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(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):
    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

# Crear la interfaz Gradio
with gr.Blocks() as demo:
    gr.Markdown("# Estima tu % de grasa corporal")
    
    pH = gr.Number(label="pH (--)", value=7.09, interactive=True)
    EC = gr.Number(label="Ec (--)", value=0.31, interactive=True)
    CCE = gr.Number(label="CCE (--)", value=0.20, interactive=True)
    SOC = gr.Number(label="SOC (--)", value=2.9408, interactive=True)
    Sa = gr.Number(label="Sa (--)", value=45.0, interactive=True)
    Si = gr.Number(label="Si (--)", value=24.0, interactive=True)
    Cy = gr.Number(label="Cy (--)", value=31.0, interactive=True)
    CEC = gr.Number(label="CEC (--)", value=23.52, interactive=True)
    eCa = gr.Number(label="eCa (--)", value=19.44, interactive=True)
    eMg = gr.Number(label="eMg (--)", value=3.47, interactive=True)
    eK = gr.Number(label="eK (--)", value=0.47, interactive=True)
    eNa = gr.Number(label="eNa (--)", value=0.15, interactive=True)
    eAlH = gr.Number(label="eAlH (--)", value=0.0, interactive=True)
    
    submit = gr.Button(value='Predecir')
    output = gr.Textbox(label=": soil bulk density", interactive=False)
    
    submit.click(predict, inputs=[pH , EC, CCE, SOC, Sa, Si, Cy, CEC, eCa, eMg, eK, eNa, eAlH], outputs=[output])

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