app.py

from transformers import pipeline
import gradio as gr

# Load spam detection model pipeline
classifier = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-sms-spam-detection")

# Define the prediction function
def predict_spam(text):
    result = classifier(text)[0]
    label = result['label']
    confidence = round(result['score'] * 100, 2)
    return f"Prediction: {label}\nConfidence: {confidence}%"

# Gradio interface
gr.Interface(
    fn=predict_spam,
    inputs=gr.Textbox(lines=4, placeholder="Enter a message..."),
    outputs="text",
    title="Spam Detector",
    description="Enter a message to check if it's spam or not using a pretrained BERT model.",
    examples=["Win a free iPhone now!", "Hey, are we still on for dinner tonight?"]
).launch()