Add 7

app.py

@@ -27,7 +27,10 @@ model.eval()
 # =====================
 # CORE PREDICTION
 # =====================
-def predict_offensive(text):
+def predict_offensive(text: str):
+    if not text.strip():
+        return {"error": "Empty text"}
+
     encoded = tokenizer(
         text,
         return_tensors="pt",
@@ -40,7 +43,7 @@ def predict_offensive(text):
 
     with torch.no_grad():
         logits = model(input_ids, attention_mask=attention_mask).logits
-        probs = F.softmax(logits, dim=1)[0]  # Softmax để lấy xác suất
+        probs = F.softmax(logits, dim=1)[0]
 
     pred_idx = torch.argmax(probs).item()
     pred_label = label_mapping[pred_idx]
@@ -69,23 +72,19 @@ class TextItem(BaseModel):
 
 @app.post("/predict")
 def api_predict(item: TextItem):
-    if not item.text:
-        raise HTTPException(status_code=400, detail="Missing text")
     return predict_offensive(item.text)
 
 # =====================
 # GRADIO UI
 # =====================
-def gradio_ui(text):
-    return predict_offensive(text)
-
 ui = gr.Interface(
-    fn=gradio_ui,
+    fn=predict_offensive,
     inputs=gr.Textbox(lines=2, placeholder="Enter a sentence here..."),
-    outputs="json",
+    outputs=gr.JSON(label="Prediction"),
     title="Offensive Language Detector",
     description="Enter a sentence and the model will predict if it is offensive, with confidence scores for all classes."
 )
 
 # Mount Gradio UI on FastAPI
 app = gr.mount_gradio_app(app, ui, path="/")
+