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movierecommandation.py
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movierecommandation.py
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import numpy as np
import pandas as pd
from flask import Flask, render_template, request
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import json
import bs4 as bs
import urllib.request
import pickle
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'))
def create_similarity():
data = pd.read_csv('main_data.csv')
# creating a count matrix
cv = CountVectorizer()
count_matrix = cv.fit_transform(data['comb'])
# creating a similarity score matrix
similarity = cosine_similarity(count_matrix)
return data,similarity
def rcmd(m):
m = m.lower()
try:
data.head()
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')
else:
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
l = []
for i in range(len(lst)):
a = lst[i][0]
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('"]','')
return my_list
def get_suggestions():
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)
@app.route("/similarity",methods=["POST"])
def similarity():
movie = request.form['name']
rc = rcmd(movie)
if type(rc)==type('string'):
return rc
else:
m_str="---".join(rc)
return m_str
@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']
# get movie suggestions for auto complete
suggestions = get_suggestions()
# call the convert_to_list function for every string that needs to be converted to list
rec_movies = convert_to_list(rec_movies)
rec_posters = convert_to_list(rec_posters)
cast_names = convert_to_list(cast_names)
cast_chars = convert_to_list(cast_chars)
cast_profiles = convert_to_list(cast_profiles)
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("]","")
# 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'\"','\"')
# 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))}
# 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)
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')
# combining reviews and comments into a dictionary
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__':
app.run(debug=True)