Files
ai-agents-masterclass/n8n-streamlit-agent/n8n-streamlit-agent-basic-auth.py

59 lines
1.7 KiB
Python

import streamlit as st
import requests
import uuid
# Constants
WEBHOOK_URL = "YOUR_N8N_WEBHOOK_URL_HERE"
BEARER_TOKEN = "YOUR_BEARER_TOKEN_HERE"
def generate_session_id():
return str(uuid.uuid4())
def send_message_to_llm(session_id, message):
headers = {
"Authorization": f"Bearer {BEARER_TOKEN}",
"Content-Type": "application/json"
}
payload = {
"sessionId": session_id,
"chatInput": message
}
response = requests.post(WEBHOOK_URL, json=payload, headers=headers)
if response.status_code == 200:
return response.json()["output"]
else:
return f"Error: {response.status_code} - {response.text}"
def main():
st.title("Chat with LLM")
# Initialize session state
if "messages" not in st.session_state:
st.session_state.messages = []
if "session_id" not in st.session_state:
st.session_state.session_id = generate_session_id()
# Display chat messages
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.write(message["content"])
# User input
user_input = st.chat_input("Type your message here...")
if user_input:
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": user_input})
with st.chat_message("user"):
st.write(user_input)
# Get LLM response
llm_response = send_message_to_llm(st.session_state.session_id, user_input)
# Add LLM response to chat history
st.session_state.messages.append({"role": "assistant", "content": llm_response})
with st.chat_message("assistant"):
st.write(llm_response)
if __name__ == "__main__":
main()