Update model.py

model.py

@@ -5,10 +5,6 @@ from transformers import BertTokenizer, BertForSequenceClassification
 tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
 model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
 
-# Load the model (Assuming it's already trained and saved in "./saved_model")
-# If you don't have a trained model, comment out this line. The code will use the default BERT model
-model = BertForSequenceClassification.from_pretrained("./saved_model")
-
 # Predicting Function
 def predict(text):
     inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")
@@ -16,6 +12,6 @@ def predict(text):
     predictions = torch.argmax(outputs.logits, dim=-1)
     return "AI-generated" if predictions.item() == 1 else "Human-written"
 
-# Get user input and predict
-user_input = input("Enter the text you want to classify: ")
-print("Classified as:", predict(user_input))
+# Example Usage (commented out as it's not needed for web deployment)
+# user_input = input("Enter the text you want to classify: ")
+# print("Classified as:", predict(user_input))