app.py

from __future__ import division, print_function
# coding=utf-8
import sys
import os
import glob
import json
import re
import numpy as np

# Keras
from keras.applications.imagenet_utils import preprocess_input, decode_predictions
from keras.models import load_model
from keras.preprocessing import image

# Flask utils
from flask import Flask, redirect, url_for, request, render_template
from werkzeug.utils import secure_filename
from gevent.pywsgi import WSGIServer

# Define a flask app
app = Flask(__name__)

# Model saved with Keras model.save()
# MODEL_PATH = 'models/your_model.h5'

# Load your trained model
# model = load_model(MODEL_PATH)
# model._make_predict_function()          # Necessary
# print('Model loaded. Start serving...')

# You can also use pretrained model from Keras
# Check https://keras.io/applications/
from keras.applications.vgg16 import VGG16
model = VGG16(weights='imagenet', include_top=False)
print('Model loaded. Check http://127.0.0.1:5000/')


def model_predict(img_path, model):
    img = image.load_img(img_path, target_size=(224, 224))

    # Preprocessing the image
    x = image.img_to_array(img)
    # x = np.true_divide(x, 255)
    x = np.expand_dims(x, axis=0)

    # Be careful how your trained model deals with the input
    # otherwise, it won't make correct prediction!
    x = preprocess_input(x)

    preds = model.predict(x)
    return preds


@app.route('/', methods=['GET'])
def index():
    # Main page
    return render_template('index.html')


@app.route('/predict', methods=['GET', 'POST'])
def upload():
    if request.method == 'POST':
        # Get the file from post request
        f = request.files['file']

        vgg16_feature_list = []

        # Save the file to ./uploads
        basepath = os.path.dirname(__file__)
        file_path = os.path.join(
            basepath, 'uploads', secure_filename(f.filename))
        f.save(file_path)

        vgg16_feature = model_predict(file_path, model)

        vgg16_feature_np = np.array(vgg16_feature)
        print(vgg16_feature_np)
        print('112')
        # np.set_printoptions(threshold=np.nan)

        np_array_to_list = vgg16_feature_np.tolist()
        print(np_array_to_list)
        print('1231')
        print(np_array_to_list)
        print('223')
        json_string = json.dumps({"data": np_array_to_list})

        return json_string
        # # Make prediction
        # preds = model_predict(file_path, model)

        # # Process your result for human
        # # pred_class = preds.argmax(axis=-1)            # Simple argmax
        # pred_class = decode_predictions(preds, top=1)   # ImageNet Decode
        # result = str(pred_class[0][0][1])               # Convert to string
        # return result
    return None


if __name__ == '__main__':
    # app.run(port=5002, debug=True)

    # Serve the app with gevent
    http_server = WSGIServer(('', 5000), app)
    http_server.serve_forever()