app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
import torch

model_name = "Aurelie123/my_qa_model"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForQuestionAnswering.from_pretrained(model_name)

def answer_question(context, question):
    inputs = tokenizer(question, context, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)

    start = torch.argmax(outputs.start_logits)
    end = torch.argmax(outputs.end_logits) + 1

    answer = tokenizer.convert_tokens_to_string(
        tokenizer.convert_ids_to_tokens(inputs["input_ids"][0][start:end])
    )
    return answer

demo = gr.Interface(
    fn=answer_question,
    inputs=[gr.Textbox(label="Context") ,gr.Textbox(label="Question")],
    outputs="text",
    title="My QA Model"
)

demo.launch()