| import torch | |
| from transformers import BertTokenizer, BertForSequenceClassification | |
| # Tokenizer and Model Initialization | |
| tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') | |
| model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2) | |
| # Predicting Function | |
| def predict(text): | |
| inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt") | |
| outputs = model(**inputs) | |
| predictions = torch.argmax(outputs.logits, dim=-1) | |
| return "AI-generated" if predictions.item() == 1 else "Human-written" | |
| # 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)) | |