# -*- 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.