Create predict_utils.py

predict_utils.py

@@ -0,0 +1,33 @@
+import torch
+import numpy as np
+from transformers import BertTokenizer
+from model import HybridModel
+
+LABELS = ['HS', 'Abusive', 'HS_Individual', 'HS_Group', 'HS_Religion', 'HS_Race',
+          'HS_Physical', 'HS_Gender', 'HS_Other', 'HS_Weak', 'HS_Moderate', 'HS_Strong']
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+def load_model_and_thresholds():
+    model = HybridModel()
+    model.load_state_dict(torch.load("best_model_dataScrap_final.pt", map_location=DEVICE))
+    model.to(DEVICE)
+    model.eval()
+
+    tokenizer = BertTokenizer.from_pretrained("indobenchmark/indobert-base-p1")
+    thresholds = np.load("optimal_thresholds_dataScrap_final.npy")
+    return model, tokenizer, thresholds
+
+def predict(text, model, tokenizer, thresholds):
+    encoding = tokenizer(text, return_tensors='pt', padding='max_length', truncation=True, max_length=128)
+    input_ids = encoding["input_ids"].to(DEVICE)
+    attention_mask = encoding["attention_mask"].to(DEVICE)
+
+    with torch.no_grad():
+        probs = model(input_ids, attention_mask).squeeze(0).cpu().numpy()
+
+    # Konversi probabilitas ke binary
+    preds_bin = (probs > thresholds).astype(int)
+
+    # Format hasil
+    return {label: f"✅ Ya ({prob:.2f})" if pred else f"❌ Tidak ({prob:.2f})"
+            for label, prob, pred in zip(LABELS, probs, preds_bin)}