1

app.py

@@ -2,7 +2,6 @@ import gradio as gr
 import torch
 from transformers import BertTokenizer, BertModel  
 import torch.nn.functional as F
-import os
 
 # Load model and tokenizer from Hugging Face
 model_name = "shobrunjb/mtl-indoBERT-product-review"
@@ -26,18 +25,13 @@ class IndoBERTMultiTaskClassifier(torch.nn.Module):
 
         return logits_task1, logits_task2
 
-# Check if the model file exists
-model_path = "pytorch_model.bin"
-if os.path.exists(model_path):
-    model = IndoBERTMultiTaskClassifier(
-        bert_model_name=model_name,
-        num_labels_task1=3,  # Adjust with your task1 classes
-        num_labels_task2=3   # Adjust with your task2 classes
-    )
-    model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
-    model.eval()
-else:
-    raise FileNotFoundError(f"Model file '{model_path}' not found. Please ensure the file is available.")
+# Load model directly from Hugging Face
+model = IndoBERTMultiTaskClassifier(
+    bert_model_name=model_name,
+    num_labels_task1=3,  # Adjust with your task1 classes
+    num_labels_task2=3   # Adjust with your task2 classes
+)
+model.eval()
 
 # Define label mappings
 label_mapping_task1 = ["trusted", "fake", "non"]  # Adjust with your task1 labels