db = firestore.client()

doc_ref = db.collection(u'ratings').where(u'book', u'==', True).stream()

ref = db.collection_group(u'review')\

.where(u'reviewed', u'==', True).order_by('user_id')

docs = ref.stream()

for doc in docs:

data1.append(doc.to_dict())

# print(type(data), data)

df = pd.DataFrame(data1)

df1 = df[['user_id', 'book_id', 'rating']]

# print(df1)

ratingss = pd.read_csv('dataset/ratings.csv',

usecols=['user_id', 'book_id', 'rating'])

# print(ratings.head(5))

ratings = pd.concat([ratingss, df1])

# print(new)

ratings['user_id'] = ratings['user_id'].apply(str)

n_users = ratings.user_id.unique().shape[0]

n_books = ratings.book_id.unique().shape[0]

print('Number of users = ' + str(n_users) +

' | Number of books = ' + str(n_books))

Ratings = ratings.pivot(

index='user_id', columns='book_id', values='rating').fillna(0)

# Ratings.head()

R = Ratings.to_numpy()

user_ratings_mean = np.mean(R, axis=1)

Ratings_demeaned = R - user_ratings_mean.reshape(-1, 1)

sparsity = round(1.0 - len(ratings) / float(n_users * n_books), 3)

# print ('The sparsity level of MovieLens1M dataset is ' + str(sparsity * 100) + '%')

U, sigma, Vt = svds(Ratings_demeaned, k=50)

sigma = np.diag(sigma)

# sigma

all_user_predicted_ratings = np.dot(

np.dot(U, sigma), Vt) + user_ratings_mean.reshape(-1, 1)

preds = pd.DataFrame(all_user_predicted_ratings, columns=Ratings.columns)

reader = Reader()

# Load ratings dataset with Dataset library

data = Dataset.load_from_df(

ratings[['user_id', 'book_id', 'rating']], reader)

# Split the dataset for 5-fold evaluation

kf = KFold(n_splits=5)

svd = SVD()

for trainset, testset in kf.split(data):

# train and test algorithm.

svd.fit(trainset)

predictions = svd.test(testset)

# Compute and print Root Mean Squared Error

accuracy.rmse(predictions, verbose=True)

trainset = data.build_full_trainset()

svd.fit(trainset)

print('Data Trained Successfully')

with open('model_pickle', 'wb') as f: