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Clean Code #156

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168 changes: 103 additions & 65 deletions movie recommandation/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,18 +10,20 @@
import requests

# load the nlp model and tfidf vectorizer from disk
filename = 'nlp_model.pkl'
clf = pickle.load(open(filename, 'rb'))
vectorizer = pickle.load(open('tranform.pkl','rb'))
filename = "nlp_model.pkl"
clf = pickle.load(open(filename, "rb"))
vectorizer = pickle.load(open("tranform.pkl", "rb"))


def create_similarity():
data = pd.read_csv('main_data.csv')
data = pd.read_csv("main_data.csv")
# creating a count matrix
cv = CountVectorizer()
count_matrix = cv.fit_transform(data['comb'])
count_matrix = cv.fit_transform(data["comb"])
# creating a similarity score matrix
similarity = cosine_similarity(count_matrix)
return data,similarity
return data, similarity


def rcmd(m):
m = m.lower()
Expand All @@ -30,70 +32,76 @@ def rcmd(m):
similarity.shape
except:
data, similarity = create_similarity()
if m not in data['movie_title'].unique():
return('Sorry! The movie that you have requested is not in our database. Please check the spelling or try with some other movies')
if m not in data["movie_title"].unique():
return "Sorry! The movie that you have requested is not in our database. Please check the spelling or try with some other movies"
else:
i = data.loc[data['movie_title']==m].index[0]
i = data.loc[data["movie_title"] == m].index[0]
lst = list(enumerate(similarity[i]))
lst = sorted(lst, key = lambda x:x[1] ,reverse=True)
lst = lst[1:11] # excluding first item since it is the requested movie itself
lst = sorted(lst, key=lambda x: x[1], reverse=True)
lst = lst[1:11] # excluding first item since it is the requested movie itself
l = []
for i in range(len(lst)):
a = lst[i][0]
l.append(data['movie_title'][a])
l.append(data["movie_title"][a])
return l



# converting list of string to list (eg. "["abc","def"]" to ["abc","def"])
def convert_to_list(my_list):
my_list = my_list.split('","')
my_list[0] = my_list[0].replace('["','')
my_list[-1] = my_list[-1].replace('"]','')
my_list[0] = my_list[0].replace('["', "")
my_list[-1] = my_list[-1].replace('"]', "")
return my_list


def get_suggestions():
data = pd.read_csv('main_data.csv')
return list(data['movie_title'].str.capitalize())
data = pd.read_csv("main_data.csv")
return list(data["movie_title"].str.capitalize())


app = Flask(__name__)


@app.route("/")
@app.route("/home")
def home():
suggestions = get_suggestions()
return render_template('home.html',suggestions=suggestions)
return render_template("home.html", suggestions=suggestions)

@app.route("/similarity",methods=["POST"])

@app.route("/similarity", methods=["POST"])
def similarity():
movie = request.form['name']
movie = request.form["name"]
rc = rcmd(movie)
if type(rc)==type('string'):
if type(rc) == type("string"):
return rc
else:
m_str="---".join(rc)
m_str = "---".join(rc)
return m_str

@app.route("/recommend",methods=["POST"])

@app.route("/recommend", methods=["POST"])
def recommend():
# getting data from AJAX request
title = request.form['title']
cast_ids = request.form['cast_ids']
cast_names = request.form['cast_names']
cast_chars = request.form['cast_chars']
cast_bdays = request.form['cast_bdays']
cast_bios = request.form['cast_bios']
cast_places = request.form['cast_places']
cast_profiles = request.form['cast_profiles']
imdb_id = request.form['imdb_id']
poster = request.form['poster']
genres = request.form['genres']
overview = request.form['overview']
vote_average = request.form['rating']
vote_count = request.form['vote_count']
release_date = request.form['release_date']
runtime = request.form['runtime']
status = request.form['status']
rec_movies = request.form['rec_movies']
rec_posters = request.form['rec_posters']
title = request.form["title"]
cast_ids = request.form["cast_ids"]
cast_names = request.form["cast_names"]
cast_chars = request.form["cast_chars"]
cast_bdays = request.form["cast_bdays"]
cast_bios = request.form["cast_bios"]
cast_places = request.form["cast_places"]
cast_profiles = request.form["cast_profiles"]
imdb_id = request.form["imdb_id"]
poster = request.form["poster"]
genres = request.form["genres"]
overview = request.form["overview"]
vote_average = request.form["rating"]
vote_count = request.form["vote_count"]
release_date = request.form["release_date"]
runtime = request.form["runtime"]
status = request.form["status"]
rec_movies = request.form["rec_movies"]
rec_posters = request.form["rec_posters"]

# get movie suggestions for auto complete
suggestions = get_suggestions()
Expand All @@ -107,46 +115,76 @@ def recommend():
cast_bdays = convert_to_list(cast_bdays)
cast_bios = convert_to_list(cast_bios)
cast_places = convert_to_list(cast_places)

# convert string to list (eg. "[1,2,3]" to [1,2,3])
cast_ids = cast_ids.split(',')
cast_ids[0] = cast_ids[0].replace("[","")
cast_ids[-1] = cast_ids[-1].replace("]","")
cast_ids = cast_ids.split(",")
cast_ids[0] = cast_ids[0].replace("[", "")
cast_ids[-1] = cast_ids[-1].replace("]", "")

# rendering the string to python string
for i in range(len(cast_bios)):
cast_bios[i] = cast_bios[i].replace(r'\n', '\n').replace(r'\"','\"')
cast_bios[i] = cast_bios[i].replace(r"\n", "\n").replace(r"\"", '"')

# combining multiple lists as a dictionary which can be passed to the html file so that it can be processed easily and the order of information will be preserved
movie_cards = {rec_posters[i]: rec_movies[i] for i in range(len(rec_posters))}

casts = {cast_names[i]:[cast_ids[i], cast_chars[i], cast_profiles[i]] for i in range(len(cast_profiles))}

cast_details = {cast_names[i]:[cast_ids[i], cast_profiles[i], cast_bdays[i], cast_places[i], cast_bios[i]] for i in range(len(cast_places))}
casts = {
cast_names[i]: [cast_ids[i], cast_chars[i], cast_profiles[i]]
for i in range(len(cast_profiles))
}

cast_details = {
cast_names[i]: [
cast_ids[i],
cast_profiles[i],
cast_bdays[i],
cast_places[i],
cast_bios[i],
]
for i in range(len(cast_places))
}

# web scraping to get user reviews from IMDB site
sauce = urllib.request.urlopen('https://www.imdb.com/title/{}/reviews?ref_=tt_ov_rt'.format(imdb_id)).read()
soup = bs.BeautifulSoup(sauce,'lxml')
soup_result = soup.find_all("div",{"class":"text show-more__control"})

reviews_list = [] # list of reviews
reviews_status = [] # list of comments (good or bad)
sauce = urllib.request.urlopen(
"https://www.imdb.com/title/{}/reviews?ref_=tt_ov_rt".format(imdb_id)
).read()
soup = bs.BeautifulSoup(sauce, "lxml")
soup_result = soup.find_all("div", {"class": "text show-more__control"})

reviews_list = [] # list of reviews
reviews_status = [] # list of comments (good or bad)
for reviews in soup_result:
if reviews.string:
reviews_list.append(reviews.string)
# passing the review to our model
movie_review_list = np.array([reviews.string])
movie_vector = vectorizer.transform(movie_review_list)
pred = clf.predict(movie_vector)
reviews_status.append('Good' if pred else 'Bad')
reviews_status.append("Good" if pred else "Bad")

# combining reviews and comments into a dictionary
movie_reviews = {reviews_list[i]: reviews_status[i] for i in range(len(reviews_list))}
movie_reviews = {
reviews_list[i]: reviews_status[i] for i in range(len(reviews_list))
}

# passing all the data to the html file
return render_template('recommend.html',title=title,poster=poster,overview=overview,vote_average=vote_average,
vote_count=vote_count,release_date=release_date,runtime=runtime,status=status,genres=genres,
movie_cards=movie_cards,reviews=movie_reviews,casts=casts,cast_details=cast_details)

if __name__ == '__main__':
return render_template(
"recommend.html",
title=title,
poster=poster,
overview=overview,
vote_average=vote_average,
vote_count=vote_count,
release_date=release_date,
runtime=runtime,
status=status,
genres=genres,
movie_cards=movie_cards,
reviews=movie_reviews,
casts=casts,
cast_details=cast_details,
)


if __name__ == "__main__":
app.run(debug=True)