def predict(movie_id):
target_movie = Movie.get(movie_id)
#target_movie = get_movie(movie_id)
target_ratings = make_target_ratings(movie_id)
# target_movie_rating = rating
my_movie_ids = movie_ids(0)
movies_to_compare = []
for id in my_movie_ids:
ratings = make_target_ratings(id)
movies_to_compare.append(ratings)
similarities = []
for ratings in movies_to_compare:
our_rating = ratings[0] # ratings is not a list, it is a dictionary
similarity = pearson(target_ratings, ratings)
tup = (similarity, our_rating)
similarities.append(tup)
# (similarity, ranking)
top_five = sorted(similarities)
top_five.reverse()
top_five = top_five[:5]
num = 0.0
den = 0.0
# Use a weighted mean rather than a strict top similarity
for sim, m in top_five:
num += (float(sim) * m)
den += sim
rating = num/den
return "%.2f" %(rating)
def movie_details(movie_id):
movie_id = int(movie_id)
movie = Movie.get(movie_id)
title = movie["title"]
genres = movie["genres"]
genre = ", ".join(genres)
imdb = movie["imdb_url"]
movie_id = movie["_id"]
rating_records = Rating.get_many(movie_id)
ratings = [ rec['rating'] for rec in rating_records ]
avg = float(sum(ratings))/len(ratings)
return render_template("movie_details.html", title=title, genre=genre, avg=avg, imdb=imdb, movie=movie)
def home():
movie_ids=[]
ratings=[]
while len(movie_ids) < 10:
movie_id = randint(1,1682)
movie = Movie.get(movie_id)
movie_ids.append(movie)
movie_id = movie["_id"]
rating_records = Rating.get_many(movie_id)
rating = [ rec['rating'] for rec in rating_records ]
avg = float(sum(rating))/len(rating)
ratings.append(avg)
return render_template("potato.html", ratings=ratings, movie_ids=movie_ids)
def get_movie(movie_id):
return Movie.get(movie_id)
def average_rating(movie_id):
movie = Movie.get(movie_id)
avg = movie.get_average()
print "%.1f stars"%(avg)
def movie_details():
movie_id = request.form['movie_id']
movie = Movie.get(int(movie_id))
return render_template("movie_details.html", movie=movie)
