app.py

# app.py
import gradio as gr
from transformers import pipeline

def load_question_generator(num_questions):
    pipe = pipeline("text2text-generation", model="SinaRp/Question_generator_persian", do_sample=True, top_k=50, top_p=0.95, num_return_sequences=num_questions)
    return pipe

def generate_questions(context, num_questions):
    try:
        generator = load_question_generator(num_questions)
        questions = generator(context, max_length=64, num_return_sequences=int(num_questions))
        return "\n\n".join([q['generated_text'] for q in questions])
    except Exception as e:
        return f"Error generating questions: {str(e)}"

iface = gr.Interface(
    fn=generate_questions,
    inputs=[
        gr.Textbox(lines=5, label="Enter your text context"),
        gr.Slider(minimum=1, maximum=5, value=3, step=1, label="Number of questions")
    ],
    outputs=gr.Textbox(label="Generated Questions"),
    title="Question Generator",
    description="Generate questions from your text using AI",
    examples=[
        ["The Sun is the star at the center of the Solar System. It is a nearly perfect sphere of hot plasma, heated to incandescence by nuclear fusion reactions in its core.", 2],
        ["Machine learning is a subset of artificial intelligence that focuses on the use of data and algorithms to imitate the way that humans learn.", 3]
    ]
)

if __name__ == "__main__":
    iface.launch()