def beer_profile(id):
beer = model.session.query(model.Beer).get(id)
user_id = current_user.id
ratings = beer.ratings # all rating objects for a particular beer
rating_nums = [] # create list rating_nums
user_rating = None
for r in ratings: # loop through each rating object in all rating objects
if r.rating != 0:
if r.user_id == current_user.id: # does this rating belong to this user
user_rating = r # yes, yes it does
rating_nums.append(r.rating)
avg_rating = float((sum(rating_nums))/(len(rating_nums)))
# # using list comprehension
# ratings = model.session.query(model.Rating).\
# filter_by(beer_id=id).\
# filter(model.Rating.rating != 0).\
# all() # all rating objects for a particular beer
# rating_nums = [ r.rating for r in ratings ]
my_rating = None
for x in ratings:
if x.rating == 0 and x.user_id == current_user.id:
my_rating = x
# only predict if the user hasn't rated yet
if user_rating is None or my_rating:
not_rounded = current_user.predict_rating(beer)
prediction = int(round(not_rounded))
else:
prediction = user_rating.rating
not_rounded = user_rating.rating
# to identify beers with similar prediction coefficients
user_ratings = model.session.query(model.Rating).filter_by(user_id=current_user.id).all()
rated_beers = []
for i in user_ratings:
if i.rating != 0:
rated_beers.append(i.beer_id)
not_rated = model.session.query(model.Beer).filter(~model.Beer.id.in_(rated_beers))
other_predictions = []
for unrated in not_rated:
if unrated.id != beer.id:
other_prediction = current_user.predict_rating(unrated)
difference = fabs(other_prediction - not_rounded)
if difference < 0.5:
mod_prediction = int(round(other_prediction))
beername = unrated.name
beerid = unrated.id
beerimage = unrated.image
other_predictions.append((other_prediction, beername, beerid, mod_prediction, beerimage))
similar_five = itertools.islice(other_predictions, 0, 5)
return render_template("beer_profile.html", beer=beer, userid=user_id,\
average=avg_rating, user_rating=user_rating, my_rating=my_rating, prediction=prediction,\
try_five=similar_five)
def beer_profile(id):
beer = model.session.query(model.Beer).get(id)
user_id = current_user.id
ratings = beer.ratings
rating_nums = []
user_rating = None
for r in ratings:
if r.user_id == current_user.id:
user_rating = r
rating_nums.append(r.rating)
avg_rating = float(sum(rating_nums)) / len(rating_nums)
# prediction code: only predict if the user hasn't rated yet
if user_rating is None:
prediction = int(round(current_user.predict_rating(beer)))
else:
prediction = user_rating.rating
# end prediction
return render_template(
"beer_profile.html",
beer=beer,
user_id=user_id,
average=avg_rating,
user_rating=user_rating,
prediction=prediction,
)
def user_predictions(id):
if id == current_user.id:
user_name = current_user.username
user_id = current_user.id
all_ratings = model.session.query(model.Rating).filter_by(user_id=id).\
order_by(model.Rating.rating.desc()).all()
all_beers = model.session.query(model.Beer).all()
user_ratings = []
for r in all_ratings:
if r.rating != 0:
user_ratings.append(r)
how_many = ((len(all_beers))-(len(user_ratings)))
rated_beers = []
for i in user_ratings:
rated_beers.append(i.beer_id)
not_rated = model.session.query(model.Beer).filter(~model.Beer.id.in_(rated_beers))
predictions = []
for beer in not_rated:
prediction = current_user.predict_rating(beer)
mod_prediction = int(round(prediction))
beername = beer.name
beerid = beer.id
predictions.append((prediction, beername, beerid, mod_prediction))
high_prediction = sorted(predictions, key=operator.itemgetter(0), reverse = True)
top_twenty = itertools.islice(high_prediction, 0, 20)
return render_template("user_predictions.html", user_name = user_name, \
ratings=user_ratings, count = how_many, not_rated = not_rated,\
high_pred = top_twenty, userid=user_id)
def user_profile():
user_name = current_user.username
user_id = current_user.id
all_ratings = model.session.query(model.Rating).filter_by(user_id=current_user.id).\
order_by(model.Rating.rating.desc()).all()
user_ratings = []
for r in all_ratings:
if r.rating != 0:
user_ratings.append(r)
how_many = len(user_ratings)
high_rated = itertools.islice(user_ratings, 0, 5)
rated_beers = []
for i in user_ratings:
rated_beers.append(i.beer_id)
not_rated = model.session.query(model.Beer).filter(~model.Beer.id.in_(rated_beers))
predictions = []
for beer in not_rated:
prediction = current_user.predict_rating(beer)
mod_prediction = int(round(prediction))
beername = beer.name
beerid = beer.id
beerimage = beer.image
predictions.append((prediction, beername, beerid, mod_prediction, beerimage))
high_prediction = sorted(predictions, key=operator.itemgetter(0), reverse = True)
best_five = itertools.islice(high_prediction, 0, 5)
return render_template("user_profile.html", high_rated = high_rated, \
count = how_many, user_name = user_name, userid=user_id,\
high_pred = best_five)
def user_queue():
user_name = current_user.username
user_id = current_user.id
user_queue = model.session.query(model.Rating).filter_by(user_id=current_user.id, rating=0).all()
how_many = len(user_queue)
in_queue = []
for i in user_queue:
in_queue.append(i.beer_id)
beer_queue = model.session.query(model.Beer).filter(model.Beer.id.in_(in_queue))
predictions = []
for beer in beer_queue:
prediction = current_user.predict_rating(beer)
mod_prediction = int(round(prediction))
beername = beer.name
beerid = beer.id
beerimage = beer.image
predictions.append((prediction, beername, beerid, mod_prediction, beerimage))
high_queue = sorted(predictions, key=operator.itemgetter(0), reverse = True)
all_ratings = model.session.query(model.Rating).filter_by(user_id=current_user.id).\
order_by(model.Rating.rating.desc()).all()
rated_beers = []
for i in all_ratings:
rated_beers.append(i.beer_id)
not_queue = model.session.query(model.Beer).filter(~model.Beer.id.in_(rated_beers))
other_predictions = []
for beer in not_queue:
prediction = current_user.predict_rating(beer)
mod_prediction = int(round(prediction))
beername = beer.name
beerid = beer.id
beerimage = beer.image
other_predictions.append((prediction, beername, beerid, mod_prediction, beerimage))
high_prediction = sorted(other_predictions, key=operator.itemgetter(0), reverse = True)
best_five = itertools.islice(high_prediction, 0, 5)
print best_five
return render_template("user_queue.html", user_name = user_name, \
queue=high_queue, high_pred = best_five, count = how_many, userid=user_id)
def beer_profile(id):
beer = model.session.query(model.Beer).get(id)
user_id = current_user.id
ratings = beer.ratings
rating_nums = []
user_rating = None
for r in ratings:
if r.user_id == current_user.id:
user_rating = r
rating_nums.append(r.rating)
avg_rating = float(sum(rating_nums)) / len(rating_nums)
# prediction code: only predict if the user hasn't rated yet
if user_rating is None:
prediction = int(round(current_user.predict_rating(beer)))
else:
prediction = user_rating.rating
# end prediction
return render_template("beer_profile.html", beer=beer, user_id=user_id,\
average=avg_rating, user_rating=user_rating, prediction=prediction)
def home_display():
username = current_user.username
userid = current_user.id
# TO SHOW HIGHEST AVERAGE RATINGS, across all beers whether user has rated or not
# AND MOST RATED / POPULAR BEERS
all_beers = model.session.query(model.Beer).all()
all_ratings = [] #becomes a list of lists. each inner list has all ratings for particular beer
for beer in all_beers:
list_ratings = []
for r in beer.ratings:
if r.rating != 0:
list_ratings.append(r)
all_ratings.append(list_ratings)
id_avg_ln = [] #stands for beer id, average, length
for r in all_ratings:
how_many = len(r)
rating_nums = []
for i in r:
beerid = i.beer_id
rating = i.rating
rating_nums.append(rating)
average = (float(sum(rating_nums))/len(rating_nums))
id_avg_ln.append((beerid, average, how_many))
beernames = []
for i in id_avg_ln:
beer = model.session.query(model.Beer).filter_by(id=i[0]).one()
beernames.append((beer.name, beer.image))
id_avg_ln_nm_d = dict(zip(id_avg_ln, beernames))
id_avg_ln_nm = [] #stands for beer id, average, length, name
for key in id_avg_ln_nm_d:
tuple1 = key[0]
tuple2 = key[1]
tuple3 = key[2]
tuple4 = id_avg_ln_nm_d[key]
id_avg_ln_nm.append((tuple1, tuple2, tuple3, tuple4))
sortby_avg = sorted(id_avg_ln_nm, key=operator.itemgetter(1), reverse = True)
high_five = itertools.islice(sortby_avg, 0, 5) #lol, high five!
sortby_len = sorted(id_avg_ln_nm, key=operator.itemgetter(2), reverse = True)
popular_five = itertools.islice(sortby_len, 0, 5) #popular, like the plastics... or your mom
# TO SHOW HIGHEST PREDICTION MATCHES
user_ratings = model.session.query(model.Rating).filter_by(user_id=userid).all()
rated_beers = []
for i in user_ratings:
if i.rating != 0:
rated_beers.append(i.beer_id)
not_rated = model.session.query(model.Beer).filter(~model.Beer.id.in_(rated_beers))
predictions = []
for beer in not_rated:
prediction = current_user.predict_rating(beer)
mod_prediction = int(round(prediction))
beername = beer.name
beerid = beer.id
beerimage = beer.image
predictions.append((prediction, beername, beerid, mod_prediction, beerimage))
high_prediction = sorted(predictions, key=operator.itemgetter(0), reverse = True)
best_five = itertools.islice(high_prediction, 0, 5)
# whoa, i can't believe i wrote all that
return render_template("home.html", user_name=username, userid=userid,\
high_averages=high_five, most_rated=popular_five, high_pred = best_five)