def home():
if "recommend" in request.args:
try:
title = request.args["recommend"]
rec = ContentBased()
did_you_mean = False
df = rec.recommend(title,
DEFAULT_LIMIT,
full_search=True,
keywords_and_desc=False,
critics=False)
poster_paths = get_poster_paths(df["id"].tolist(),
df["original_title"].tolist())
if rec.changed_title != title and rec.changed_title != str():
did_you_mean = True
else:
rec.changed_title = title
rec_title_meta = get_meta(rec.changed_title, None)
rec_id = rec_title_meta[0]["id"]
return render_template('recommendations.html',
titles=df["original_title"].tolist(),
images=poster_paths,
votes=df["vote_average"].tolist(),
m_id=df["id"].tolist(),
rec_title=rec.changed_title,
rec_id=rec_id,
did_you_mean=did_you_mean)
except:
abort(404)
elif "genres" in request.args:
genre = request.args["genres"]
if genre == "All":
genre = None
offset = int(request.args["offset"])
gen_rec = Recommendation()
gen_rec.filter_genres()
df = gen_rec.top_movies(gen_rec.md,
percentile=0.85,
limit=DEFAULT_LIMIT,
offset=offset,
genre=genre)
poster_paths = get_poster_paths(df["id"].tolist(),
df["original_title"].tolist())
return render_template('recommendations.html',
titles=df["original_title"].tolist(),
images=poster_paths,
votes=df["vote_average"].tolist(),
m_id=df["id"].tolist(),
rec_title=request.args["genres"],
offset=offset,
next_offset=offset + DEFAULT_LIMIT,
prev_offset=offset - DEFAULT_LIMIT,
rec_id=None,
did_you_mean=None)
else:
return render_template('homepage.html')
def recommend(self,
title,
limit,
critics=False,
full_search=False,
use_pickle=True,
keywords_and_desc=False):
"""
param: title - movie title (as in TMDB dataset)
limit - no. of movies to display
critics - True - will display critically acclaimed movies
False - will not sort movies on basis of their imdb rankings
(DEFAULT - False)
full_search - True - will search using cast, crew, keywords
and genre as metadata
False - will search using overview and tagline
as metadata
(DEFAULT - False)
use_pickle - True - will use pickled results
False - will compute the results from scratch
(DEFAULT - True)
keywords_and_desc - True - will merge results of keywords
and description
False - will not merge results of keywords
and description
return: pandas DataFrame object with attributes -
original_title, id, vote_average, vote_count, popularity, release_date
"""
rec = Recommendation()
rec.filter_genres()
title_index = self.verify_title(rec.md, title)
if keywords_and_desc:
if isfile(PATH_PICKLE_KEYWORDS) and isfile(
PATH_PICKLE_DESC) and use_pickle:
df_keywords = pd.read_pickle(PATH_PICKLE_KEYWORDS)
df_desc = pd.read_pickle(PATH_PICKLE_DESC)
rec_matrix_keywords = self.countvectorize(df_keywords)
rec_matrix_desc = self.tfidf(df_desc)
rec_matrix = rec_matrix_keywords + rec_matrix_desc
df = df_keywords
elif full_search:
if isfile(PATH_PICKLE_KEYWORDS) and use_pickle:
df = pd.read_pickle(PATH_PICKLE_KEYWORDS)
else:
df = self.make_keywords(rec.md)
df.to_pickle(PATH_PICKLE_KEYWORDS)
rec_matrix = self.countvectorize(df)
else:
if isfile(PATH_PICKLE_DESC) and use_pickle:
df = pd.read_pickle(PATH_PICKLE_DESC)
else:
df = self.make_desc(rec.md)
df.to_pickle(PATH_PICKLE_DESC)
rec_matrix = self.tfidf(df)
rec_movie = rec_matrix[title_index]
ids = rec_movie.argsort()[::-1][1:SCAN_SIZE + 1]
if critics:
return rec.top_movies(df.iloc[ids],
percentile=0.50,
limit=limit,
offset=0)
else:
return df.iloc[ids[:limit]][[
"original_title",
"id",
"vote_average",
"vote_count",
"popularity",
"release_date",
]]