def predict(movie_id):
target_movie = Movies.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
print "Best guess for movie %d: %s is %.2f stars"%\
(movie_id, target_movie['title'], rating)
def movie_details():
movie_id = int(request.form["movie_id"])
average_rating = movies.average_rating(movie_id)
md = Movies.get(movie_id)
return render_template("details.html", md=md, avg = average_rating)
