main.py

from flask import Flask, redirect, request, render_template, url_for, flash, session
from keras.backend import tensorflow_backend as backend
from PIL import Image
from werkzeug import secure_filename
import os, io
import base64
from tensorflow.keras.models import model_from_json
import glob
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt

app = Flask(__name__)
app.config['UPLOAD_FOLDER'] = os.path.join("file://", os.getcwd(), 'uploads')
app.secret_key = 'ahiahi'


def pla_num(number):
    if number == 0:
        return '銅像'
    elif number == 3:
        return '教育研究4号棟'
    elif number == 15:
        return '総合研究1号棟'
    elif number == 26:
        return 'インタラクティブ教育棟'
    elif number == 33:
        return '図書館'
    elif number == 52:
        return 'LL'
    elif number == 55:
        return '食堂'
    elif number == 62:
        return 'ものつくり工房'
    elif number == 64:
        return '体育館'

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/predict', methods=['GET','POST'])
def predict():
    if request.method == 'POST':
        if 'file' not in request.files:
            return redirect(url_for('index'))
        else:
            img = request.files["file"]
            filename = secure_filename(img.filename)
            
            if filename == '':
                flash('ファイルがないお')
                return redirect(url_for('index'))
            else:
                root, ext = os.path.splitext(filename)
                ext = ext.lower()
                extset = set([".jpg", ".jpeg", ".png"])
                
                if ext not in extset:
                    flash('対応してない拡張子です')
                    return redirect(url_for('index'))
                else:
                    backend.clear_session() # 2回以上連続してpredictするために必要な処理
                    # モデルの読み込み
                    model = model_from_json(open('and.json', 'r').read())
                
                    # 重みの読み込み
                    model.load_weights('and_weight.hdf5')
                

                    image_size = 60
                
                    image = Image.open(img)
                    fileimg = image
                    image = image.convert("RGB")
                    image = image.resize((image_size, image_size))
                    data = np.asarray(image)
                    X = np.array(data)
                    X = X.astype('float32')
                    X = X / 255.0
                    X = X[None, ...]
                
                    prd = model.predict(X).argmax(axis=1)
                    ans = [3, 62, 26, 52, 55, 33, 15, 64, 0]
                    number = ans[int(prd)]
                    #fileimg.open(img)
                    buf = io.BytesIO()
                    fileimg.save(buf, 'png')
                    qr_b64str = base64.b64encode(buf.getvalue()).decode("utf-8")
                    qr_b64data = "data:image/png;base64,{}".format(qr_b64str)



                    return render_template('predict.html', img=qr_b64data, number=number, place=pla_num(number))

    else:
        return redirect(url_for('index'))

if __name__ == "__main__":
    app.run(host='0.0.0.0')