app.py

# 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