app.py

import gradio as gr
import joblib

# Load the model and label encoder
model = joblib.load("soil.pkl")
label_encoder = joblib.load("label_encoder.pkl")

def predict_crop(N, P, K, temperature, humidity, ph, rainfall):
    input_data = [[N, P, K, temperature, humidity, ph, rainfall]]
    prediction = model.predict(input_data)
    crop = label_encoder.inverse_transform(prediction)[0]
    return f"Recommended Crop: {crop}"

demo = gr.Interface(
    fn=predict_crop,
    inputs=[
        gr.Number(label="Nitrogen"),
        gr.Number(label="Phosphorus"),
        gr.Number(label="Potassium"),
        gr.Number(label="Temperature (°C)"),
        gr.Number(label="Humidity (%)"),
        gr.Number(label="pH"),
        gr.Number(label="Rainfall (mm)")
    ],
    outputs="text",
    title="Crop Recommendation System",
    description="Enter your soil and weather data to get a crop suggestion!"
)

demo.launch()