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app.py
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app.py
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import os
import sys
# Flask
from flask import Flask, redirect, url_for, request, render_template, Response, jsonify, redirect
from werkzeug.utils import secure_filename
from gevent.pywsgi import WSGIServer
# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.applications.imagenet_utils import preprocess_input, decode_predictions
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
# Some utilites
import numpy as np
from util import base64_to_pil
# Declare a flask app
app = Flask(__name__)
# You can use pretrained model from Keras
# Check https://keras.io/applications/
# or https://www.tensorflow.org/api_docs/python/tf/keras/applications
#from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2
#model = MobileNetV2(weights='imagenet')
# Model saved with Keras model.save()
MODEL_PATH = 'models/leukoRight.h5'
# Load your own trained model
model = load_model(MODEL_PATH)
model.make_predict_function() # Necessary
print('Model loaded. Start serving...')
print('Model loaded. Check http://127.0.0.1:5000/')
#def model_predict(img, model):
# img = img.resize((512, 512))
#
# # 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='tf')
#
# 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 predict():
if request.method == 'POST':
image_size = (512, 512)
img = base64_to_pil(request.json)
#img.save("./uploads/image.png")
img_array = keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, 0) # Create batch axis
predictions = model.predict(img_array)
theWBC = str(np.argmax(predictions))
overall = np.sum(predictions)
theValue = str(np.amax(predictions))
wbc_lookup = dict([
('0','artifact'),
('1','band neutrophil'),
('2','basophil'),
('3','bursted cell'),
('4','eosinophil'),
('5','large lympthocyte'),
('6','metamyelocyte'),
('7','monocyte'),
('8','neutrophil'),
('9','nRBC'),
('10','small lymphocyte')
])
wbcName = str(wbc_lookup[theWBC])
return jsonify(result=wbcName, probability=theValue)
return None
if __name__ == '__main__':
# app.run(port=5002, threaded=False)
# Serve the app with gevent
http_server = WSGIServer(('0.0.0.0', 5000), app)
http_server.serve_forever()