1

app.py

@@ -2,8 +2,9 @@ import gradio as gr
 import torch
 from transformers import BertTokenizer, BertModel  
 import torch.nn.functional as F
+import os
 
-# Load model dan tokenizer dari Hugging Face
+# Load model and tokenizer from Hugging Face
 model_name = "shobrunjb/mtl-indoBERT-product-review"
 tokenizer = BertTokenizer.from_pretrained(model_name)
 
@@ -25,34 +26,38 @@ class IndoBERTMultiTaskClassifier(torch.nn.Module):
 
         return logits_task1, logits_task2
 
-# Load the model
-model = IndoBERTMultiTaskClassifier(
-    bert_model_name="shobrunjb/mtl-indoBERT-product-review",
-    num_labels_task1=3,  # Adjust with your task1 classes
-    num_labels_task2=3   # Adjust with your task2 classes
-)
-model.load_state_dict(torch.load("pytorch_model.bin", map_location=torch.device('cpu')))
-model.eval()
+# 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.")
 
 # Define label mappings
 label_mapping_task1 = ["trusted", "fake", "non"]  # Adjust with your task1 labels
 label_mapping_task2 = ["positive", "negative", "neutral"]  # Adjust with your task2 labels
 
 def classify(text):
-    # Tokenisasi input teks
+    # Tokenize input text
     inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
     input_ids = inputs['input_ids']
     attention_mask = inputs['attention_mask']
 
-    # Prediksi dengan model
+    # Prediction with model
     with torch.no_grad():
         logits_task1, logits_task2 = model(input_ids, attention_mask)
     
-    # Softmax untuk mendapatkan probabilitas
+    # Softmax to get probabilities
     probs_task1 = F.softmax(logits_task1, dim=1).cpu().numpy()
     probs_task2 = F.softmax(logits_task2, dim=1).cpu().numpy()
 
-    # Prediksi label dengan probabilitas tertinggi
+    # Predict label with highest probability
     pred_task1 = label_mapping_task1[probs_task1.argmax()]
     pred_task2 = label_mapping_task2[probs_task2.argmax()]
 
@@ -61,8 +66,8 @@ def classify(text):
 # Gradio Interface
 iface = gr.Interface(fn=classify, 
                      inputs="text", 
-                     outputs=[gr.outputs.Label(label="Fake Review Detection"), 
-                              gr.outputs.Label(label="Sentiment Classification")],
+                     outputs=[gr.Label(label="Fake Review Detection"), 
+                              gr.Label(label="Sentiment Classification")],
                      title="Multitask IndoBERT: Fake Review & Sentiment Classification",
                      description="Enter a skincare product review in Indonesian and the model will classify it as fake or trusted, and determine the sentiment.")