added python file

notebooks/main.py

@@ -0,0 +1,144 @@
+import numpy as np
+import pandas as pd
+import json
+import bs4 as bs
+import urllib.request
+import pickle
+import requests
+from flask import Flask, render_template, request
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+from datetime import date, datetime
+
+# 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'))
+
+# 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
+
+# convert list of numbers to list (eg. "[1,2,3]" to [1,2,3])
+def convert_to_list_num(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("/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']
+    rel_date = request.form['rel_date']
+    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']
+    rec_movies_org = request.form['rec_movies_org']
+    rec_year = request.form['rec_year']
+    rec_vote = request.form['rec_vote']
+
+    # 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_org = convert_to_list(rec_movies_org)
+    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)
+
+    """"
+    list = [' rec_movies_org','rec_movies']
+    for i in list:
+        list = convert_to_list(list)
+    """
+
+    # convert string to list (eg. "[1,2,3]" to [1,2,3])
+    cast_ids = convert_to_list_num(cast_ids)
+    rec_vote = convert_to_list_num(rec_vote)
+    rec_year = convert_to_list_num(rec_year)
+
+    # 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'\"','\"')
+
+    for i in range(len(cast_chars)):
+        cast_chars[i] = cast_chars[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],rec_movies_org[i],rec_vote[i],rec_year[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('Positive' if pred else 'Negative')
+
+    # getting current date
+    movie_rel_date = ""
+    curr_date = ""
+    if(rel_date):
+        today = str(date.today())
+        curr_date = datetime.strptime(today,'%Y-%m-%d')
+        movie_rel_date = datetime.strptime(rel_date, '%Y-%m-%d')
+
+    # 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,movie_rel_date=movie_rel_date,curr_date=curr_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)