Update app.py

app.py

@@ -1,6 +1,3 @@
-import os
-import requests
-import chainlit as cl
 from langchain import hub
 from langchain.agents import AgentExecutor, load_tools
 from langchain.agents.format_scratchpad import format_log_to_str
@@ -8,14 +5,9 @@ from langchain.agents.output_parsers import ReActJsonSingleInputOutputParser
 from langchain.tools.render import render_text_description
 from langchain_community.llms import HuggingFaceHub
 from langchain_community.utilities import SerpAPIWrapper
+import chainlit as cl
 
-# Define the API endpoint URL
-API_URL = "https://api-inference.huggingface.co/models/mistralai/Mixtral-8x7B-Instruct-v0.1"
-
-# Replace this with your actual Hugging Face API token
-HUGGINGFACE_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
-
-# Load the LLM (Hugging Face Hub)
+# Instantiate the LLM
 llm = HuggingFaceHub(
     repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
     task="text-generation",
@@ -38,7 +30,7 @@ prompt = prompt.partial(
 )
 
 # Define the agent
-chat_model_with_stop = llm.bind(stop=["\nObservation"])
+chat_model_with_stop = ChatHuggingFace(llm=llm).bind(stop=["\nObservation"])
 agent = (
     {
         "input": lambda x: x["input"],
@@ -50,22 +42,15 @@ agent = (
 )
 
 # Instantiate AgentExecutor
-agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True, handle_parsing_errors=True)
-
-@cl.on_chat_start
-async def main():
-    cl.user_session.set("agent_executor", agent_executor)
-    return "API-based Mixtral-Instruct"
-
-@cl.on_message
-async def run(message: cl.Message):
-    # Retrieve the AgentExecutor from the user session
-    agent_executor = cl.user_session.get("agent_executor")
-
-    question = message.content
-    result = agent_executor.invoke({"input": question})
-
-    if result["output"]:
-        await cl.Message(content=result["output"]).send()
-    else:
-        await cl.Message(content="I'm sorry, I couldn't find an answer.").send()
+agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+
+# Create the Chainlit app
+@cl.chainlit_app
+def app():
+    question = cl.get_text_input("Enter your question")
+    if question:
+        response = agent_executor.invoke({"input": question})
+        cl.display_output(response["output"])
+
+# Run the app
+app()