app.py

import gradio as gr
from langchain_huggingface.llms import HuggingFacePipeline
from langchain_core.prompts import PromptTemplate

# Load the HuggingFace model
hf = HuggingFacePipeline.from_model_id(
    model_id="gpt2",
    task="text-generation",
    pipeline_kwargs={"max_new_tokens": 10},
)

# Create a prompt template
template = """Question: {question}

Answer: Let's think step by step."""
prompt = PromptTemplate.from_template(template)

# Combine prompt with HuggingFace model
chain = prompt | hf

# Define a function for Gradio interface
def respond(question,history):
    return chain.invoke({"question": question})

# Create Gradio ChatInterface
chat_interface = gr.ChatInterface(fn=respond, title="Q&A Chatbot", description="Ask any question and get an answer!")

# Launch the interface
chat_interface.launch()