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main.py
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main.py
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from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import pandas as pd
cv = CountVectorizer()
df = pd.read_csv("movie_dataset.csv")
df = df.iloc[:, 0:24]
features = ['keywords', 'cast', 'genres', 'director']
for feature in features:
df[feature] = df[feature].fillna(' ')
def combine_features(row):
return row['keywords'] + " " + row['cast'] + " " + row['genres'] + " " + row['director']
df["combine_features"] = df.apply(combine_features, axis=1)
count_matrix = cv.fit_transform(df["combine_features"])
cosine_sim = cosine_similarity(count_matrix)
favorite_movie = ""
def get_movie_user_likes(movie):
global favorite_movie
favorite_movie = movie
return favorite_movie
def get_title_from_index(index):
return df[df.index == index]["title"].values[0]
def get_index_from_title(title):
return df[df.title == title]["index"].values[0]
def get_recommendations():
li = []
movie_index = get_index_from_title(favorite_movie)
similar_movies = list(enumerate(cosine_sim[int(movie_index)]))
sorted_similar_movies = sorted(similar_movies, key=lambda x: x[1], reverse=True)
i = 0
for movie in sorted_similar_movies:
if 0 < i < 6:
li.append(get_title_from_index(movie[0]))
i = i + 1
return li