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app.py
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app.py
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from flask import Flask, render_template, request, redirect, url_for
from flask import jsonify
from keras.models import load_model
from matplotlib import pyplot
from PIL import Image
from pylab import *
import os
import numpy as np
# import cv2
import json
import scipy.misc as image
from werkzeug.utils import secure_filename
from PIL import Image
import base64
import re
from io import StringIO
from io import BytesIO
import random
import tensorflow as tf
UPLOAD_FOLDER = '/uploads'
ALLOWED_EXTENSIONS = set(['txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif'])
application = Flask(__name__)
application.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
@application.route('/')
def index():
return render_template('index.html')
@application.route('/video')
def video():
return render_template('video.html')
@application.route('/dropdown')
def dropdown():
return render_template('dropdown.html')
@application.route('/navbar')
def navbar():
return render_template('navbar.html')
@application.route('/button')
def button():
return render_template('button.html')
@application.route('/thankyou')
def thankyou():
return render_template('thankyou.html')
@application.route('/predict', methods=['GET', 'POST'])
def predict():
model = load_model('./model/my_model_flow.h5')
im = image.imread('/Users/savinaynarendra/Downloads/test1.png')
im = image.imresize(im, (150,150))
img = np.expand_dims(im, axis=0)
def allowed_file(filename):
return '.' in filename and \
filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
@application.route('/save', methods=['POST'])
def get_image():
model = load_model('./model/latest_model.h5')
image_b64 = request.values['imageBase64']
clss = request.values['class']
random_number = random.randint(0, 100000)
file_name = clss + str(random_number)
image_data = re.sub('^data:image/.+;base64,', '', image_b64)
# .decode('base64')
image_PIL = Image.open(BytesIO(base64.b64decode(image_data)))
image_np = np.array(image_PIL)
# print image_np
im = image.imresize(image_np, (150,150))
img = np.expand_dims(im, axis=0)
arr = model.predict(img, batch_size=16, verbose=0)
arr = arr.tolist()
return jsonify(arr)
# return "Success"
if __name__ == "__main__":
# model = load_model('./model/my_model_flow_trained.h5')
application.run(debug=False, host='0.0.0.0')