app.py

import gradio as gr
import numpy as np
import pickle

# Cargar el modelo
with open('model_rf.pkl', 'rb') as file:
    rf = pickle.load(file)

# Cargar el scaler
with open('my-standard-scaler.pkl', 'rb') as file:
    s_c = pickle.load(file)

# Definir la función de predicción
def predict_1(SOC: float):
    SOC = SOC/1.724 # SOC <- MO/1.724
    prediction = 1.58 + np.exp(-0.07*SOC)
    return prediction.round(2)

def predict_2(SOC: float, Cy: float):
    SOC = SOC/1.724 # SOC <- MO/1.724
    prediction = np.array(2.03 - 0.008*Cy - 0.008*SOC)
    return prediction.round(2)

def predict_3(SOC: float, Cy: float, eCa: float, eMg: float, eK: float, eNa: float, eAlH: float):
    SOC = SOC/1.724 # SOC <- MO/1.724
    ECEC = eCa + eMg + eK + eNa + eAlH
    xK = eK/ECEC
    prediction = np.array(1.53 - 0.076*SOC + 0.004*Cy - 2.04*xK)
    return prediction.round(2)
    
def predict_4(pH: float, EC: float, CCE: float, SOC: float, Sa: float, Si: float,
            Cy: float, CEC: float, eCa: float, eMg: float, eK: float, eNa: float, eAlH: float):
    SOC = SOC/1.724 # SOC <- MO/1.724
    ECEC = eCa + eMg + eK + eNa + eAlH
    xCa = eCa/ECEC
    xMg = eMg/ECEC
    xK = eK/ECEC
    xNa = eNa/ECEC
    xAlH = eAlH/ECEC
    BS1 = (eCa + eMg + eK + eNa)/CEC
    BS2 = (eCa + eMg + eK + eNa)/ECEC
    input_features = np.array([[pH,	EC,	CCE, SOC, Sa, Si, Cy, CEC, ECEC, xCa, xMg, xK, xNa, xAlH, BS1, BS2]])
    input_features_scale = s_c.transform(input_features)
    prediction = rf.predict(input_features_scale)[0].round(2)
    return prediction
# <img src="https://www.wycia.com/wp-content/uploads/2016/09/LOGO-PUCP.jpg" width=200px>
# Crear la interfaz Gradio
with gr.Blocks() as demo:
    #with gr.Row():
    #    with gr.Column():
    #        gr.Markdown("""<img src="https://www.wycia.com/wp-content/uploads/2016/09/LOGO-PUCP.jpg" width=250px>""")
    #    with gr.Column():
    #        gr.Markdown("""<img src="https://prociencia.gob.pe/wp-content/uploads/2022/05/PROCIENCIA-LOGO-300x169.png" width=200px>""")
    #    with gr.Column():
    #        gr.Markdown("""<img src="https://i.ibb.co/k8LxVMT/images.jpg" width=150px>""")
    #gr.Markdown("""<img src="https://www.wycia.com/wp-content/uploads/2016/09/LOGO-PUCP.jpg" width=1000px height=250px >""")
    gr.Markdown("""<img src="https://i.ibb.co/dJB3MTX/banner-v1.png" width=1000px >""")
    #gr.Markdown("""<img src="https://drive.google.com/uc?export=view&id=1kmnKJcF0QIqxVDsqGcENmVe1qZJqV96m" width=1000px >""")
    gr.Markdown("# Estimación de la densidad aparente del suelo")
    
    with gr.Row():
        with gr.Column(scale=0, min_width=250):
            gr.Markdown("**M.O. (%)**")
            SOC = gr.Number(label="", value=2.9408, interactive=True, elem_classes="bold-label")
            gr.Markdown("**K<sup>+</sup> (meq/100g)**")
            eK = gr.Number(label="", value=0.47, interactive=True)
            gr.Markdown("**pH (1:1) en H<sub>2</sub>O**")
            pH = gr.Number(label="", value=7.09, interactive=True)
            gr.Markdown("**C.E. (1:1 dS/m)**")
            EC = gr.Number(label="", value=0.31, interactive=True)
            
        with gr.Column(scale=0, min_width=250):
            gr.Markdown("**Arcilla (%)**")
            Cy = gr.Number(label="", value=31.0, interactive=True)
            gr.Markdown("**Na<sup>+</sup> (meq/100g)**")
            eNa = gr.Number(label="", value=0.15, interactive=True)
            gr.Markdown("**CaCO<sub>2</sub> (%)**")
            CCE = gr.Number(label="", value=0.20, interactive=True) 
            
            
        with gr.Column(scale=0, min_width=250):
            gr.Markdown("**Ca<sup>2+</sup> (meq/100g)**")
            eCa = gr.Number(label="", value=19.44, interactive=True)
            gr.Markdown("**Al<sup>3+</sup> + H<sup>+</sup> (meq/100g)**")
            eAlH = gr.Number(label="", value=0.0, interactive=True)
            gr.Markdown("**Limo (%)**")
            Si = gr.Number(label="", value=24.0, interactive=True)  
            
        with gr.Column(scale=0, min_width=250):  
            gr.Markdown("**Mg<sup>2+</sup> (meq/100g)**")
            eMg = gr.Number(label="", value=3.47, interactive=True)
            gr.Markdown("**Arena (%)**")
            Sa = gr.Number(label="", value=45.0, interactive=True)
            gr.Markdown("**CIC (meq/100g)**")
            CEC = gr.Number(label="", value=23.52, interactive=True)   

    gr.Markdown("Seleccione un modelo:")
    with gr.Row():
        with gr.Column(scale=0, min_width=250):
            submit_1 = gr.Button(value='Abdelbaki')
        with gr.Column(scale=0, min_width=250):
            submit_2 = gr.Button(value='Benites')
        with gr.Column(scale=0, min_width=250):
            submit_3 = gr.Button(value='MLRegression')
        with gr.Column(scale=0, min_width=250):
            submit_4 = gr.Button(value='Random Forest')

    gr.Markdown("Densidad aparente del suelo (g/cm<sup>3</sup>):")
    output = gr.Textbox(label="", interactive=False)

    submit_1.click(predict_1, inputs=[SOC], outputs=[output])
    submit_2.click(predict_2, inputs=[SOC, Cy], outputs=[output])
    submit_3.click(predict_3, inputs=[SOC, Cy, eCa, eMg, eK, eNa, eAlH], outputs=[output])
    submit_4.click(predict_4, inputs=[pH , EC, CCE, SOC, Sa, Si, Cy, CEC, eCa, eMg, eK, eNa, eAlH], outputs=[output])

demo.launch(share=False, debug=False)