app.py

import gradio as gr


def interpret_pred(pred):
    low_bond = -6.748472
    high_bound = 6.7176056
    result = "IA" if pred.argmax(dim=-1).item() == 1 else "Humain"
    pred_value = pred[0][1].item()
    interpreted_pred = (pred_value - low_bond) / (high_bound - low_bond)
    is_ai_percent = round(100 * interpreted_pred)
    return result, is_ai_percent


def main(text_sentence):
    import torch
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    from transformers import Trainer, TrainingArguments, EarlyStoppingCallback

    barthez_tokenizer = AutoTokenizer.from_pretrained("moussaKam/barthez")
    model = AutoModelForSequenceClassification.from_pretrained("Anvil-ML/detecteur-ia")

    input_ids = torch.tensor(
        [barthez_tokenizer.encode(text_sentence, add_special_tokens=True)]
    )

    predict = model.forward(input_ids)[0]

    result = (
        "Résultat : {}.\nCe texte a {}% de chances d'avoir été généré par de l'IA"
        .format(interpret_pred(predict)[0], interpret_pred(predict)[1])
    )

    return result


iface = gr.Interface(fn=main, inputs="text", outputs="text")
iface.launch()