app.py

# python3 -m flask run
# python3 -m flask run --port 8000
from flask import Flask, jsonify, request, make_response, render_template

import joblib
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
# import streamlit as st
# import requests

app = Flask(__name__)
app.config["DEBUG"] = False

@app.route('/', methods=['GET'])
def index():
  # return make_response(jsonify({'data': 'success'}), 200)
  return render_template('home.html')

@app.route('/predict', methods=['POST'])
def hello():
  data = request.get_json()
  umur = data['umur']
  jk = data['jk']
  tinggi = data['tinggi']

  knn = joblib.load('knn.model')
  test = pd.DataFrame({'Umur (bulan)': [umur], 'Jenis Kelamin': [jk], 'Tinggi Badan (cm)': [tinggi]})
  pred = knn.predict(test)
  a = np.array(pred)
  b = a.tolist()

  return make_response(jsonify({'data': b}), 200)

@app.route('/bb-u', methods=['POST'])
def berat():
  data = request.get_json()
  umur = data['umur']
  jk = data['jk']
  berat = data['berat']

  knn = joblib.load('knn_berat_status_gizi.model')
  test = pd.DataFrame({'Umur (bulan)': [umur], 'Berat Badan (kg)': [berat], 'Jenis Kelamin': [jk]})
  pred = knn.predict(test)
  a = np.array(pred)
  b = a.tolist()

  return make_response(jsonify({'data': b}), 200)
# app.run()
app.run(host='0.0.0.0', port=5001, use_reloader=False)