Mayukh Bhattacharyya mayukh18

Setting up flask app in heroku with a database

The below article will cover the intricacies of setting up databases and heroku in respect to a flask app. This is more like a memo and will have out of sequence instructions or solutions to errors so read thoroughly.

Setting up a database

You'll need the packages

Movies Recommendation:

MovieLens - Movie Recommendation Data Sets http://www.grouplens.org/node/73
Yahoo! - Movie, Music, and Images Ratings Data Sets http://webscope.sandbox.yahoo.com/catalog.php?datatype=r
Jester - Movie Ratings Data Sets (Collaborative Filtering Dataset) http://www.ieor.berkeley.edu/~goldberg/jester-data/
Cornell University - Movie-review data for use in sentiment-analysis experiments http://www.cs.cornell.edu/people/pabo/movie-review-data/

Music Recommendation:

Last.fm - Music Recommendation Data Sets http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/index.html

	#!pip install faiss-gpu
	import faiss
	faiss_index = faiss.IndexFlatL2(1000) # build the index

	# storing the image representations
	im_indices = []
	with torch.no_grad():
	for f in glob.glob(os.path.join(PATH_TRAIN, '/')):
	im = Image.open(f)
	im = im.resize((224,224))

	class TripletLoss(nn.Module):
	def __init__(self, margin=1.0):
	super(TripletLoss, self).__init__()
	self.margin = margin

	def calc_euclidean(self, x1, x2):
	return (x1 - x2).pow(2).sum(1)

	# Distances in embedding space is calculated in euclidean
	def forward(self, anchor, positive, negative):

	class TripletData(Dataset):
	def __init__(self, path, transforms, split="train"):
	self.path = path
	self.split = split # train or valid
	self.cats = 102 # number of categories
	self.transforms = transforms

	def __getitem__(self, idx):
	# our positive class for the triplet
	idx = str(idx%self.cats + 1)

	import keras.backend as K
	from keras.legacy import interfaces
	from keras.optimizers import Optimizer


	class AdamAccumulate(Optimizer):

	def __init__(self, lr=0.001, beta_1=0.9, beta_2=0.999,
	epsilon=None, decay=0., amsgrad=False, accum_iters=1, **kwargs):
	if accum_iters < 1:

	model.zero_grad() # Reset gradients tensors
	for i, (inputs, labels) in enumerate(training_set):
	predictions = model(inputs) # Forward pass
	loss = loss_function(predictions, labels) # Compute loss function
	loss = loss / accumulation_steps # Normalize our loss (if averaged)
	loss.backward() # Backward pass
	if (i+1) % accumulation_steps == 0: # Wait for several backward steps
	optimizer.step() # Now we can do an optimizer step
	model.zero_grad() # Reset gradients tensors
	if (i+1) % evaluation_steps == 0: # Evaluate the model when we...

	import pandas as pd
	import numpy as np


	data = pd.read_csv('movielens100k.csv')
	data['userId'] = data['userId'].astype('string')
	data['movieId'] = data['movieId'].astype('string')


	users = list(set(data['userId'])) #list of all users