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import gradio as gr |
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import numpy as np |
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from sklearn.neighbors import KNeighborsClassifier |
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import pickle |
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with open('prueba.pkl', 'rb') as file: |
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kmprueba = pickle.load(file) |
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def modelo(Fresk, Milk, Grocery, Frozen, Detergents_Paper,Delicassen,Channel1,Channel2,Region1,Region2,Region3): |
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species = ['Grupo 0','Grupo 1', 'Grupo 2','Grupo 3'] |
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i = kmprueba.predict([[Fresk, Milk, Grocery, Frozen,Detergents_Paper,Delicassen,Channel1,Channel2,Region1,Region2,Region3]])[0] |
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return species[i] |
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interfaz = gr.Interface( |
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fn=modelo, |
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inputs=[ |
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gr.Slider(label='Fresk', minimum=0.0, maximum=5.0, step=0.05), |
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gr.Slider(label='Milk', minimum=0.0, maximum=5.0, step=0.05), |
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gr.Slider(label='Grocery', minimum=0.0, maximum=5.0, step=0.05), |
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gr.Slider(label='Frozen', minimum=0.0, maximum=5.0, step=0.05), |
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gr.Slider(label='Detergents_Paper', minimum=0.0, maximum=5.0, step=0.05), |
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gr.Slider(label='Delicassen', minimum=0.0, maximum=5.0, step=0.05), |
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gr.Slider(label='Channel1', minimum=0.0, maximum=5.0, step=0.05), |
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gr.Slider(label='Channel2', minimum=0.0, maximum=5.0, step=0.05), |
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gr.Slider(label='Region1', minimum=0.0, maximum=5.0, step=0.05), |
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gr.Slider(label='Region2', minimum=0.0, maximum=5.0, step=0.05), |
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gr.Slider(label='Region3', minimum=0.0, maximum=5.0, step=0.05), |
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], |
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outputs=gr.Textbox(label='Kmeans Grupo:'), |
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title='Ventas de productos. K-means', |
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description='Este modelo est谩 desarrollado para la agrupacion Kmeans de productos.', |
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article= 'Autor: <a href=\"https://huggingface.co/Antonio49\">Antonio Fern谩ndez</a> de <a href=\"https://saturdays.ai/\">SaturdaysAI</a>. Formaci贸n: <a href=\"https://cursos.saturdays.ai/courses/\">Cursos Online AI</a> Aplicaci贸n desarrollada con fines docentes', |
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theme='peach', |
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examples = [[0,0,0,0,0,0,0,0,0,0,0], |
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[0,1,2,2,0,0,0,0,0,0,0]] |
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) |
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interfaz.launch() |