Update app.py

app.py

@@ -8,14 +8,29 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
 model.eval()
 
-labels = ["happy", "love", "angry", "sadness", "fear", "trust", "disgust", "surprise", "anticipation", "optimism", "pessimism"]
+labels = ["happy", "love", "angry", "sadness", "fear", "trust",
+          "disgust", "surprise", "anticipation", "optimism", "pessimism"]
 
-def predict(text):
+def predict(text, threshold=0.5):
     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(model.device)
     with torch.no_grad():
         outputs = model(**inputs)
         probs = torch.sigmoid(outputs.logits)[0].cpu().numpy()
-    results = {labels[i]: float(np.round(probs[i], 3)) for i in range(len(labels))}
+
+    # เลือกเฉพาะอารมณ์ที่ prob > threshold
+    results = {
+        labels[i]: float(np.round(probs[i], 3))
+        for i in range(len(labels)) if probs[i] > threshold
+    }
+
+    # ถ้าไม่มีอารมณ์ไหนผ่าน threshold
+    if not results:
+        return {"No dominant emotion": 1.0}
+
     return results
 
-gr.Interface(fn=predict, inputs=gr.Textbox(label="Enter english comment"), outputs="label").launch()
+gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(label="Enter english comment"),
+    outputs=gr.Label(label="Detected Emotions")
+).launch()