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engine.py
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import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel
from ast import literal_eval
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
df1=pd.read_csv('recommendation/tmdb-5000-movie-dataset/tmdb_5000_credits.csv')
df2=pd.read_csv('recommendation/tmdb-5000-movie-dataset/tmdb_5000_movies.csv')
df1.columns = ['id','tittle','cast','crew']
df2= df2.merge(df1,on='id')
C= df2['vote_average'].mean()
m= df2['vote_count'].quantile(0.9)
tfidf = TfidfVectorizer(stop_words='english')
df2['overview'] = df2['overview'].fillna('')
tfidf_matrix = tfidf.fit_transform(df2['overview'])
cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)
indices = pd.Series(df2.index, index=df2['title']).drop_duplicates()
def get_recommendations(title, cosine_sim, indices):
idx = indices[title]
sim_scores = list(enumerate(cosine_sim[idx]))
sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
sim_scores = sim_scores[1:11]
movie_indices = [i[0] for i in sim_scores]
return df2['title'].iloc[movie_indices]
features = ['cast', 'crew', 'keywords', 'genres', 'production_companies']
for feature in features:
df2[feature] = df2[feature].apply(literal_eval)
def get_director(x):
for i in x:
if i['job'] == 'Director':
return str(i['name'])
return " "
def get_companies(x):
return [i['name'] for i in x]
def get_list(x):
if isinstance(x, list):
names = [i['name'] for i in x]
if len(names) > 3:
names = names[:3]
return names
return []
df2['director'] = df2['crew'].apply(get_director)
features = ['cast', 'keywords', 'genres']
for feature in features:
df2[feature] = df2[feature].apply(get_list)
def clean_data(x):
if isinstance(x, list):
return [str.lower(i.replace(" ", "")) for i in x]
else:
if isinstance(x, str):
return str.lower(x.replace(" ", ""))
else:
return ''
features = ['cast', 'keywords', 'director', 'genres']
for feature in features:
df2[feature] = df2[feature].apply(clean_data)
def create_general_soup(x):
return ' '.join(x['keywords']) + ' ' + ' '.join(x['cast']) + ' ' + x['director'] + ' ' + ' '.join(x['genres'])
def create_director_soup(x):
val = ' '
for i in x['keywords']:
val += ' ' + x['director']
return ' '.join(x['keywords']) + val
def create_plot_soup(x):
return ' '.join(x['keywords']) + ' ' + x['overview']
def create_genre_soup(x):
return ' '.join(x['keywords']) + ' ' + ' '.join(x['genres'])
def create_company_soup(x):
return ' '.join(x['keywords']) + ' ' + ' '.join(x['company_list'])
def return_recommendation(movie, choice):
switchDict = {"general": create_general_soup, "director": create_director_soup, "plot": create_plot_soup,
"genre": create_genre_soup, "company": create_company_soup}
df2['director'] = df2['crew'].apply(get_director).tolist()
df2['company_list'] = df2['production_companies'].apply(get_companies).tolist()
df2['soup'] = df2.apply(switchDict.get(choice, "invalid choice"), axis=1)
count = CountVectorizer(stop_words='english')
count_matrix = count.fit_transform(df2['soup'])
cosine_sim2 = cosine_similarity(count_matrix, count_matrix)
indices = pd.Series(df2.index, index=df2['title'])
recommendationlist = (get_recommendations(movie, cosine_sim2, indices))
rec_indices = recommendationlist.index.values.tolist()
recObj = []
rectitles = []
recoverviews = []
genres = []
directors = []
companies = []
for i in rec_indices:
recObj.append(df2.loc[(df2.index.values == i)])
for entry in recObj:
rectitles.append(entry.iloc[0]['original_title'])
recoverviews.append(entry.iloc[0]['overview'])
genres.append(entry.iloc[0]['genres'])
directors.append(get_director(entry.iloc[0]['crew']))
companies.append(get_companies(entry.iloc[0]['production_companies']))
return_list = []
for x in range(10):
return_list.append({"title":rectitles[x], "overview":recoverviews[x], "genres":genres[x], "director":directors[x], "companies":companies[x]})
return return_list
def return_general(movie):
df2['director'] = df2['crew'].apply(get_director).tolist()
df2['company_list'] = df2['production_companies'].apply(get_companies).tolist()
df2['soup'] = df2.apply(create_general_soup, axis=1)
count = CountVectorizer(stop_words='english')
count_matrix = count.fit_transform(df2['soup'])
cosine_sim2 = cosine_similarity(count_matrix, count_matrix)
indices = pd.Series(df2.index, index=df2['title'])
recommendationlist = (get_recommendations(movie, cosine_sim2, indices))
rec_indices = recommendationlist.index.values.tolist()
recObj = []
rectitles = []
recoverviews = []
genres = []
directors = []
companies = []
for i in rec_indices:
recObj.append(df2.loc[(df2.index.values == i)])
for entry in recObj:
rectitles.append(entry.iloc[0]['original_title'])
recoverviews.append(entry.iloc[0]['overview'])
genres.append(entry.iloc[0]['genres'])
directors.append(get_director(entry.iloc[0]['crew']))
companies.append(get_companies(entry.iloc[0]['production_companies']))
return_list = []
for x in range(10):
return_list.append({"title":rectitles[x], "overview":recoverviews[x], "genres":genres[x], "director":directors[x], "companies":companies[x]})
return return_list
#recommendation_info = return_general("The Dark Knight Rises")
movielist = df2['original_title'].tolist()
infolist = df2['overview'].tolist()
directors = df2['crew'].apply(get_director).tolist()
genre = df2['genres'].tolist()
companieslist = df2['production_companies'].apply(get_companies).tolist()
runtimelist = df2['runtime'].tolist()