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 AutoModelForCausalLM, AutoTokenizer

model_name = "gpt2"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)


def generate_answer(question):
    inputs = tokenizer.encode("Question: " + question, return_tensors="pt")
    outputs = model.generate(inputs, max_length=100, num_return_sequences=1, early_stopping=True)
    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return answer

iface = gr.Interface(
    fn=generate_answer,
    inputs="text",
    outputs="text",
    title="Open-Domain Question Answering",
    description="Enter your question to get an answer.",
    theme="compact"
)

iface.launch(share=True)  # Deploy the interface