Create app.py

app.py

@@ -0,0 +1,59 @@
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import gradio as gr
+
+MODEL_NAME = "engsoftexperimental/modelos/modelo_final_distilbert2"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+
+model.config.id2label = {0: "hate_speech", 1: "offensive", 2: "neither", 3: "spam"}
+model.config.label2id = {"hate_speech": 0, "offensive": 1, "neither": 2, "spam": 3}
+
+def classify_text(texto):
+    inputs = tokenizer(
+        texto,
+        return_tensors="pt",
+        truncation=True,
+        padding="max_length",
+        max_length=128
+    ).to(model.device)
+
+    with torch.no_grad():
+        logits = model(**inputs).logits
+        probs = torch.softmax(logits, dim=1)[0].cpu().numpy()
+        pred = int(torch.argmax(logits, dim=1))
+
+    return {
+        "classe_prevista": model.config.id2label[pred],
+        "prob_hate_speech": float(probs[0]),
+        "prob_offensive": float(probs[1]),
+        "prob_neither": float(probs[2])
+        "spam": float(probs[3])
+    }
+
+def gradio_predict(text):
+    result = classify_text(text)
+    return (
+        result["classe_prevista"],
+        f"{result['prob_hate_speech']:.4f}",
+        f"{result['prob_offensive']:.4f}",
+        f"{result['prob_neither']:.4f}",
+        f"{result['prob_spam']:.4f}",
+    )
+
+demo = gr.Interface(
+    fn=gradio_predict,
+    inputs=gr.Textbox(lines=5, label="Digite ou cole um texto"),
+    outputs=[
+        gr.Label(label="Classificação Prevista"),
+        gr.Textbox(label="Probabilidade (Hate Speech)"),
+        gr.Textbox(label="Probabilidade (Offensive)"),
+        gr.Textbox(label="Probabilidade (Neither)"),
+        gr.Textbox(label="Probabilidade (Spam)")
+    ],
+    title="Protótipo de Moderador de Conteúdo (HateBR + BERT)",
+    description="Ferramenta experimental utilizada na pesquisa para classificação de conteúdo em três classes: Hate Speech, Offensive, Neither e Spam."
+)
+
+demo.launch()