def refresh_user_level(current_user_id):
with app.app_context():
db.init_app(app)
db.create_all()
# get all articles in the curated table for this user
user_curated_article_levels = UserSortedContent.query.filter_by(user_id=current_user_id).all()
# calculate standard deviaion of the user level
sqd_deviations = 0
for entry in user_curated_article_levels:
level = (Content.query.filter_by(id=entry.content_id).first()).level
sqd_deviations += level ** 2
if len(user_curated_article_levels) > 0:
std_deviation = (sqd_deviations / len(user_curated_article_levels)) ** 0.5
else:
std_deviation = 0
# add user skill to the user_lang_skill table
user = UserLangSkill.query.filter_by(user_id=current_user_id).first()
if std_deviation > 0:
# user = UserLangSkill(user_id=current_user_id, language_id=815, skill=std_deviation)
user.skill = std_deviation
db.session.add(user)
db.session.commit()
return 0
else:
return -1
def refresh_content_level(current_user_id):
with app.app_context():
db.init_app(app)
db.create_all()
# clear user_sorted_content table
user_content = UserSortedContent.query.filter_by(user_id=current_user_id).all()
for item in user_content:
db.session.delete(item)
db.session.commit()
#only curate for all articles
content_text = Content.query.all()
# pickled_classifier = User.query.filter_by(id=current_user_id).first().classifier
# g.classifier = pickle.loads(pickled_classifier)
# pickled_feature_set = User.query.filter_by(id=current_user_id).first().feature_set
# g.feature_set = pickle.loads(pickled_feature_set)
classifier = get_classifier(current_user_id)
feature_set = get_feature_set(current_user_id)
# classify and create as a new entry
for content_item in content_text:
result = classify(content_item.body, classifier, feature_set)
# print(result)
# only the suitable entries are added
if result == 0:
article_entry = UserSortedContent(user_id=current_user_id, content_id=content_item.id, sortedSkill=result)
db.session.add(article_entry)
# commit the change
db.session.commit()
def get_feature_set(user_id):
# Retrieve classifier from db
with app.app_context():
db.init_app(app)
db.create_all()
pickled_feature_set = User.query.filter_by(
id=user_id).first().feature_set
feature_set = pickle.loads(pickled_feature_set)
return feature_set
def get_classifier(user_id):
# Retrieve classifier from db
with app.app_context():
db.init_app(app)
db.create_all()
pickled_classifier = User.query.filter_by(
id=user_id).first().classifier
classifier = pickle.loads(pickled_classifier)
return classifier
def delete_entries():
with app.app_context():
db.init_app(app)
db.create_all()
user = User.query.filter_by(username='******').first()
if user:
user_id = user.id
# Query DB for user Progress entries. While true, delete.
entry = Progress.query.filter_by(user_id=user_id).first()
while entry:
db.session.delete(entry)
entry = Progress.query.filter_by(user_id=user_id).first()
db.session.delete(user)
db.session.commit()
def create_entries():
with app.app_context():
db.init_app(app)
db.create_all()
# Create test user
password = global_bcrypt.generate_password_hash('password').decode(
'utf-8')
user = User(username='******',
email='*****@*****.**',
password=password)
db.session.add(user)
db.session.commit()
# Get new user's id
user = User.query.filter_by(username='******').first()
user_id = user.id
print("New user's id: " + str(user_id))
''' select everything from the content '''
articles = Content.query.all()
''' Create 500 progress entries for the test user from the
first 500 entries in the Content table. Rating -1 == too easy,
0 == just right, 1 == too difficult '''
for i in range(ARTICLE_CT):
content = articles[i]
score = content.level
# Add appropriate Progress entry
if score >= 0 and score < EASY:
entry = Progress(user_id=user_id,
content_id=content.id,
rating=-1,
read_ct=1)
db.session.add(entry)
elif score >= 5 and score < JUST_RIGHT:
entry = Progress(user_id=user_id,
content_id=content.id,
rating=0,
read_ct=1)
db.session.add(entry)
else:
entry = Progress(user_id=user_id,
content_id=content.id,
rating=1,
read_ct=1)
db.session.add(entry)
# Commit transactions to DB
db.session.commit()
def assign_levels():
with app.app_context():
db.init_app(app)
db.create_all()
''' select everything from the content
loop through it to calculate the score
then add the it back to the database '''
articles = Content.query.all()
#for each article
for article in articles:
text = str(article.body)
score = cal_diff(text)
article.level = score
# print(article.id, score)
db.session.add(article)
# Commit the transactions
db.session.commit()
def create_classifier(user_id, word_ct):
# Load data
user_ratings = get_ratings(user_id)
#print(user_ratings)
all_words = get_words(user_ratings)
# Convert all_words to dictionary with word frequency denoted
all_words = nltk.FreqDist(all_words)
# Create a list of n most frequent words where n = word_ct
feature_words = list(all_words.keys())[:word_ct]
# Create featuresets
featuresets = create_featuresets(feature_words, user_ratings)
# Train Naive-Bayes Algorithm
classifier = nltk.NaiveBayesClassifier.train(featuresets)
# Pickle the feature set for later use
saved_dictionary = pickle.dumps(feature_words)
# Pickle classifier for later use
saved_classifier = pickle.dumps(classifier)
# Save feature set and classifier in user's row in DB
with app.app_context():
db.init_app(app)
db.create_all()
# Get user
user = User.query.filter_by(id=user_id).first()
user.feature_set = saved_dictionary
user.classifier = saved_classifier
# Commit updates
db.session.commit()
#return [classifier, feature_words]
return
def get_ratings(user_id):
with app.app_context():
db.init_app(app)
db.create_all()
''' Grab all user's ratings from the database '''
user_entries = Progress.query.filter_by(user_id=user_id).all()
''' Tokenize each piece of content's words and
create a tuple entry [[word_list], rating]
***NOTE: need to add punctuation stripping, accepting
alpha chars only, etc. to refine features '''
tokenized_entries = []
for entry in user_entries:
''' Get binary first '''
tokenized_words_b = Content.query.filter_by(
id=entry.content_id).first().body.split()
tokenized_words = []
''' Decode from binary to utf8 '''
for word in tokenized_words_b:
word = word.decode('utf8')
word = word.strip('.,!?-*();:\'\"[]{}\\')
tokenized_words.append(word)
tokenized_entries.append([tokenized_words, entry.rating])
return tokenized_entries
def init_db():
"""Clear existing data and create new tables."""
db = get_db()
db.create_all()
from cassiopeia.models.models import db, Country, Language from flask import Flask from flask_sqlalchemy import SQLAlchemy # Custom App to load to cassiopeia_prod database, neeed utf8mb4 for emoji support app = Flask(__name__) app.config['SQLALCHEMY_DATABASE_URI'] = 'mysql+mysqlconnector://[email protected]/cassiopeia_prod?charset=utf8mb4' db.init_app(app) f = open('all.json', 'r') # From https://restcountries.eu/#api-endpoints-language buf = f.read() obj_dict = json.loads(buf) with app.app_context(): with db.session.no_autoflush: db.create_all() language_hash = set() for c in obj_dict: country = Country(name=c['name'], alpha2code=c['alpha2Code'], alpha3code=c['alpha3Code'], flag_image=c['flag']) for l in c['languages']: new_language = Language.query.filter(Language.name==l['name']).first() if new_language is None: new_language = Language(name=l['name'], iso639_1=l['iso639_1'], iso639_2=l['iso639_2']) db.session.add(new_language) country.languages.append(new_language) for item in db.session: print(item)
