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: <a href=\"https://huggingface.co/Antonio49\">Antonio Fernández</a> de <a href=\"https://saturdays.ai/\">SaturdaysAI</a>. Formación: <a href=\"https://cursos.saturdays.ai/courses/\">Cursos Online AI</a> 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()