labels update

app.py

@@ -9,22 +9,21 @@ tokenizer = AutoTokenizer.from_pretrained(model_id, return_tensors="pt", use_fas
 model = AutoModelForSequenceClassification.from_pretrained(model_id)
 model.to(device)
 
-label2ids = {
-                "sadness": 0,
-                "joy": 1,
-                "love": 2,
-                "anger": 3,
-                "fear": 4,
-                "surprise": 5,
-            }
-
 def predict(query: str) -> dict:
     inputs = tokenizer(query, return_tensors='pt')
     inputs.to(device)
     outputs = model(**inputs)
     outputs = torch.sigmoid(outputs.logits)
     outputs = outputs.detach().cpu().numpy()
-
+    
+    label2ids = {
+                "sadness": 0,
+                "joy": 1,
+                "love": 2,
+                "anger": 3,
+                "fear": 4,
+                "surprise": 5,
+                }
     for i, k in enumerate(label2ids.keys()):
         label2ids[k] = outputs[0][i]
     label2ids = {k: float(v) for k, v in sorted(label2ids.items(), key=lambda item: item[1], reverse=True)}