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#!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))

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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):

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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)

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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:

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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...

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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

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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

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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