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import spaces
import gradio as gr
from langchain_huggingface.llms import HuggingFacePipeline
from langchain.prompts import PromptTemplate
# Initialize the model pipeline
llm = HuggingFacePipeline.from_model_id(
model_id="ibm-granite/granite-3.2-2b-instruct",
task="text-generation",
pipeline_kwargs={
"max_new_tokens": 512,
},
)
prompt = PromptTemplate(
input_variables=["question"],
template="Answer the following question: {question}",
)
# Create a chain with the model pipeline and prompt template
chain = prompt | llm
@spaces.GPU
def ask_question(question: str) -> str:
# Invoke your chain and return the generated answer.
response = chain.invoke({"question": question})
return response
# Create a Gradio interface
iface = gr.Interface(
fn=ask_question,
inputs="text",
outputs="text",
title="Question Answering Bot",
description="Ask any question and get an answer from the model."
)
# Launch the Gradio interface
if __name__ == "__main__":
iface.launch() |