app.py

import torch
import gradio as gr
from transformers import T5Tokenizer, T5ForConditionalGeneration

# Set device
device = "mps" if torch.backends.mps.is_available() else "cuda" if torch.cuda.is_available() else "cpu"

# Load model and tokenizer
model_path = "thenHung/question_decomposer_t5"
tokenizer = T5Tokenizer.from_pretrained(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path)
model.to(device)
model.eval()

def decompose_question(question):
    """
    Decompose a complex question into sub-questions

    Args:
        question (str): Input complex question

    Returns:
        list: List of decomposed sub-questions
    """
    try:
        # Prepare input
        input_text = f"decompose question: {question}"
        input_ids = tokenizer(
            input_text,
            max_length=128,
            padding="max_length",
            truncation=True,
            return_tensors="pt"
        ).input_ids.to(device)

        # Generate sub-questions
        with torch.no_grad():
            outputs = model.generate(
                input_ids,
                max_length=128,
                num_beams=4,
                early_stopping=True
            )

        # Decode and split output
        decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
        sub_questions = decoded_output.split(" [SEP] ")

        return sub_questions
    except Exception as e:
        return [f"Error: {str(e)}"]

# Create Gradio interface
demo = gr.Interface(
    fn=decompose_question,
    inputs=gr.Textbox(label="Enter your complex question"),
    outputs=gr.JSON(label="Decomposed Sub-Questions"),
    title="Question Decomposer",
    description="Breaks down complex questions into simpler sub-questions using a T5 model",
    examples=[
        "Who is taller between John and Mary?",
        "What is the capital of Vietnam and the largest city in Vietnam?",
    ]
)

# Launch the app
if __name__ == "__main__":
    demo.launch()