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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys, os
from nltk import sent_tokenize, word_tokenize
from nltk import everygrams
from nltk import FreqDist
import json
import re
"""
PART 1
We create the dictionary and save it.
"""
stopws = [",", ".", "?","!",":","(",")",">","<","@","#","``","/","","''","","-","", "DOCTYPE", "html", "!", "'", "<br>", "<br />", "/body", "/html", "/head", "h2", "/h2", "h1", "/h1","",""]
path = "static/files/"
for path, subdirs, files in os.walk(path):
for name in files:
if name.endswith('html'):
file = os.path.join(path, name)
total = open("allhtml.txt", "a")
with open(file) as f:
content = f.read()
total.write(content)
total.close()
keyword_list = []
# with open('allhtml.txt') as f:
# content = f.read()
# tokens = word_tokenize(content)
# tokens = [token for token in tokens if token not in stopws]
# freq_file=FreqDist(tokens)
# print(tokens)
# keyword_list.append(freq_file.most_common(50))
# print(keyword_list[0])
with open('allhtml.txt') as f:
content = f.read()
tokens = word_tokenize(content)
tokens = [token for token in tokens if token not in stopws]
keyword_list = list(set(tokens))
# print(tokens)
# print(keyword_list)
"""
PART 2
We iterate through the entire collection of html files, tokenize the words, and check to see whether any of them is in the keyword_list. If they are, then we generate a json file.
"""
# wordlist = {}
# avoiding_repetition = []
sentences_w_word = {}
def analysis(the_word, file_name):
id = file_name[13:15]
with open(file_name) as f:
content = f.read()
sent_tokens = sent_tokenize(content)
new_sent_tokens = []
for sent_token in sent_tokens:
if the_word in sent_token:
new_sent_tokens.append({'id': id, 'sentence': sent_token.replace('\n', ' ').strip("'<>()")})
if the_word in sentences_w_word: # if this is not the first iteration
previous_sent_tokens = sentences_w_word[the_word]
full_sent_tokens = previous_sent_tokens + new_sent_tokens
else:
full_sent_tokens = new_sent_tokens
sentences_w_word[word] = full_sent_tokens
path = "static/files/"
for path, subdirs, files in os.walk(path):
for name in files:
if name.endswith('html'):
file = os.path.join(path, name)
for word in keyword_list:
analysis(word, file)
with open('wordlist.json', 'w', encoding="utf8") as outfile:
json.dump(sentences_w_word, outfile, ensure_ascii=False)
# def analysis(file, id):
# sent_tokens = sent_tokenize(file) # sentence tokenizing
# for sent_token in sent_tokens:
# tokens = word_tokenize(sent_token) # word tokenizing
# print(tokens)
# for token in tokens:
# for first in keyword_list:
# if token == first: # if token is in keyword_list
# if token not in wordlist:
# wordlist[token] = []
# sent_dict = {}
# sent_dict["id"]=id
# sent_dict["sentence"] = sent_token.replace('\n', ' ')
# wordlist[token].append(sent_dict)
# elif token not in avoiding_repetition:
# # print(wordlist[token])
# sent_dict = {}
# sent_dict["id"]=id
# sent_dict["sentence"] = sent_token.replace('\n', ' ')
# wordlist[token].append(sent_dict)
# avoiding_repetition.append(token)
# with open('static/files/17/17.blurb.html') as f:
# content = f.read()
# analysis(content, '17')
# # reading each individual html file
# path = "static/files/"
# for path, subdirs, files in os.walk(path):
# for name in files:
# if name.endswith('html'):
# file = os.path.join(path, name)
# with open(file) as f:
# content = f.read()
# id=name[:2]
# analysis(content, id)
# json_wordlist = json.dumps(wordlist)
# for item in wordlist:
# for item2 in wordlist[item]:
# print(item)
# print(item2["sentence"])
# print("\n")