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app.py
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app.py
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# # # # # # # # # # # # # # #
# Author: Mustafa Mert Tunalı
# ---------------------------
# ---------------------------
# Deep Learning Training GUI - Backend Side
# ---------------------------
# ---------------------------
# # # # # # # # # # # # # # #
import numpy as np
from multiprocessing import Process
import threading
from flask import Flask, request, jsonify, render_template
from dltgui.dlgui import dl_gui
import tensorflow as tf
# Set Flask
app = Flask(__name__, static_folder="static/")
@app.route('/')
def home():
return render_template('index.html', title ="Version 1.0.2")
@app.route('/contact')
def contact():
return render_template('contact.html')
@app.route('/training')
def training():
return render_template('training.html')
@app.route('/terminal-object-detection',methods = ['POST'])
def terminal_object_detection():
if request.method == 'POST':
result = request.form
dataset = result['dataset']
type_of_label = result['type_of_label']
split_dataset = result['split_dataset']
project_name = result['project_name']
pre_trained_model = result['Pre-trained Model']
number_of_classes = result['noc']
batch_size = result['batch_size']
epoch = result['epoch']
return render_template("terminal-object-detection.html",result = result)
@app.route('/terminal',methods = ['POST'])
def terminal():
if request.method == 'POST':
'''Read the values from HTML file and set the values for training.'''
result = request.form
dataset = result['dataset']
split_dataset = result['split_dataset']
project_name = result['project_name']
pre_trained_model = result['Pre-trained Model']
cpu_gpu = result['CPU/GPU']
number_of_classes = result['noc']
batch_size = result['batch_size']
epoch = result['epoch']
activation_function = result['activation_function']
flip = result['flip']
rotation = result['rotation']
zoom = result['zoom']
fine_tuning = result['fine_tuning']
fine_tune_epochs = result['fine_tune_epochs']
gui = dl_gui(dataset=dataset,
project_name = str(project_name),
split_dataset = float(split_dataset),
pre_trained_model = pre_trained_model,
cpu_gpu = cpu_gpu,
number_of_classes = int(number_of_classes),
batch_size = int(batch_size),
epoch = int(epoch),
activation_function = activation_function,
fine_tune_epochs = int(fine_tune_epochs))
if(flip == "True" or rotation == "True" or zoom == "True"):
samples = result['samples']
gui.load_dataset(imgaugmentation = True, flip = flip, rotation = rotation, zoom = zoom, samples = int(samples))
else:
gui.load_dataset()
thread_gui = threading.Thread(target= gui.train, args=(fine_tuning,)).start()
return render_template("terminal.html",result = result, thread_gui = thread_gui)
@app.route('/predict')
def predict():
return render_template('predict.html')
@app.route('/result', methods = ['POST'])
def result():
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.compat.v1.Session(config=config)
if request.method == 'POST':
result = request.form
dataset = result['dataset']
model_dir= result['model']
img = result['img']
gui = dl_gui(project_name = "Test", dataset=dataset)
predicted_class, max_pred, show_heatmap, img_name = gui.predict(img, model_dir)
return render_template('result.html', result = result, img = img, max_pred = max_pred, predicted_class = predicted_class, show_heatmap = show_heatmap, img_name = img_name, mimetype="text/event-stream")
@app.route('/test', methods = ['POST'])
def test():
if request.method == 'POST':
'''Read the values from HTML file and set the values for training.'''
result = request.form
dataset = result['dataset']
split_dataset = result['split_dataset']
project_name = result['project_name']
pre_trained_model = result['Pre-trained Model']
cpu_gpu = result['CPU/GPU']
number_of_classes = result['noc']
batch_size = result['batch_size']
epoch = result['epoch']
activation_function = result['activation_function']
flip = result['flip']
rotation = result['rotation']
zoom = result['zoom']
print("Flip: ", flip, rotation,zoom)
return "Testing page - look at the conda terminal for values.."
if __name__ == "__main__":
app.run(debug=True)