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()