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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 sys
import os
import glob
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/2002epoch.model'
# 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.resnet50 import ResNet50
#model = ResNet50(weights='imagenet')
#model.save('')
print('Model loaded. Check http://127.0.0.1:5000/')
def model_predict(img_path, model):
img = image.load_img(img_path, target_size=(200, 200))
# 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)
# preds = np.argmax(preds)
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']
# 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)
# 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
# pred_class = int(pred_class)
classes ={0: "ALL", 1: "AML", 2: "CLL", 3: "CML"}
# return str("The image is classified as: "+str(classes[pred_class]))
return str(pred_class)
return None
if __name__ == '__main__':
app.run(debug=True)