Fix: Load model from local .pth file instead of Hub

app.py

@@ -1,17 +1,39 @@
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForTokenClassification
 import torch
+import os
 
-# Load model dan tokenizer dari HuggingFace Hub
-MODEL_NAME = "ecaaa09/IndoELECTRA-NER-Singgalang"
+# Label mapping
+LABEL_LIST = ['O', 'B-Organisation', 'I-Organisation', 'B-Person', 'I-Person', 'B-Place', 'I-Place']
+LABEL_TO_ID = {l: i for i, l in enumerate(LABEL_LIST)}
+ID_TO_LABEL = {i: l for l, i in LABEL_TO_ID.items()}
 
-print("Loading model...")
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+# Base model
+BASE_MODEL = "ChristopherA08/IndoELECTRA"
+MODEL_PATH = "./model.pth"
+
+print("Loading tokenizer and model...")
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
+
+# Load model architecture
 model = AutoModelForTokenClassification.from_pretrained(
-    MODEL_NAME,
-    ignore_mismatched_sizes=True  # Handle vocab size mismatch
+    BASE_MODEL,
+    num_labels=len(LABEL_LIST),
+    label2id=LABEL_TO_ID,
+    id2label=ID_TO_LABEL
 )
-print("Model loaded successfully!")
+
+# Resize embeddings (penting!)
+model.resize_token_embeddings(len(tokenizer))
+
+# Load weights dari .pth file
+if os.path.exists(MODEL_PATH):
+    print(f"Loading weights from {MODEL_PATH}...")
+    state_dict = torch.load(MODEL_PATH, map_location="cpu")
+    model.load_state_dict(state_dict)
+    print("✅ Model loaded successfully!")
+else:
+    print(f"⚠️  Warning: {MODEL_PATH} not found. Using base model.")
 
 def predict_ner(text):
     """

model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:005c48c9d56e3718ee8f4269303d63b97dd269817dba1e7a1e87d0ad8235cba0
+size 449428284