import gradio as gr
from transformers import pipeline, BertForSequenceClassification, BertTokenizer

# Load the model and tokenizer from the Hugging Face Hub
model = BertForSequenceClassification.from_pretrained("KaranNag/Ai_text_model")
tokenizer = BertTokenizer.from_pretrained("KaranNag/Ai_text_model")

# Create a pipeline
text_classification = pipeline('text-classification', model=model, tokenizer=tokenizer)

# Define the Gradio interface function
def classify_text(text):
    result = text_classification(text)
    label = result[0]['label']
    score = result[0]['score']
    return f"Label: {label}, Score: {score:.4f}"

# Create Gradio interface
interface = gr.Interface(fn=classify_text, inputs='text', outputs='text', title='Text Classification', description='Classify text as human or AI generated')

if __name__ == "__main__":
    interface.launch()