Update app.py

app.py

@@ -6,9 +6,11 @@ from model import EmotionClassifier
 MODEL_NAME = "microsoft/deberta-v3-base"
 
 tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+# Build model with correct structure
 model = EmotionClassifier(MODEL_NAME)
 
-# important! map_location=cpu
+# Load checkpoint — MUST MATCH KEYS NOW
 model.load_state_dict(torch.load("best_deberta_base.bin", map_location="cpu"))
 model.eval()
 
@@ -19,23 +21,24 @@ def predict(text):
         text,
         padding=True,
         truncation=True,
-        return_tensors="pt",
-        max_length=128
+        max_length=128,
+        return_tensors="pt"
     )
+    
     with torch.no_grad():
         logits = model(
             encoded["input_ids"],
             encoded["attention_mask"]
         )
-        probs = torch.sigmoid(logits).cpu().numpy()[0]
-
-    return {emotion: float(prob) for emotion, prob in zip(EMOTIONS, probs)}
+        probs = torch.sigmoid(logits).numpy()[0]
+    
+    return {emo: float(p) for emo, p in zip(EMOTIONS, probs)}
 
 demo = gr.Interface(
     fn=predict,
     inputs="text",
     outputs="label",
-    title="Emotion Classifier – DeBERTa-v3-base"
+    title="Emotion Classifier — DeBERTa"
 )
 
 demo.launch()