server.py

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

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

# Keras
from keras.applications.imagenet_utils import preprocess_input, decode_predictions
from tensorflow.keras.preprocessing import image
from tensorflow.keras.models import load_model
from tensorflow.keras.applications.resnet50 import ResNet50

app = Flask(__name__)
# app.debug =1


MODEL_PATH = 'models/your_model.h5'

model = ResNet50(weights='imagenet')

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

@app.route('/image')
def imager():
	return render_template('pages/image.html')

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

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

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, mode='caffe')
    preds = model.predict(x)
    return preds

@app.route('/predict', methods=['GET', 'POST'])
def upload():
    if request.method == 'POST':
        # Get the file from post request
        f = request.files['image']
        # Save the file to ./uploads
        basepath = os.path.dirname(__file__)
        print(basepath)
        file_path = os.path.join(basepath, 'uploads', secure_filename(f.filename))
        f.save(file_path)
        # 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(('0.0.0.0', 5000), app)
    http_server.serve_forever()