import gradio as gr
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
import pickle
with open('prueba.pkl', 'rb') as file:
kmprueba = pickle.load(file)
def modelo(Fresk, Milk, Grocery, Frozen, Detergents_Paper,Delicassen,Channel1,Channel2,Region1,Region2,Region3):
species = ['Grupo 0','Grupo 1', 'Grupo 2','Grupo 3']
i = kmprueba.predict([[Fresk, Milk, Grocery, Frozen,Detergents_Paper,Delicassen,Channel1,Channel2,Region1,Region2,Region3]])[0]
return species[i]
interfaz = gr.Interface(
fn=modelo,
inputs=[
gr.Slider(label='Fresk', minimum=0.0, maximum=5.0, step=0.05),
gr.Slider(label='Milk', minimum=0.0, maximum=5.0, step=0.05),
gr.Slider(label='Grocery', minimum=0.0, maximum=5.0, step=0.05),
gr.Slider(label='Frozen', minimum=0.0, maximum=5.0, step=0.05),
gr.Slider(label='Detergents_Paper', minimum=0.0, maximum=5.0, step=0.05),
gr.Slider(label='Delicassen', minimum=0.0, maximum=5.0, step=0.05),
gr.Slider(label='Channel1', minimum=0.0, maximum=5.0, step=0.05),
gr.Slider(label='Channel2', minimum=0.0, maximum=5.0, step=0.05),
gr.Slider(label='Region1', minimum=0.0, maximum=5.0, step=0.05),
gr.Slider(label='Region2', minimum=0.0, maximum=5.0, step=0.05),
gr.Slider(label='Region3', minimum=0.0, maximum=5.0, step=0.05),
],
outputs=gr.Textbox(label='Kmeans Grupo:'),
title='Ventas de productos. K-means',
description='Este modelo está desarrollado para la agrupacion Kmeans de productos.',
article= 'Autor: Antonio Fernández de SaturdaysAI. Formación: Cursos Online AI Aplicación desarrollada con fines docentes',
theme='peach',
examples = [[0,0,0,0,0,0,0,0,0,0,0],
[0,1,2,2,0,0,0,0,0,0,0]]
)
interfaz.launch()