app.py

import gradio as gr
import joblib
import pandas as pd

# Load the trained model (make sure the model file is named correctly)
model = joblib.load('crop_recommendation_model.joblib')

def predict_crop(temperature, humidity, rainfall):
    """
    Predict the recommended crop based on temperature, humidity, and rainfall.
    """
    # Create a DataFrame from the input values
    user_input = pd.DataFrame({
        'temperature': [temperature],
        'humidity': [humidity],
        'rainfall': [rainfall]
    })

    # Make prediction
    prediction = model.predict(user_input)

    # Return the first (and only) prediction
    return prediction[0]

# Define the Gradio interface
iface = gr.Interface(
    fn=predict_crop,
    inputs=[
        gr.Number(label="Temperature (°C)"),
        gr.Number(label="Humidity (%)"),
        gr.Number(label="Rainfall (mm)")
    ],
    outputs=gr.Textbox(label="Recommended Crop"),
    title="🌾 Crop Recommendation System",
    description="Enter the temperature, humidity, and rainfall to get a crop recommendation."
)

# Launch the Gradio app
if __name__ == "__main__":
    iface.launch()