app.py

import gradio as gr
import pandas as pd
import pickle

# Load model
with open("random_forest_model.pkl", "rb") as f:
    loaded_model = pickle.load(f)

# Load scaler
with open("standard_scaler.pkl", "rb") as f:
    loaded_scaler = pickle.load(f)


def predict_crop(N, P, K, temperature, humidity, ph, rainfall):

    input_data = pd.DataFrame(
        [[N, P, K, temperature, humidity, ph, rainfall]],
        columns=[
            "N",
            "P",
            "K",
            "temperature",
            "humidity",
            "ph",
            "rainfall",
        ],
    )

    scaled_input = loaded_scaler.transform(input_data)

    prediction = loaded_model.predict(scaled_input)[0]

    return f"🌱 Recommended Crop: {prediction}"


iface = gr.Interface(
    fn=predict_crop,
    inputs=[
        gr.Number(label="Nitrogen (N)", minimum=0, maximum=140, value=50),
        gr.Number(label="Phosphorus (P)", minimum=5, maximum=145, value=50),
        gr.Number(label="Potassium (K)", minimum=5, maximum=205, value=50),
        gr.Number(label="Temperature (°C)", minimum=8.8, maximum=43.7, value=25),
        gr.Number(label="Humidity (%)", minimum=14.2, maximum=100, value=70),
        gr.Number(label="pH", minimum=3.5, maximum=9.9, value=6.5),
        gr.Number(label="Rainfall (mm)", minimum=20.2, maximum=298.6, value=100),
    ],
    outputs=gr.Textbox(label="Prediction"),
    title="🌾 Crop Recommendation System",
    description="Enter soil nutrients and environmental parameters to receive a recommended crop.",
)

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