app.py

import gradio as gr
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForSequenceClassification

def check_spam(text):
    classifier = pipeline("text-classification")
    model_name = "ericjedha/spammy_overfit" #ModernBert
    model = AutoModelForSequenceClassification.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    classifier = pipeline("text-classification", model = model, tokenizer = tokenizer)
    result = classifier(text)[0]  # Récupère le premier résultat
    return f"Label: {result['label']}, Score: {result['score']:.2f}"  # Formatage du résultat

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            sms = gr.Textbox(label="SMS à checker")
            spam_check_btn = gr.Button("Check")
        with gr.Column():  # Correction de l'indentation
            result = gr.Textbox(label="Résultat")
    
    spam_check_btn.click(check_spam, inputs=sms, outputs=result)  # Correction de `input` -> `inputs`

demo.launch(share=True)