to make up an answer. 
 Use fifteen sentences maximum. Keep the answer short and sweet crisp.Always say "It was my pleasure!" at the end of the answer.
 {context}
@@ -94,8 +94,6 @@ def initialize_agent_with_prompt(prompt_template):
 # Define the function to generate answers
 def generate_answer(message, choice):
     logging.debug(f"generate_answer called with prompt_choice: {choice}")
-    current_date = "17th June 2024"
-    today_weather = get_current_weather()
     
     if choice == "Details":
         agent = initialize_agent_with_prompt(QA_CHAIN_PROMPT_1)
@@ -104,8 +102,7 @@ def generate_answer(message, choice):
     else:
         logging.error(f"Invalid prompt_choice: {choice}. Defaulting to 'Conversational'")
         agent = initialize_agent_with_prompt(QA_CHAIN_PROMPT_2)
-    response = agent(message, today_weather=today_weather, current_date=current_date)
-    #response = agent(message)
+    response = agent(message)
     return response['output']
 
 def bot(history, choice):