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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 tensorflow as tf
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/model.h5'
# Load your trained model
# model = load_model('model\model_densenet_eksperimen5.weights.best.hdf5')
# 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 = tf.keras.models.load_model(
'model/model_densenet_FC1.weights.best.hdf5', compile=False)
print('Model loaded. Check http://127.0.0.1:5000/ or http://localhost:5000/')
def model_predict(img_path, model):
img = image.load_img(img_path, grayscale=False, target_size=(32, 32))
show_img = image.load_img(img_path, grayscale=False, target_size=(32, 32))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = np.array(x, 'float32')
x /= 255
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 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)
print(preds[0])
# x = x.reshape([64, 64]);
# disease_class = ['Pepper__bell___Bacterial_spot',
# 'Pepper__bell___healthy']
disease_class = ['Bacterial',
'Healthy']
a = preds[0]
ind = np.argmax(a)
print('Prediction:', disease_class[ind])
result = disease_class[ind]
return result
return None
if __name__ == '__main__':
# app.run(port=5002, debug=True)
# Serve the app with gevent
#http_server = WSGIServer(('', 5000), app)
# http_server.serve_forever()
app.run()