Create main.py

main.py

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+import os
+import torch
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+from langchain import PromptTemplate, LLMChain
+from langchain.llms import HuggingFacePipeline
+from langchain.agents import AgentExecutor, Tool, ZeroShotAgent
+from langchain.memory import ConversationBufferMemory
+import pandas as pd
+from sqlalchemy import create_engine
+import smtplib
+from email.mime.text import MIMEText
+from email.mime.multipart import MIMEMultipart
+
+# Set up the open-source LLM
+@st.cache_resource
+def load_model():
+    model_name = "google/flan-t5-large"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    pipe = pipeline(
+        "text2text-generation",
+        model=model, 
+        tokenizer=tokenizer, 
+        max_length=512
+    )
+    return HuggingFacePipeline(pipeline=pipe)
+
+local_llm = load_model()
+
+# Set up the database connection
+db_connection_string = "sqlite:///leads.db"  # Replace with your actual database connection string
+engine = create_engine(db_connection_string)
+
+# Define the tools for the agent
+def search_leads(query):
+    df = pd.read_sql(f"SELECT * FROM leads WHERE name LIKE '%{query}%'", engine)
+    return df.to_dict(orient='records')
+
+def send_email(to_email, subject, body):
+    # For demo purposes, we'll just print the email details
+    st.write(f"Email sent to: {to_email}")
+    st.write(f"Subject: {subject}")
+    st.write(f"Body: {body}")
+    return "Email sent successfully"
+
+tools = [
+    Tool(
+        name="Search Leads",
+        func=search_leads,
+        description="Useful for searching leads in the database"
+    ),
+    Tool(
+        name="Send Email",
+        func=send_email,
+        description="Useful for sending emails to leads"
+    )
+]
+
+# 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 database of leads and can send emails.
+
+You have access to the following tools:"""
+
+suffix = """Begin!
+
+{chat_history}
+Human: {human_input}
+AI: Let's approach this step-by-step:"""
+
+prompt = ZeroShotAgent.create_prompt(
+    tools,
+    prefix=prefix,
+    suffix=suffix,
+    input_variables=["human_input", "chat_history"]
+)
+
+llm_chain = LLMChain(llm=local_llm, prompt=prompt)
+memory = ConversationBufferMemory(memory_key="chat_history")
+
+agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
+agent_executor = AgentExecutor.from_agent_and_tools(
+    agent=agent, tools=tools, verbose=True, memory=memory
+)
+
+# Streamlit interface
+st.title("AI CyberSecurity Program Advisor Demo")
+
+st.write("This demo showcases an AI agent that can engage with leads and attempt to book video calls for in-person sales meetings.")
+
+lead_name = st.text_input("Enter a lead's name to engage with:")
+
+if lead_name:
+    lead_info = search_leads(lead_name)
+    if not lead_info:
+        st.write(f"No lead found with the name {lead_name}")
+    else:
+        lead = lead_info[0]
+        st.write(f"Lead found: {lead['name']} (Email: {lead['email']})")
+        
+        initial_message = f"Hello {lead['name']}, I'd like to discuss our cybersecurity program with you. Are you available for a quick video call?"
+        
+        if st.button("Engage with Lead"):
+            with st.spinner("AI is generating a response..."):
+                response = agent_executor.run(initial_message)
+            
+            st.write("AI Response:")
+            st.write(response)
+
+st.sidebar.title("About")
+st.sidebar.info("This is a demo of an AI CyberSecurity Program Advisor using an open-source LLM and LangChain. It's designed to engage with leads and attempt to book video calls for sales meetings.")
+
+# To run this script, use: streamlit run your_script_name.py