app.py

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

MODEL_NAME = "GroNLP/hateBERT"  # troque para seu modelo fine-tunado se quiser

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

def predict(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.softmax(outputs.logits, dim=1).tolist()[0]

    return {
        "Not Hate": float(probs[0]),
        "Hate Speech": float(probs[1])
    }

demo = gr.Interface(
    fn=predict,
    inputs=gr.Textbox(lines=3, placeholder="Digite o texto aqui..."),
    outputs=gr.Label(num_top_classes=2),
    title="HateBERT Classifier",
)

demo.launch()