data=request.get_json()

print(data)

import pandas as pd

from rake_nltk import Rake

import numpy as np

from sklearn.metrics.pairwise import cosine_similarity

from sklearn.feature_extraction.text import CountVectorizer

pd.set_option('display.max_columns', 100)

df = pd.read_csv('https://query.data.world/s/uikepcpffyo2nhig52xxeevdialfl7')

df.head()

df = df[['Title','Genre','Director','Actors','Plot']]

df.head()

#df['Actors'] = df['Actors'].map(lambda x: x.split(',')[:3])

#df['Genre'] = df['Genre'].map(lambda x: x.lower().split(','))

#df['Director'] = df['Director'].map(lambda x: x.split(' '))

#for index, row in df.iterrows():

#row['Actors'] = [x.lower().replace(' ','') for x in row['Actors']]

#row['Director'] = ''.join(row['Director']).lower()

df['Key_words'] = ""

for index, row in df.iterrows():

plot = row['Plot']

r = Rake()

r.extract_keywords_from_text(plot)

key_words_dict_scores = r.get_word_degrees()

row['Key_words'] = list(key_words_dict_scores.keys())

df.drop(columns = ['Plot'], inplace = True)

df.set_index('Title', inplace = True)

df.head()

df['bag_of_words'] = ''

columns = df.columns

for index, row in df.iterrows():

words = ''

for col in columns:

if col != 'Director':

words = words + ' '.join(row[col])+ ' '

else:

words = words + row[col]+ ' '

row['bag_of_words'] = words

df.drop(columns = [col for col in df.columns if col!= 'bag_of_words'], inplace = True)

df.head()

count = CountVectorizer()

count_matrix = count.fit_transform(df['bag_of_words'])

indices = pd.Series(df.index)

indices[:5]

cosine_sim = cosine_similarity(count_matrix, count_matrix)

cosine_sim

recommended_movies = []

def recommendations(title, cosine_sim = cosine_sim):

print("You are in the recommendations section")

idx = indices[indices == title].index[0]

score_series = pd.Series(cosine_sim[idx]).sort_values(ascending = False)

top_10_indexes = list(score_series.iloc[1:11].index)

for i in top_10_indexes:

recommended_movies.append(list(df.index)[i])

return recommended_movies

for key,value in data.items():

recommendations(value)