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app.py
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app.py
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from __future__ import division, print_function
# coding=utf-8
import os
import sys
import glob
import re
import numpy as np
# keras
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.applications.imagenet_utils import preprocess_input, decode_predictions
from tensorflow.keras.preprocessing.image import ImageDataGenerator, img_to_array
# Flask utils
from flask import Flask, flash, render_template, request, jsonify, make_response, url_for, redirect
from flask_cors import CORS, cross_origin
from werkzeug.utils import secure_filename
from gevent.pywsgi import WSGIServer
import time
import base64
import io
from PIL import Image
import warnings
from tensorboard.summary.v1 import image
from tensorflow.python.data.experimental.ops.optimization import model
warnings.filterwarnings("ignore")
# define global paths for Image
# IMG_FOLDER = os.path.join('static', 'images')
UPLOAD_FOLDER = 'static/uploads'
ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}
# Define a flask app
app = Flask(__name__)
# Config environment variables
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
app.config["UPLOADED_IMG_URL"] = ""
def allowed_file(filename):
return "." in filename and \
filename.rsplit(".", 1)[1].lower() in ALLOWED_EXTENSIONS
# Save & Load Model
def get_model():
model_path = 'static/models/VGG16_model_at_20210319_235059.h5'
model = load_model(model_path)
print("* Model loaded!")
# Load your trained model
model = load_model(model_path)
model._make_predict_function() # Necessary
# Preprocessing function
def preprocess_image(image, target_size):
if image.mode != "RGB":
image = image.convert("RGB")
image = image.resize.convert("RGB")
image = img_to_array(image)
image = np.expand_dims(image, axis=0)
return image
print(" *Loading Keras model...")
get_model()
# Predict model
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
# Upload file
@app.route("/upload/<filename>")
def uploaded_file(filename):
fileURL = os.path.join("uploads", filename)
return render_template("index.html", fileURL=fileURL)
@app.route("/predict", methods=['GET', 'POST'])
def upload_file():
if request.method == 'POST':
# check if the post request has the file path
if 'file' not in request.files:
flash('No file path')
return redirect(request.url)
# Get the file from post request
f = request.files['file']
# if user does't select file, browser also
# submit an empty part without filename
if f.filename == '':
flash('No selected file')
time.sleep(10)
return redirect(request.url)
if f and allowed_file(f.filename):
filename = secure_filename(f.filename)
file_path = f.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
return redirect(url_for('uploaded_file', filename=filename))
# Make model prediction
pred = preprocess_image(file_path, model)
#Read prediction
# pred = model_predict(filename, model)
# pred_class = decode_predictions(pred, top=1)
# result = str(pred_class[0][0][1])
# return result
# return None
# # Read Prediction
@app.route("/", methods=["POST"])
@cross_origin()
def predict():
# print("request", request)
message = request.get_json()
# print("### message", message)
encoded = message['image'].split(",")[-1]
decoded = base64.b64decode(encoded)
image = Image.open(io.BytesIO(decoded))
input_image_size = (224, 224)
processed_image = preprocess_image(image, target_size=input_image_size)
print("######### processed_img size", processed_image.shape)
prediction = model.predict(processed_image).tolist()
response = {
'prediction': {
'men': men-prediction[0][0],
'women': women-prediction[0][1]
}
}
return jsonify(response)
# Main
@app.route("/", methods=['GET'])
@cross_origin()
def index():
# Main page
return render_template("index.html")
if __name__ == "__main__":
app.run(debug=True)