app.py

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

MODEL_NAME = "duclo90/Semeval"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
model.eval()

def classify_text(text):
    if not text.strip():
        return "Please enter some text."

    inputs = tokenizer(
        text,
        return_tensors="pt",
        truncation=True,
        padding=True,
        max_length=512,
    )

    with torch.no_grad():
        logits = model(**inputs).logits

    probs = torch.softmax(logits, dim=-1)
    pred_id = torch.argmax(probs, dim=1).item()

    label = model.config.id2label[pred_id]
    confidence = round(probs[0][pred_id].item(), 3)

    return f"Prediction: {label} (Confidence: {confidence})"

iface = gr.Interface(
    fn=classify_text,
    inputs=gr.Textbox(lines=6),
    outputs="text",
    title="Human vs Machine Text Classifier",
)

iface.launch()