Update app.py

app.py

@@ -1,22 +1,17 @@
 import os
-from dotenv import load_dotenv
 from transformers import TFBertForSequenceClassification, BertTokenizerFast
-import tensorflow as tf
-
-# Load environment variables
-load_dotenv()
 
 def load_model(model_name):
     try:
-        # Try loading the model as a TensorFlow model
-        model = TFBertForSequenceClassification.from_pretrained(model_name, use_auth_token=os.getenv('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'))
+        # Load TensorFlow model first
+        model = TFBertForSequenceClassification.from_pretrained(model_name, use_auth_token="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")
     except OSError:
-        # If loading fails, assume it's a PyTorch model and use from_pt=True
-        model = TFBertForSequenceClassification.from_pretrained(model_name, use_auth_token=os.getenv('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'), from_pt=True)
+        # Fallback to PyTorch model if TensorFlow fails
+        model = TFBertForSequenceClassification.from_pretrained(model_name, use_auth_token="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", 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="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")
     return tokenizer
 
 def predict(text, model, tokenizer):
@@ -25,10 +20,13 @@ def predict(text, model, tokenizer):
     return outputs
 
 def main():
-    model_name = os.getenv('Erfan11/Neuracraft')
+    # Replace 'Erfan11/Neuracraft' with the correct model path if necessary
+    model_name = "Erfan11/Neuracraft"
+
     model = load_model(model_name)
     tokenizer = load_tokenizer(model_name)
-    # Example usage
+
+    # Example prediction
     text = "Sample input text"
     result = predict(text, model, tokenizer)
     print(result)