Create app.py

app.py

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+from langchain import PromptTemplate, LLMChain
+import chainlit as cl
+from langchain import HuggingFaceHub
+
+repo_id = "tiiuae/falcon-7b-instruct"
+llm = HuggingFaceHub(
+    huggingfacehub_api_token='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',
+    repo_id=repo_id,
+    model_kwargs={"temperature":0.3, "max_new_tokens":500}
+)
+
+template = """Question: {question}
+
+Answer: Let's think step by step."""
+
+
+@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()])
+
+    # Do any post processing here
+
+    # "res" is a Dict. For this chain, we get the response by reading the "text" key.
+    # This varies from chain to chain, you should check which key to read.
+    await cl.Message(content=res["text"]).send()