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# import gradio as gr
# from transformers import pipeline
# # Load the pre-trained model
# generator = pipeline("question-answering", model="EleutherAI/gpt-neo-2.7B")
# # Define Gradio interface
# def generate_response(prompt):
# # Generate response based on the prompt
# response = generator(prompt, max_length=50, do_sample=True, temperature=0.9)
# return response[0]['generated_text']
# # Create Gradio interface
# iface = gr.Interface(
# fn=generate_response,
# inputs="text",
# outputs="text",
# title="OpenAI Text Generation Model",
# description="Enter a prompt and get a generated text response.",
# )
# # Deploy the Gradio interface
# iface.launch(share=True)
import gradio as gr
from transformers import pipeline
# Load the question answering pipeline
qa_pipeline = pipeline("question-answering", model="distilbert-base-cased-distilled-squad", tokenizer="distilbert-base-cased")
# Define a function to generate answer for the given question
def generate_answer(question):
# Call the question answering pipeline
result = qa_pipeline(question=question, context=None)
return result["answer"]
iface = gr.Interface(
fn=generate_answer,
inputs="text",
outputs="text",
title="Open-Domain Question Answering",
description="Enter your question to get an answer.",
)
iface.launch(share=True) # Deploy the interface
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