Update predict.py

predict.py

@@ -9,21 +9,21 @@ model.eval()
 
 # Load the reverse label mapping
 reverse_label_mapping = {
-    0: "label_0",
-    1: "label_1",
-    2: "label_2",
-    3: "label_3",
-    4: "label_4",
-    5: "label_5",
-    6: "label_6",
-    7: "label_7",
-    8: "label_8",
-    9: "label_9",
-    10: "label_10",
-    11: "label_11",
-    12: "siyaset"  # Example: Map index 12 back to "siyaset"
+    0: "turkiye",
+    1: "ekonomi",
+    2: "dunya",
+    3: "spor",
+    4: "magazin",
+    5: "guncel",
+    6: "genel",
+    7: "siyaset",
+    8: "saglik",
+    9: "kultur-sanat",
+    10: "teknoloji",
+    11: "yasam"
 }
 
+
 def predict(text):
     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="max_length", max_length=512)
     inputs = {key: value.to("cuda" if torch.cuda.is_available() else "cpu") for key, value in inputs.items()}