def addmonitoritem(user_id):
form = AddMonitorItemForm()
item = Monitor()
if form.validate_on_submit():
item_id = request.form.get("item_id")
if not len(strip(item_id)):
flash("商品ID为必填项")
return redirect(url_for("addmonitoritem", user_id=user_id))
user_price = request.form.get("user_price")
if not len(strip(user_price)):
flash("The content is necessray!")
return redirect(url_for("addmonitoritem", user_id=user_id))
mall_id = request.form.get("mall_id")
if not len(strip(item_id)):
flash("The content is necessray!")
return redirect(url_for("addmonitoritem", user_id=user_id))
item.item_id = item_id
item.user_price = user_price
item.mall_id = mall_id
item.add_date = datetime.datetime.now()
item.user_id = user_id
item.status = True
try:
item_exist = Monitor.query.filter(
Monitor.item_id == item_id).first()
# print(item_exist)
if item_exist is not None:
flash("该商品已经在监控列表中")
return redirect(url_for("users", user_id=user_id))
except:
flash("数据库查询商品错误，请重试")
return redirect(url_for("addmonitoritem", user_id=user_id))
item_name, item_price = additemcrawl.additemcrawl(
item_id, user_id, mall_id)
print(type(item_name), type(item_price), item_name)
if type(item_name) == str:
flash("该商品不存在，请输入正确的商品ID")
return redirect(url_for("addmonitoritem", user_id=user_id))
item.item_name = item_name
item.item_price = item_price
try:
db.session.add(item)
db.session.commit()
except:
flash("写入数据库错误")
return redirect(url_for("addmonitoritem", user_id=user_id))
flash("添加商品成功")
return redirect(url_for("users", user_id=user_id))
return render_template("addmonitoritem.html", form=form)
def init_add_monitors():
global session, SERVER_MODE, engine
# Lets assign them all
accounts = session.query(Account).all()
channels = session.query(Channel).all()
account_index = 0
channel_count = 0
for channel in channels:
if account_index in accounts:
account = accounts[account_index]
logging.info(
'{}: Adding monitoring to channel {} with account_id {} to the database'
.format(sys._getframe().f_code.co_name, channel.channel_name,
account.account_id))
session.add(
Monitor(channel_id=channel.id,
account_id=account.account_id,
monitor_tcreate=datetime.now(),
monitor_tmodified=datetime.now()))
channel_count += 1
if channel_count > 500:
account_index += 1
channel_count = 0
session.commit()
def index():
if request.method == 'GET':
monitors = Monitor.query.order_by(desc(Monitor.id)).limit(50)
return render_template("admin/monitor.html", monitors=monitors)
elif request.method == 'POST':
ipv4 = request.form['ipv4']
monitor = Monitor(ipv4)
db_session.add(monitor)
db_session.commit()
return "OK"
def monitor_collect(request):
req = request
if req.POST:
ip = req.POST.get('ip')
time = req.POST.get('time')
game = req.POST.get('game')
app = req.POST.get('app')
pid = req.POST.get('pid')
useage = req.POST.get('useage')
monitor = Monitor()
monitor.ip = ip
monitor.time = time
monitor.game = game
monitor.app = app
monitor.pid = pid
monitor.useage = useage
monitor.save()
return HttpResponse('OK')
else:
return HttpResponse('no post data')
def monitor_collect(request):
req = request
if req.POST:
ip = req.POST.get('ip')
time = req.POST.get('time')
game = req.POST.get('game')
app = req.POST.get('app')
pid = req.POST.get('pid')
useage = req.POST.get('useage')
monitor = Monitor()
monitor.ip = ip
monitor.time = time
monitor.game = game
monitor.app = app
monitor.pid = pid
monitor.useage = useage
monitor.save()
return HttpResponse('OK')
else:
return HttpResponse('no post data')
# Compile model
# model.compile(loss=crossentropy_mask,
# metrics=["accuracy", f1_mean], optimizer=optimizer)
model.compile(optimizer=optimizer,
loss=losses,
loss_weights=lossWeights,
metrics=custom_metrics)
# Train model on dataset
history = model.fit_generator(
generator=training_generator,
validation_data=validation_generator,
epochs=params.num_epochs,
callbacks=[
Monitor(validation=validation_generator,
patience=15,
model_dir=model_k,
classes=params.classes)
])
model.save(file_output)
# save training history
filename = os.path.join(model_k, 'history_plot_acc.png')
Results(history.history['cl_output_f_acc'],
history.history['val_cl_output_f_acc'],
history.history['cl_output_loss'],
history.history['val_cl_output_loss'],
filename).show_result()
filename = os.path.join(model_k, 'history_plot_f1batch.png')
Results(history.history['mean_f1'],
history.history['val_cl_output_f_acc'],