import gradio as gr
import pickle
import numpy as np
import os

# Load the RandomForest model
working_dir = os.path.dirname(os.path.abspath(__file__))
model = pickle.load(open(f'{working_dir}/RF_Crop.sav', 'rb'))



# Define the prediction function
def predict_crop(N, P, K, temperature, humidity, pH, rainfall):
    user_input = np.array([[N, P, K, temperature, humidity, pH, rainfall]])
    if np.all(user_input == 0):
        return "Please enter valid values."
    else:
        prediction = model.predict(user_input)
        crop = prediction[0]
        return f"Hey, you should grow **{crop}** based on your soil and environmental factors."

# Gradio UI components
def main_interface():
    with gr.Blocks() as demo:
    
        
        with gr.Tab("Get Recommendation"):
            gr.Markdown("Enter the details about your soil and environmental factors to get a crop recommendation.")
            gr.Markdown("**Example Values:** [104, 18, 30, 23.6, 60.3, 6.7, 140.91] or [60, 18, 30, 23.6, 60.3, 8, 40.91]")

            N = gr.Number(label="Nitrogen (N)", value=0, precision=0)
            P = gr.Number(label="Phosphorus (P)", value=0, precision=0)
            K = gr.Number(label="Potassium (K)", value=0, precision=0)
            temperature = gr.Number(label="Temperature (°C)", value=0.0)
            humidity = gr.Number(label="Humidity (%)", value=0.0)
            pH = gr.Number(label="pH", value=0.0)
            rainfall = gr.Number(label="Rainfall (mm)", value=0.0)

            output = gr.Textbox(label="Recommendation", interactive=False)

            gr.Button("Predict").click(
                predict_crop, 
                inputs=[N, P, K, temperature, humidity, pH, rainfall], 
                outputs=output
            )

    return demo

# Run the Gradio app
if __name__ == "__main__":
    app = main_interface()
    app.launch()