mirror of
https://github.com/coleam00/ai-agents-masterclass.git
synced 2025-11-29 08:33:16 +00:00
98 lines
3.4 KiB
Python
98 lines
3.4 KiB
Python
from dotenv import load_dotenv
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from datetime import datetime
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import streamlit as st
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import asyncio
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import json
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import uuid
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import os
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from langchain_core.messages import SystemMessage, AIMessage, HumanMessage, ToolMessage
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from langserve import RemoteRunnable
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load_dotenv()
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agent_endpoint_url = os.getenv('AGENT_ENDPOINT_URL', 'http://localhost:8000')
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@st.cache_resource
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def create_chatbot_instance():
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return RemoteRunnable(agent_endpoint_url)
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chatbot = create_chatbot_instance()
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@st.cache_resource
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def get_thread_id():
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return str(uuid.uuid4())
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thread_id = get_thread_id()
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system_message = f"""
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You are a personal assistant who helps manage tasks in Asana and documents in Google Drive.
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You never give IDs to the user since those are just for you to keep track of.
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The current date is: {datetime.now().date()}
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"""
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async def prompt_ai(messages):
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config = {
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"configurable": {
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"thread_id": thread_id
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}
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}
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async for event in chatbot.astream_events(
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{"messages": messages}, config, version="v1"
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):
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if event["event"] == "on_chat_model_stream":
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yield event["data"]["chunk"].content
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# ~~~~~~~~~~~~~~~~~~ Main Function with UI Creation ~~~~~~~~~~~~~~~~~~~~
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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async def main():
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st.title("LangServe Chatbot")
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# Initialize chat history
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if "messages" not in st.session_state:
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st.session_state.messages = [
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SystemMessage(content=system_message)
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]
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# Display chat messages from history on app rerun
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for message in st.session_state.messages:
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message_json = json.loads(message.json())
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message_type = message_json["type"]
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if message_type in ["human", "ai", "system"]:
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with st.chat_message(message_type):
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st.markdown(message_json["content"])
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# React to user input
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if prompt := st.chat_input("What would you like to do today?"):
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# Display user message in chat message container
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st.chat_message("user").markdown(prompt)
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# Add user message to chat history
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st.session_state.messages.append(HumanMessage(content=prompt))
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# Display assistant response in chat message container
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response_content = ""
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with st.chat_message("assistant"):
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message_placeholder = st.empty() # Placeholder for updating the message
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# Run the async generator to fetch responses
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async for chunk in prompt_ai(st.session_state.messages):
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if isinstance(chunk, str):
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response_content += chunk
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elif isinstance(chunk, list):
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for chunk_text in chunk:
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if "text" in chunk_text:
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response_content += chunk_text["text"]
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else:
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raise Exception("Chunk is not a string or list.")
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# Update the placeholder with the current response content
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message_placeholder.markdown(response_content)
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st.session_state.messages.append(AIMessage(content=response_content))
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if __name__ == "__main__":
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asyncio.run(main()) |