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from langchain import HuggingFaceHub, PromptTemplate, LLMChain
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
import gradio as gr
from getpass import getpass
import os
template = """Question: {question}
------------------
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
# Callbacks support token-wise streaming
callbacks = [StreamingStdOutCallbackHandler()]
# Instantiate the Hugging Face model
repo_id = "gpt2" # Replace with the desired model
llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature": 0, "max_length": 64})
# Initialize the chain
llm_chain = LLMChain(prompt=prompt, llm=llm)
# Define the Gradio interface
def chatbot_interface(input_text):
response = llm_chain.run(input_text)
return response
# Define the Gradio app
gradio_app = gr.Interface(
fn=chatbot_interface,
inputs=gr.inputs.Textbox(label="Say something..."),
outputs=gr.outputs.Textbox(),
title="ConversationChain Chatbot",
description="A chatbot interface powered by ConversationChain and Hugging Face.",
)
# Run the Gradio app
if __name__ == "__main__":
gradio_app.launch()
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