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)

# 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")
    outputs = model(**inputs)
    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))