Update app.py

app.py

@@ -30,7 +30,8 @@ local_llm = load_model()
 
 # Define the tools for the agent
 def search_leads(query):
-    return [lead for lead in LEADS if query.lower() in lead['name'].lower()]
+    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
@@ -52,7 +53,7 @@ tools = [
     )
 ]
 
-# Set up the agent
+# Set up the agent with a specified output key
 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.
 
@@ -69,8 +70,7 @@ 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: **output**={final_answer}
-
+Final Answer: {final_answer}  # Ensure this is defined as the output key
 Begin!
 
 Question: {input}
@@ -78,9 +78,19 @@ Thought: Let's approach this step-by-step:
 {agent_scratchpad}"""
 )
 
-agent = create_react_agent(local_llm, tools, prompt)
+agent = create_react_agent(
+    local_llm, 
+    tools, 
+    prompt,
+    output_key="final_answer"  # Specify the output key here
+)
+
 agent_executor = AgentExecutor.from_agent_and_tools(
-    agent=agent, tools=tools, verbose=True, memory=ConversationBufferMemory()
+    agent=agent, 
+    tools=tools, 
+    verbose=True, 
+    memory=ConversationBufferMemory(),
+    output_key="final_answer"  # Ensure this matches the agent's output key
 )
 
 # Streamlit interface