# -*- coding: utf-8 -*- from flask import Flask, jsonify, request from iris import iris_classifier from pprint import pprint import numpy as np import requests import json # Main app: app = Flask(__name__) # Global: version = 'v0.0' model = iris_classifier() species = {'0': 'I. setosa', '1': 'I. versicolor', '2': 'I. virginica'} # API MAIN STRUCTURE: @app.route('/api/' + version, methods=['GET']) def test(): """ GET method to test the API. """ # Output message: message = {"response": [{"text": "Hello world!"}]} return jsonify(message)
# its conditions. # =============================================================== from flask import Flask, jsonify, request from iris import iris_classifier from pprint import pprint import numpy as np import requests import json # Main app: app = Flask(__name__) # Global: version = 'v0.0' classifier = iris_classifier() species = {'0': 'I. setosa', '1': 'I. versicolor', '2': 'I. virginica'} # API MAIN STRUCTURE: @app.route('/api/' + version, methods=['GET']) def test(): """ GET method to test the API. """ # Output message: message = {"response": [{"text": "Hello world!"}]} return jsonify(message)
import streamlit as st import pandas as pd import numpy as np from iris import iris_classifier from figure import plotly_figure_1 from figure import plotly_figure_2 data = load_iris() iris = pd.DataFrame(data.data, columns=data.feature_names) classes = data.target_names models = { "Árbol de decisión": iris_classifier() } # Sección de introducción st.title("Predicción de especies de Iris usando scikit-learn y Streamlit") st.write( """ Bienvenid@ a este sencillo ejemplo que ejecuta un modelo entrenado de scikit-learn directo en Streamlit. """ ) # Sección de datos st.write( """ A continuación los datos utilizados.