recommender.py

# creating a Series for the movie titles so they are associated to an ordered numerical
# list I will use in the function to match the indexes
indices = pd.Series(df.index)

#  defining the function that takes in movie title 
# as input and returns the top 10 recommended movies
def recommendations(title, cosine_sim = cosine_sim):
    
    # initializing the empty list of recommended movies
    recommended_movies = []
    
    # gettin the index of the movie that matches the title
    idx = indices[indices == title].index[0]

    # creating a Series with the similarity scores in descending order
    score_series = pd.Series(cosine_sim[idx]).sort_values(ascending = False)

    # getting the indexes of the 10 most similar movies
    top_10_indexes = list(score_series.iloc[1:11].index)
    
    # populating the list with the titles of the best 10 matching movies
    for i in top_10_indexes:
        recommended_movies.append(list(df.index)[i])
        
    return recommended_movies