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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()
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