Get glossary from text

glossay.py

import requests

# pip install numpy
import numpy as np
# pip install pandas
import pandas as pd
# pip install scikit-learn
from sklearn.feature_extraction.text import TfidfVectorizer

# ################# Edit this parameters ###################

# Add you Oxford API id and key here
app_id = 'ffffffff'
app_key = 'ffffffffffffffffffffffffffffffff'

input_file_name = 'report.txt'
output_file_name = 'glossary.txt'
max_word = 20

############################################################

in_file = open(input_file_name, 'r')
out_file = open(output_file_name, 'w')
data = in_file.read()

vectorize = TfidfVectorizer()
tvec_weights = vectorize.fit_transform([data])

weights = np.asarray(tvec_weights.mean(axis=0)).ravel().tolist()
weights_df = pd.DataFrame({'term': vectorize.get_feature_names(), 'weight': weights})
weights_df = weights_df.sort_values(by='weight', ascending=True)


def get_defination(word):
    url = 'https://od-api.oxforddictionaries.com:443/api/v1/entries/en/' + word.lower()
    r = requests.get(url, headers={'app_id': app_id, 'app_key': app_key})
    try:
        r_dict = r.json()
        return r_dict['results'][0]['lexicalEntries'][0]['entries'][0]['senses'][0]['definitions'][0]
    except:
        return None


i = 0
for w in weights_df['term']:
    print("Getting defination for " + w)
    defination = get_defination(w)
    if defination is not None:
        i += 1
        print("Defination of " + w + " -> " + defination)
        out_file.write("%s - %s\n" % (w, defination))
        if i >= max_word:
            break
    else:
        print("Cannot find defination of " + w)

in_file.close()
out_file.close()