class PythonPredictor:
def __init__(self, config={}):
""" Requires configuration from cortex.yaml """
# When using s3 bucket to download the model
# s3 = boto3.client("s3")
# s3.download_file(config["bucket"], config["key"], "w2v_limitingfactor_v3.51.model")
self.model = Recommender('models/w2v_limitingfactor_v3.51.model')
pass
def predict(self, payload): # recieves userid, outputs recommendation_id
"""Called once per request. Runs preprocessing of the request payload, inference, and postprocessing of the inference output. Required.
Args:
payload: The parsed JSON request payload.
Returns:
Prediction or a batch of predictions.
"""
self.model.connect_db()
user_id = payload
""" testing with local letterboxd data """
# ratings = pd.read_csv('exported_data/imdb/riley_imdb_ratings.csv', engine='python')
# ratings = pd.read_csv('exported_data/letterboxd/riley/ratings.csv', engine='python')
query = "SELECT EXISTS(SELECT 1 FROM user_letterboxd_ratings where user_id=%s);"
self.model.cursor_dog.execute(query, (user_id,))
boolean = self.model.cursor_dog.fetchall()
if boolean[0][0]==False: # True
self.model.cursor_dog.close()
self.model.connection.close()
return "user_id not found"
self.model.cursor_dog.execute("SELECT date, name, year, letterboxd_uri, rating FROM user_letterboxd_ratings WHERE user_id=%s;", (user_id,))
ratings_sql= self.model.cursor_dog.fetchall()
ratings = pd.DataFrame(ratings_sql, columns = ['Date', 'Name', 'Year', 'Letterboxd URI', 'Rating'])
ratings= ratings.dropna()
# self.model.cursor_dog.execute("SELECT * FROM test_watchlist WHERE user_id=%s;", (user_id,))
# watchlist_sql= self.model.cursor_dog.fetchall()
# watchlist = pd.DataFrame(watchlist_sql, columns = ['Date', 'Name', 'Year', 'Letterboxd URI', 'user_id'])
# watchlist = watchlist.dropna()
# self.model.cursor_dog.execute("SELECT * FROM test_watched WHERE user_id=%s;", (user_id,))
# watched_sql= self.model.cursor_dog.fetchall()
# watched = pd.DataFrame(watched_sql, columns = ['Date', 'Name', 'Year', 'Letterboxd URI', 'user_id'])
# watched = watched.dropna()
# self.model.cursor_dog.execute("SELECT * FROM test_title_basics_small;")
# title_basics_small_sql= self.model.cursor_dog.fetchall()
# id_book = pd.DataFrame(title_basics_small_sql, columns = ['tconst', 'primaryTitle', 'originalTitle', 'startYear'])
# id_book = id_book.dropna()
""" Prepare data """
good_list, bad_list, hist_list, val_list, ratings_dict = prep_data(
ratings, watched_df=None, watchlist_df=None, good_threshold=3, bad_threshold=2)
""" Load JSON into a list (if applicable) """
# payload_jsonified = json.dumps(payload)
# movie_dict = json.loads(payload_jsonified)
# movie_list = list(movie_dict.values())
""" Run prediction with parameters """
predictions = self.model.predict(good_list, bad_list, hist_list, val_list, ratings_dict, n=20, harshness=4, rec_movies=True, scoring=True,)
""" Turn predictions into JSON """
names = ['Title', 'Year', 'IMDB URL', 'Average Rating', 'Number of Votes', 'Similarity Score', 'IMDB ID']
names_lists = {key:[] for key in names}
for x in range(0, len(predictions[0])):
for y in range(0, len(predictions)):
names_lists[names[x]].append(predictions[y][x])
results_dict = [dict(zip(names_lists,t)) for t in zip(*names_lists.values())]
recommendation_json = json.dumps(results_dict)
""" Commit to the database """
string_json = str(recommendation_json)
hash_object = hashlib.md5(string_json.encode('ascii'))
recommendation_id = hash_object.hexdigest()
query = "SELECT EXISTS(SELECT 1 FROM recommendations where recommendation_id=%s);"
self.model.cursor_dog.execute(query, (recommendation_id,))
boolean = self.model.cursor_dog.fetchall()
date = datetime.now()
if boolean[0][0]: # True
self.model.cursor_dog.close()
self.model.connection.close()
return "Already recommended", recommendation_json
else:
query = "INSERT INTO recommendations(user_id, recommendation_id, recommendation_json, date) VALUES (%s, %s, %s, %s);"
self.model.cursor_dog.execute(query, (user_id, recommendation_id, recommendation_json, date))
self.model.connection.commit()
self.model.cursor_dog.close()
self.model.connection.close()
return "Recommendation committed to DB with id:", recommendation_id
