Update services/sentiment.py

services/sentiment.py

@@ -1,20 +1,25 @@
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
-from config import MODEL_NAME
+from transformers import pipeline
 
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
-
-labels = ["Negative", "Neutral", "Positive"]
+# 🔥 lebih ringan daripada load manual
+classifier = pipeline(
+    "sentiment-analysis",
+    model="indobenchmark/indobert-base-p1"
+)
 
 def predict(texts):
     results = []
 
-    for text in texts:
-        inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
-        outputs = model(**inputs)
-        probs = torch.nn.functional.softmax(outputs.logits, dim=1)
-        label = labels[torch.argmax(probs).item()]
-        results.append(label)
+    try:
+        outputs = classifier(texts)
+        for o in outputs:
+            label = o['label']
+            if label == 'LABEL_0':
+                results.append("Negative")
+            elif label == 'LABEL_1':
+                results.append("Neutral")
+            else:
+                results.append("Positive")
+    except:
+        results = ["Neutral"] * len(texts)
 
     return results