Update app.py

app.py

@@ -8,8 +8,6 @@ from langchain.memory import ConversationBufferMemory
 # Mock lead data
 LEADS = [
     {"name": "John Doe", "email": "[email protected]", "company": "TechCorp"},
-    {"name": "Jane Smith", "email": "[email protected]", "company": "InnoSoft"},
-    {"name": "Bob Johnson", "email": "[email protected]", "company": "DataTech"},
 ]
 
 # Set up the open-source LLM
@@ -28,12 +26,7 @@ def load_model():
 
 local_llm = load_model()
 
-
 # Define the tools for the agent
-def search_leads(query):
-    results = [lead for lead in LEADS if query.lower() in lead['name'].lower()]
-    return results
-
 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}")
@@ -42,11 +35,6 @@ def send_email(to_email, subject, body):
     return "Email sent successfully"
 
 tools = [
-    Tool(
-        name="Search Leads",
-        func=search_leads,
-        description="Useful for searching leads by name"
-    ),
     Tool(
         name="Send Email",
         func=send_email,
@@ -97,30 +85,17 @@ agent_executor = AgentExecutor.from_agent_and_tools(
 # 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']}, Company: {lead['company']})")
-        
-        initial_message = f"Hello {lead['name']}, I'd like to discuss our cybersecurity program with {lead['company']}. 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.call({"input": initial_message})
-                st.write("AI Response:")
-                st.write(response['output'])  # Access the final output using the appropriate key
-
+st.write("This demo showcases an AI agent that can engage with leads and attempt to book video calls for sales meetings.")
 
+# Start a conversation with a predefined lead
+lead = LEADS[0]
+initial_message = f"Hello {lead['name']}, I'd like to discuss our cybersecurity program with {lead['company']}. Are you available for a quick video call?"
 
+if st.button("Start Conversation"):
+    with st.spinner("AI is generating a response..."):
+        response = agent_executor({"input": initial_message})
+        st.write("AI Response:")
+        st.write(response["output"])  # Adjust this based on the actual output key
 
 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