def borrow(product_id, lender_id):
user_id = session.get("user_id", None)
form = BorrowForm(product_id=product_id,
lender_id=lender_id,
user_id=user_id)
if form.validate_on_submit():
borrower_id = user_id
lender_id = lender_id
product_id = product_id
date_wanted = datetime.datetime.strptime(form.start_date.data,
"%d-%b-%Y")
date_returned_est = datetime.datetime.strptime(form.end_date.data,
"%d-%b-%Y")
#create query
borrow_request = model.History(borrower_id=borrower_id,
lender_id=lender_id,
product_id=product_id,
date_wanted=date_wanted,
date_returned_est=date_returned_est)
#Add the object to a session and commit it.
model.session.add(borrow_request)
model.session.commit()
return redirect("/dashboard")
else:
flash("didn't work")
library_item = model.session.query(
model.Library).filter_by(product_id=product_id).first()
print library_item.id
return render_template("borrow.html",
library_item=library_item,
user_id=user_id,
lender_id=lender_id,
form=form)
def path_transmit(path, fileData):
if not session.get("user_id") and \
((fileData.DownloadLimit is not None and fileData.Downloaded >= fileData.DownloadLimit) or \
(fileData.ExpiresIn is not None and time.time() > fileData.Uploaded + fileData.ExpiresIn)):
if fileData.HideAfterLimitExceeded:
return render_template("no_such_file.html")
return render_template("limit_exceeded.html")
fileData.Downloaded = model.Path.Downloaded + 1
geoipISOCode = "-"
if app.config.get("ENABLE_GEOIP", False):
try:
geoipISOCode = addon.geoipGetCountry(request.remote_addr)
except:
import traceback
print traceback.format_exc()
db.session.add(model.History(path, request.remote_addr, int(time.time()),
request.user_agent.string, request.referrer, geoipISOCode))
db.session.commit()
return file.transmit(fileData.ActualName, fileData.File.StoredPath)
return response
# Custom filter
app.jinja_env.filters["usd"] = usd
# Configure session to use filesystem (instead of signed cookies)
app.config["SESSION_FILE_DIR"] = mkdtemp()
app.config["SESSION_PERMANENT"] = False
app.config["SESSION_TYPE"] = "filesystem"
Session(app)
# Configure CS50 Library to use SQLite database
db = SQL("sqlite:///finance.db")
Utable = model.User()
Stable = model.Stock()
Htable = model.History()
@app.route("/")
@login_required
def index():
"""Show portfolio of stocks"""
# Get some values
user_id = session["user_id"]
porto = Stable.get_portfolio(user_id)
total = 0
total_stocks = 0
# Makes json for portfolio
for stock in porto:
sym = stock['stock']
x_train = x_train.reshape(x_train.shape[0],48,48,1)
x_val = x_val.reshape(x_val.shape[0],48,48,1)
datagen = ImageDataGenerator(
featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
rotation_range=0,
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True,
vertical_flip=True)
datagen.fit(x_train)
emotion_classifier = model.vgg()
emotion_classifier.summary()
history = model.History()
tbCallBack = TensorBoard(log_dir=os.path.join('./','logs'), write_graph=True, write_images=False)
emotion_classifier.fit_generator(datagen.flow(x_train, y_train, batch_size=100),
steps_per_epoch=len(x_train) / 100, epochs=80, validation_data=(x_val,y_val), callbacks=[history, tbCallBack])
dump_history('./',history)
emotion_classifier.save('model.h5')
score = emotion_classifier.evaluate(x_train,y_train)
print ('Train Acc:', score[1])