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app.py

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+from langchain_community.llms import HuggingFaceEndpoint
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+import chainlit as cl
+import os
+repo_id = "tiiuae/falcon-7b-instruct"
+
+from config import HUGGINGFACEHUB_API_TOKEN
+
+# Set the token as an environment variable
+os.environ["HUGGINGFACEHUB_API_TOKEN"] = HUGGINGFACEHUB_API_TOKEN
+
+llm = HuggingFaceEndpoint(
+    repo_id=repo_id, max_length=128, temperature=0.5, token=HUGGINGFACEHUB_API_TOKEN
+)
+
+
+template = """ You're a general chatbot that can answer any thing related to enterprises.{question}
+"""
+
+
+@cl.on_chat_start
+def main():
+    # Instantiate the chain for that user session
+    prompt = PromptTemplate(template=template, input_variables=["question"])
+    llm_chain = LLMChain(prompt=prompt, llm=llm, verbose=True)
+
+    # Store the chain in the user session
+    cl.user_session.set("llm_chain", llm_chain)
+
+
+@cl.on_message
+async def main(message: cl.Message):
+    # Retrieve the chain from the user session
+    llm_chain = cl.user_session.get("llm_chain")  # type: LLMChain
+
+    # Call the chain asynchronously
+    res = await llm_chain.acall(message.content, callbacks=[cl.AsyncLangchainCallbackHandler()])
+    await cl.Message(content=res["text"]).send()

requirements.txt

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+chainlit
+langchain