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import gradio as gr
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
import pickle
with open('modelo (3).pkl', 'rb') as file:
knn = pickle.load(file)
# Crear un modelo KNN de ejemplo para que funcione el c贸digo
#knn = KNeighborsClassifier(n_neighbors=3)
#X = np.array([[6.7, 3.0, 5.2, 2.3], [4.7, 3.2, 1.3, 0.2], [5.0, 3.6, 1.4, 0.2]])
#y = np.array([0, 1, 2])
#knn.fit(X, y)
def modelo(sepal_length, sepal_width, petal_length, petal_width):
species = ['Iris-Setosa', 'Iris-Versicolour', 'Iris-Virginica']
i = knn.predict([[sepal_length, sepal_width, petal_length, petal_width]])[0]
return species[i]
interfaz = gr.Interface(
fn=modelo,
inputs=[
gr.Slider(label='Sepal Length', minimum=0.0, maximum=8.0, step=0.1),
gr.Slider(label='Sepal Width', minimum=0.0, maximum=8.0, step=0.1),
gr.Slider(label='Petal Length', minimum=0.0, maximum=8.0, step=0.1),
gr.Slider(label='Petal Width', minimum=0.0, maximum=8.0, step=0.1),
],
outputs=gr.Textbox(label='Specie'),
title='Detector de especies de iris',
description='Este modelo est谩 desarrollado para la clasificaci贸n de flores de la especie Iris.',
article= 'Autor: <a href=\"https://huggingface.co/Antonio49\">Antonio Fern谩ndez</a> de <a href=\"https://saturdays.ai/\">SaturdaysAI</a>. Aplicaci贸n desarrollada con fines docentes',
theme='peach',
examples = [[5,7,0,0],
[0,1,2,8]]
)
interfaz.launch()
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