Update app.py

app.py

@@ -1,7 +1,6 @@
 import streamlit as st
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
 from langchain_core.prompts import PromptTemplate
-from langchain_core.runnables import RunnableSequence
 from langchain_huggingface import HuggingFacePipeline
 from langchain.agents import create_react_agent, AgentExecutor, Tool
 from langchain.memory import ConversationBufferMemory
@@ -54,27 +53,34 @@ tools = [
 ]
 
 # Set up the agent
-prefix = """You are an AI CyberSecurity Program Advisor. Your goal is to engage with leads and get them to book a video call for an in-person sales meeting. You have access to a list of leads and can send emails.
+prompt = PromptTemplate.from_template(
+    """You are an AI CyberSecurity Program Advisor. Your goal is to engage with leads and get them to book a video call for an in-person sales meeting. You have access to a list of leads and can send emails.
 
-You have access to the following tools:"""
+You have access to the following tools:
 
-suffix = """Begin!
+{tools}
 
-{chat_history}
-Human: {human_input}
-AI: Let's approach this step-by-step:"""
+Use the following format:
 
-prompt = PromptTemplate(
-    template=prefix + "{agent_scratchpad}" + suffix,
-    input_variables=["human_input", "chat_history", "agent_scratchpad"]
-)
+Question: the input question you must answer
+Thought: you should always think about what to do
+Action: the action to take, should be one of [{tool_names}]
+Action Input: the input to the action
+Observation: the result of the action
+... (this Thought/Action/Action Input/Observation can repeat N times)
+Thought: I now know the final answer
+Final Answer: the final answer to the original input question
+
+Begin!
 
-llm_chain = RunnableSequence(prompt, local_llm)
-memory = ConversationBufferMemory(memory_key="chat_history")
+Question: {input}
+Thought: Let's approach this step-by-step:
+{agent_scratchpad}"""
+)
 
 agent = create_react_agent(local_llm, tools, prompt)
 agent_executor = AgentExecutor.from_agent_and_tools(
-    agent=agent, tools=tools, verbose=True, memory=memory
+    agent=agent, tools=tools, verbose=True, memory=ConversationBufferMemory()
 )
 
 # Streamlit interface