Update app.py

app.py

@@ -4,14 +4,14 @@ from transformers import TFBertForSequenceClassification, BertTokenizerFast
 def load_model(model_name):
     try:
         # Load TensorFlow model from Hugging Face
-        model = TFBertForSequenceClassification.from_pretrained(model_name, use_auth_token=os.getenv('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'))
+        model = TFBertForSequenceClassification.from_pretrained(model_name, use_auth_token=os.getenv('API_KEY'))
     except OSError:
         # Fallback to PyTorch model if TensorFlow fails
-        model = TFBertForSequenceClassification.from_pretrained(model_name, use_auth_token=os.getenv('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'), from_pt=True)
+        model = TFBertForSequenceClassification.from_pretrained(model_name, use_auth_token=os.getenv('API_KEY'), from_pt=True)
     return model
 
 def load_tokenizer(model_name):
-    tokenizer = BertTokenizerFast.from_pretrained(model_name, use_auth_token=os.getenv('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'))
+    tokenizer = BertTokenizerFast.from_pretrained(model_name, use_auth_token=os.getenv('API_KEY'))
     return tokenizer
 
 def predict(text, model, tokenizer):
@@ -20,9 +20,9 @@ def predict(text, model, tokenizer):
     return outputs
 
 def main():
-    model_name = os.getenv('Erfan11/Neuracraft')
+    model_name = os.getenv('MODEL_PATH')
     if model_name is None:
-        raise ValueError("Erfan11/Neuracraft environment variable not set or is None")
+        raise ValueError("MODEL_PATH environment variable not set or is None")
 
     model = load_model(model_name)
     tokenizer = load_tokenizer(model_name)