app.py

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

# Charger le tokenizer depuis Hugging Face Spaces
tokenizer = AutoTokenizer.from_pretrained("Dofla/bert-squad")

# Charger le modèle depuis Hugging Face Spaces
model = AutoModelForQuestionAnswering.from_pretrained("Dofla/bert-squad")
def answer_question(context, question):
    inputs = tokenizer.encode_plus(question, context, return_tensors="pt", padding=True, truncation=True)
    start_logits, end_logits = model(**inputs)
    outputs = model(**inputs)
    start_logits = outputs.start_logits
    end_logits = outputs.end_logits


    # Assurez-vous que les logits sont des tenseurs
    start_index = torch.argmax(start_logits, dim=1).item()
    end_index = torch.argmax(end_logits, dim=1).item() + 1
    answer = tokenizer.decode(inputs["input_ids"][0][start_index:end_index])
    return answer
# Créer une interface Gradio pour l'inférence
iface = gr.Interface(
    fn=answer_question,
    inputs=[
        gr.Textbox(lines=7, label="Contexte"),
        gr.Textbox(lines=1, label="Question")
    ],
    outputs="text",
    title="Question Answering with Fine-Tuned Model"
)

# Lancer l'interface
iface.launch('share=True')