import gradio as gr
import joblib
import numpy as np

# Load the model and scaler
model = joblib.load("conme.pkl")  
scaler = joblib.load("scaler.joblib")

def predict(soil_moisture, temperature, air_humidity, light_intensity):
    # Prepare input data
    input_data = np.array([[soil_moisture, temperature, air_humidity, light_intensity]])
    # Scale the data
    std_data = scaler.transform(input_data)
    # Make prediction
    prediction = model.predict(std_data)
    return int(prediction[0])  # Convert prediction to int (0 or 1)

# Set up Gradio interfacea
iface = gr.Interface(
    fn=predict,
    inputs=[
        gr.Number(label="Soil Moisture"),
        gr.Number(label="Temperature"),
        gr.Number(label="Air Humidity"),
        gr.Number(label="Light Intensity"),
    ],
    outputs="text",
    title="Plant Watering Prediction"
)

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