import gradio as gr
import pickle
import sklearn  # Ensure scikit-learn is available

# Load the trained model and vectorizer safely
try:
    model = pickle.load(open('model.pkl', 'rb'))  # Ensure correct file name
    vectorizer = pickle.load(open('vectorizer.pkl', 'rb'))
except Exception as e:
    print(f"Error loading model: {e}")

def predict_sms(message):
    try:
        transformed_text = vectorizer.transform([message])
        prediction = model.predict(transformed_text)[0]
        return "Spam" if prediction == 1 else "Not Spam"
    except Exception as e:
        return f"Error: {e}"

# Gradio Web Interface
iface = gr.Interface(
    fn=predict_sms,
    inputs=gr.Textbox(label="Enter SMS Message"),
    outputs=gr.Label(),
    title="SMS Spam Classifier",
    description="Enter a message to check if it's spam or not."
)

# Ensure Hugging Face properly serves the app
iface.launch(server_name="0.0.0.0", server_port=7860)