def make_jsonFile(dic_path, doc_path, file_name):
igate_cl = generate_Dictionary(dic_path)
# 목차를 이용하여 사전 생성
index_dictionary = igate_cl.DICTIONRAY_LIST
json_file = []
# 문서 로드
doc_result = docx2python(doc_path)
main_title = ''
sub_title = ''
title = ''
index_len = ''
for j in range(1, len(doc_result.body)):
doc_body = doc_result.body[j]
doc_len = len(doc_body)
if doc_len == 1:
# 문서 스플릿
doc_list = generate_doc(doc_body, doc_len, index_dictionary)
print(doc_list)
for _, content in enumerate(doc_list):
#print(content)
for text in content:
if text in index_dictionary:
#print(text)
index = index_dictionary[text] # index는 해당 목차(사전)의 번호
index_len = len(index)
# 1: 대분류 / 2: 중분류 / 3: 소분류
if index_len == 1:
main_title = text
sub_title = text
title = text
elif index_len == 2:
sub_title = text
title = text
elif index_len == 3:
title = text
json_data = generate_doc_to_json(index_len, content,
main_title, sub_title, title,
"string")
json_file.append(json_data)
else:
print('ID = {0}'.format(str(j)))
# 테이블
table = generate_table(doc_body, doc_len)
#print(table)
json_data = generate_doc_to_json(index_len, table, main_title,
sub_title, title, "table")
json_file.append(json_data)
with open(path.RESULT_DATA_PATH + file_name + '.json',
'w',
encoding='utf-8') as make_file:
json.dump(json_file, make_file, ensure_ascii=False, indent="\t")
def docx2text(self,filename):
"""
:param filename: docx源文件
:return: docx的文字内容
"""
def flatten(S):
"""
展平嵌套列表
:param S: 嵌套列表
:return: 单个不嵌套的列表
"""
if S == []:
return S
if isinstance(S[0], list):
return flatten(S[0]) + flatten(S[1:])
return S[:1] + flatten(S[1:])
if filename.split('.')[-1] == "docx":
# 提取文本
doc_result = docx2python(filename)
# 展开结果
res = flatten(doc_result.body)
# 去除空格
res = [r for r in res if r.strip()]
# 返回成原来格式
content = '。'.join(res)
elif filename.split('.')[-1] == "doc":
content = subprocess.check_output(['antiword', filename])
content = content.decode('utf-8')
return content
def main(path):
print(path)
real_path = path
if path.endswith(".doc"):
word = wc.Dispatch('Word.Application')
doc = word.Documents.Open(path)
doc.SaveAs(path + "x", 12, False, "", True, "", False, False, False,
False)
doc.Close()
word.Quit()
real_path = path + "x"
elif not path.endswith(".docx"):
print("请检查文件后缀名是否有效！")
return
keyword = '参考文献'
file = docx2python(real_path)
temp = file.text.split('\n')
content = []
for i in range(len(temp)):
if i % 2 == 0:
content.append(temp[i])
# 可以输出reference看看，已经是text了
reference = extract(keyword, content)
check(reference)
def post(self, *args, **kwargs):
uploaded_file = self.request.FILES['document']
document = docx2python(uploaded_file, self.img_path)
os.chdir(self.img_path)
for f in os.listdir(self.img_path):
if f.endswith('.wmf') or f.endswith('.emf') or f.endswith('.jpeg'):
i = Image.open(f)
fn, fext = os.path.splitext(f)
i.save('{}.png'.format(fn))
document_content = document.body
z = document_content[0][0][0]
composite_list = [z[x:x + 8] for x in range(0, len(z), 8)]
for row in composite_list:
try:
data = Questions.objects.create(subject=row[0],
question=row[1],
ques_image=row[2],
option_a=row[3],
option_b=row[4],
option_c=row[5],
option_d=row[6],
ans=row[7])
except Exception as e:
print(e)
messages.success(self.request, 'Data uploded successfully')
return redirect("admin/")
def parse_word(self, file_location):
#Define variables
combo_filing = []
exhibits = []
emerging_growth = []
period = []
items = []
eight_k = {}
# CREATE WORD DOCUMENT OBJECT
try:
doc = docx.Document(file_location)
docs = docx2python(file_location, extract_image=True)
except:
print('Only ".DOCX" files are excepted.')
return None
# CREATE PARAGRAPH OBJECT
paragraphs = doc.paragraphs
# LOOP THROUGH LINES OF PARAGRAPH. FIND CENTER ALIGNED TEXT THAT EQUAL "FORM 8-K" AND HOUSE IT IN A VARIABLE
for line in paragraphs:
if line.alignment == WD_TAB_ALIGNMENT.CENTER and (form_type := line.text.strip().title()) == 'Form 8-K':
eight_k['FORM_TYPE'] = form_type
def extract_table_image_count(resume, celltext):
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
file_path = (os.path.join(BASE_DIR, "media"))
file_path_for_images = os.path.join(file_path, resume.name)
doc = docx.Document(resume)
doc_result = docx2python(file_path_for_images)
table_count = sum(1 for tab in doc.tables)
images_count = sum(1 for img in doc_result.images)
return [table_count, images_count]
def get_random_course_fragment_from_pages():
fragment_path = get_random_filepath_by_path(
project_root_directory + "\\course_fragments\\pages\\",
COURSE_FRAGMENTS_EXTENSION)
document = docx2python(fragment_path)
text = ""
for line in document.text.splitlines()[:-4]:
text = text + '\n' + line
return text[1:]
def test_empty_properties_dict_if_docProps_not_found(self) -> None:
"""
It seems Google Docs docx files to not contain a document properties file:
`docProps/core.xml`. The contents of this file are returned as a dictionary.
To correct the above error, result.properties will now return an empty
dictionary.
"""
result = docx2python(TEST_FILE)
assert result.properties == {}
def _extract(self) -> str:
text = ''
try:
text = docx2python(self.fpath)
except Exception as ee:
print(f'extract_unique_text_from_msword: {ee}')
pass
return text
def __init__(self, path: str):
"""
TODO Docs
"""
self.sections = []
raw = docx2python(path).body[0][0][0]
chunks = self.chunk_raw_body(raw)
for chunk in chunks[1:]:
self.sections.append(Section(chunk))
def write_book(self, doc):
#don't judge me, I didn't make the choice to store the info in a Word doc
with open("data/book/book.docx", "wb") as file:
file.seek(0)
file.write(doc.getvalue())
file.truncate()
text = re.sub(self.STRIP_SPACES, "\n",
docx2python("data/book/book.docx").text)
results = re.findall(self.FIND_DATA,
text[text.find("This document contains"):])
return {entry[0]: entry for entry in results}
def readWithFormatting(files, inputFolderName, outputFolderName):
"""
Converts files into .txt
Maintains formatting as much as possible
Uses different libraries depending on file types
"""
# go over every file in list containing all files
for inputFile in files:
doc = ''
# get the full name and the extension of the files
fileName, fileExtension = os.path.splitext(inputFile)
# direct the new file name to the output folder by replacing the input folder name to the output folder name
outputPathFileName = fileName.replace(inputFolderName,
outputFolderName)
# get the output file path (not including the filename)
outputPath = os.path.dirname(outputPathFileName)
if fileExtension == '.docx':
print('Processing: {}'.format(inputFile))
# uses docx2python for now, since it's probably easier to keep the formatting consistent later on
doc = docx2python(inputFile)
doc = doc.text
# uses regex to replace repeated new lines into one
doc = re.sub(r'\n\n*', '\n', doc)
elif fileExtension == '.doc':
print('Processing: {}'.format(inputFile))
doc = textract.process(inputFile).decode("utf-8")
# uses regex to replace repeated new lines into one newline
doc = re.sub(r'\n\n*', '\n', doc)
elif fileExtension == '.pdf':
print('Processing: {}'.format(inputFile))
# read in the pdf file as a string
# this is a bit more complicated so it has its own function
doc = readPDFFile(inputFile)
elif fileExtension == '.png' or fileExtension == '.jpeg' or fileExtension == '.jpg':
print('Processing: {}'.format(inputFile))
# uses OCR to extract the text
doc = textract.process(inputFile,
method='tesseract',
language='eng+ind').decode("utf-8")
if doc:
# standardise the string (e.g. convert ligatures, other encoding issues)
doc = doc.translate(LIGATURES)
# save the string as the file name + .txt
newFileName = outputPathFileName + '.txt'
with open(newFileName, 'w') as text_file:
text_file.write(doc)
def __init__(self, contentsList_path):
doc_result = docx2python(contentsList_path)
doc_body_for_dic = doc_result.body[0][0][0]
pre_data1 = self.remove_blank_and_reg(doc_body_for_dic)
pre_data2, self.INDEX_LIST = self.find_num_n_remove_Front(pre_data1)
self.CONTENTS_LIST = self.find_num_n_remove_Back(
pre_data2) # 목차 리스트..! -> 키워드로 활용 가능
# 목차와 index 를 이용하여 사전 생성
self.DICTIONRAY_LIST = {
key: value
for key, value in zip(self.CONTENTS_LIST, self.INDEX_LIST)
}
def DocType(source):
result = source.filename
#split filename with (.) to get the file extension
result_splitted = result.split('.')
file_extension = result_splitted[-1]
#check the extension type and use appropriate method to read
if file_extension == "docx":
doc = docx2python(source).text
return doc
elif file_extension == "txt":
with open(source) as file:
doc = file.read()
return doc
elif file_extension == "pdf":
raw = parser.from_file(source)
doc = raw['content']
return doc
def prepare_text(file_name, main_path):
# create the path to the file
fn = os.path.join(main_path, file_name)
# convert it
subprocess.call(['C:\Program Files\LibreOffice\program\soffice.exe', '--headless', '--convert-to', 'docx', fn], shell=True)
file_name = file_name[:-4] + '.docx'
# read the file
content = docx2python.docx2python(file_name, extract_image=False)
# remove the docx after reading
os.remove(file_name)
# take the content with the structure it came with
content = content.body
#debug_print(content)
# remove duplicate nodes in the lists, remove duplicate strings at the lowest level
no_duplicates = removeInnerDups(removeDuplicates(content))
# debug_print(no_duplicates)
# remove empty arrs including the sorts of [[[]]]
removeEmptyArrs(no_duplicates)
return no_duplicates
def use_docx2python(fileName, params):
''' gets our title and the place, if theres any'''
parsed = docx2python(fileName)
heading = parsed.body
body = flatten(heading)
title = ''
place = ''
for i in body:
if '<a href=' in i:
title += i
if title:
title = lxml.html.fromstring(title).text_content()
else:
pass
if '[' in title:
place += re.split('\[', title)[-1]
params["headline"] = title
params["place"] = place[:-1]
def __init__(self, path):
#path is the location of the file, given when obj is created
path = path + "\\"
self.path = path
self.error = False
self.errorMsg = '错误提示：' + path
#find file name to contract and inspection file
os.chdir(path)
dirEntry = os.listdir(path)
contractName = ''
shipmentName = ''
for entry in dirEntry:
if '合同' in entry and '$' not in entry and 'pdf' not in entry and 'jpg' not in entry and 'jpeg' not in entry and '~$' not in entry:
contractName = entry
if '发货单' in entry:
shipmentName = entry
if len(contractName) == 0 or len(shipmentName) == 0:
foundFile = False
self.error = True
self.errorMsg += '\n 无法找到合同或者发货单文件，无法录入'
else:
foundFile = True
if foundFile:
#use docx2txt to find contract name and company name
contractInPy = docx2txt.process(contractName)
if '合同编号：' in contractInPy:
self.contractNum = contractInPy[contractInPy.find('合同编号：') + 5:contractInPy.find('合同编号：') + 12]
else:
self.contractNum = 0
self.error = True
self.errorMsg += '\n 合同docx文件，无法提取合同编号'
if '需 方:' in contractInPy and '产品名称' in contractInPy:
fullnamecomp_re = r"(?<=需 方: )(.*)(?=产品名称)"
match2 = re.search(fullnamecomp_re, contractInPy, flags=re.DOTALL)
self.companyFullName = match2[0].strip()
else:
self.companyFullName = 0
self.error = True
self.errorMsg += '\n 合同docx文件，无法提取需方信息'
#use docx2txt to fetch info in shipment info file
shipmentinPy = docx2txt.process(shipmentName)
if '用户：' in shipmentinPy:
compnam_re = r"(?<=用户：)([^\s]+)"
match = re.search(compnam_re, shipmentinPy)
self.companyName = match[0].strip()
else:
self.error = True
self.companyName = 0
self.errorMsg += '\n 发货单内无法提取用户简称'
if '收货单位地址：' in shipmentinPy:
shipadd_re = r'(?<=收货单位地址：)(.*)(\s)'
match4 = re.search(shipadd_re, shipmentinPy)
self.address = match4[0].strip()
elif '收货地址：' in shipmentinPy:
shipadd_re = r'(?<=收货地址：)(.*)(\s)'
match4 = re.search(shipadd_re, shipmentinPy)
self.address = match4[0].strip()
else:
self.error = True
self.address = 0
self.errorMsg += '\n发货单内无法提取收货单位地址'
phone_re = r"(?<=电话：)\s*([0-9]{3}|[0-9]{4})-*[0-9]{4}\s*[0-9]{4}"
match3 = re.search(phone_re, shipmentinPy)
if match3 == None:
self.phone = '无'
else:
self.phone = match3[0].strip()
# function for docx2python, remove empty element from returned list
def remove_empty(table):
# remove empty element of list
return list(filter(lambda x: not isinstance(x, (str, list, tuple)) or x,
(remove_empty(x) if isinstance(x, (tuple, list)) else x for x in table)))
# use docx2python to generate list and use that list to find price ,model count, and model number
contractInList = docx2python(path + contractName)
table = remove_empty(contractInList.body)
self.modelNumber = []
self.modelCount =[]
self.price = []
for row in table[1][1:]:
if len(row) == 5:
self.modelNumber.append(row[1][0])
if row[3][0].find("台") == -1:
self.modelCount.append(int(row[3][0]))
else:
self.modelCount.append(int(row[3][0][:row[3][0].find("台")]))
if row[2][0].find("元") == -1:
self.price.append(int(row[2][0]))
else:
self.price.append(int(row[2][0][:row[2][0].find("元")]))
def post(self, request):
nlp = en_core_web_sm.load()
pf = ProfanityFilter(nlps={'en': nlp})
# pf.custom_profane_word_dictionaries = {'en': {'sold down the river', 'dog'}}
# pf.extra_profane_word_dictionaries = {'en': {'sold', 'orange'}}
wordlist = []
context = {}
# FILE UPLOADED
if 'doc' in request.FILES:
doc = request.FILES['doc']
if doc.name.endswith(".docx"):
docx = docx2python(doc, extract_image=False)
context['doc'] = docx.text
elif doc.name.endswith(".txt"):
print("This is a test")
mytext = str(doc.read())
context['doc'] = mytext
return render(request, 'index.html', context=context)
# RETRIEVE WORDS AND SPLIT
document = request.POST['document']
word_lines = document.splitlines()
# CHECK EACH WORD IF PROFANITY
for line in word_lines:
if line == '':
wordlist.append(r'\n')
# NO LINE BREAK CONTINUE HERE
else:
words = line.split()
temp_list = []
original_list = []
# LOOP THROUGH EACH WORD.
for word in words:
clean_word = clear_punctuation(word).lower()
in_db = Words.objects.all().filter(
word__icontains=clean_word)
# WORD IS IN DATABASE
if in_db:
temp_list.append(clean_word)
temp_word = " ".join(temp_list)
starting_phrase = Words.objects.all().filter(
word__istartswith=temp_word)
# CURRENT WORD IS THE START OF THE PHRASE
if starting_phrase:
original_list.append(word)
completed = Words.objects.all().filter(
word__iexact=temp_word)
# CURRENT PHRASE IS COMPLETED
if completed:
original = " ".join(original_list)
original_list.clear()
new_word = format_word(original)
wordlist.append(new_word)
temp_list.clear()
# # TEMP WORD DID NOT COMPLETE THE PHRASE
# else:
# print('now we here bish')
# original = " ".join(original_list)
# original_list.clear()
# wordlist.append(original)
# temp_list.clear()
# NOT START OF PHRASE KEEP GOING
else:
wordlist.append(word)
temp_list.clear()
original_list.clear()
# WORD IS A PROFANITY
elif pf._is_profane_word('en', clean_word):
temp_word = " ".join(temp_list)
wordlist.append(temp_word)
new_word = format_word(word)
wordlist.append(new_word)
temp_list.clear()
# JUST A REGULAR WORD
else:
temp_word = " ".join(temp_list)
wordlist.append(temp_word)
wordlist.append(word)
temp_list.clear()
context["results"] = " ".join(wordlist)
context['document'] = document
return render(request, 'index.html', context=context)
def get_text_d2p(filename):
result = docx2python(filename)
string_list = [i for i in flatten_list(result.document)]
return remove_placeholders(string_list)
print(probs)
def check_for_ftnotes(body):
found = []
with open("found_ftnotes.csv", 'r') as f:
for row in csv.reader(f):
ftnote_found = row[1]
found.append(ftnote_found)
with open("ftnotes.csv", 'r') as orig_f:
for row in csv.reader(orig_f):
if len(row) > 1:
perek, orig_ftnote_marker, orig_ftnote = row
if orig_ftnote not in found:
if len(orig_ftnote) > 2:
print(row[1:])
pass
else:
print("Strange case")
bible_sections = [
ref.normal() for el in library.get_indexes_in_category("Tanakh")
for ref in library.get_index(el).all_section_refs()
]
start_at = 0
document = docx2python("betulah.docx", html=True)
body, ftnotes = get_body_html(document, {})
body = get_body_insert_ftnotes(body, ftnotes)
#check_for_ftnotes(body)
#post(body)
prev_body = []
last_ftnote_found = 0
text = {}
for (dirpath, dirnames, filenames) in walk(path):
filenames = [file for file in filenames if file.endswith(".docx") and not "~$" in file]
filenames = sorted(filenames, key=lambda f: int(re.search("\d+", f).group(0)))
create_footnotes_indices(dirpath)
counter = 0
for f in filenames:
if "OH" not in f:
continue
docx_file = dirpath+"/"+f
index = library.get_index(dirpath.split("/")[1])
if not index.title in text:
text[index.title] = {"Footnotes": {}}
document = docx2python(docx_file)
ftnotes = get_footnotes(document)
header = document.header[0][0][0][0]
match, prev_beer_match = get_match(header, prev_beer_match, prev_match)
if match not in text[index.title]:
text[index.title][match] = []
text[index.title]["Footnotes"][match] = []
last_ftnote_found = 0
body, last_ftnote_found = parse_body(document, index, ftnotes, last_ftnote_found)
text[index.title][match] += body
text[index.title]["Footnotes"][match] += ftnotes
prev_match = match
prev_body = body
def app():
##### Set up data for modelling
# import data to model CV against
workpac_data = pd.read_csv('job_descriptions.csv')
# remove id column as we can use index
workpac_data = workpac_data[['job_link', 'job_description', 'job_title']]
# clean Na's so functions can run
workpac_data = workpac_data.replace(np.nan, ' ', regex=True)
# clean job description
workpac_data['job_description_cleaned'] = workpac_data[
'job_description'].apply(_removeNonAscii)
workpac_data[
'job_description_cleaned'] = workpac_data.job_description_cleaned.apply(
func=make_lower_case)
workpac_data[
'job_description_cleaned'] = workpac_data.job_description_cleaned.apply(
func=remove_stop_words)
workpac_data[
'job_description_cleaned'] = workpac_data.job_description_cleaned.apply(
func=remove_punctuation)
workpac_data[
'job_description_cleaned'] = workpac_data.job_description_cleaned.apply(
func=remove_html)
workpac_data['job_title_cleaned'] = workpac_data['job_title'].apply(
_removeNonAscii)
workpac_data['job_title_cleaned'] = workpac_data.job_title_cleaned.apply(
func=make_lower_case)
workpac_data['job_title_cleaned'] = workpac_data.job_title_cleaned.apply(
func=remove_stop_words)
workpac_data['job_title_cleaned'] = workpac_data.job_title_cleaned.apply(
func=remove_punctuation)
workpac_data['job_title_cleaned'] = workpac_data.job_title_cleaned.apply(
func=remove_html)
# stem cleaned data
workpac_data['job_description_cleaned'] = workpac_data[
'job_description_cleaned'].apply(stem_sentences)
workpac_data['job_title_cleaned'] = workpac_data[
'job_title_cleaned'].apply(stem_sentences)
workpac_data['job_text_cleaned'] = workpac_data[
'job_title_cleaned'] + ' ' + workpac_data['job_description_cleaned']
model_df = workpac_data[['job_link', 'job_text_cleaned']]
# title of streamlit app
st.title(
'Candidate Job Ranking System Using Natural Language Processing (NLP)')
# sidebar
st.subheader('Files')
# file uploader
uploaded_file = st.file_uploader(label='Please upload your CV',
type=['docx'])
# create message variable
message = ''
doc = ''
if uploaded_file is not None:
try:
doc = docx2python(uploaded_file).text
message = 'File upload successful'
except Exception as e:
message = 'There was an error uploading your file'
# display success/failure message
st.text(message)
# display uploaded file (text only as the method used in docx2python is .text)
try:
st.text(doc)
except Exception as e:
print(e)
st.write('Please upload CV')
if doc != '':
# call recommender function
recommended_jobs = recommender(model_df, doc)
# create output dataframe
recommended_jobs = pd.merge(left=recommended_jobs,
right=workpac_data,
left_on='job_link',
right_on='job_link')
# summarise output
recommender_summary = recommended_jobs[['job_title', 'job_link']]
# display output
fig = go.Figure(data=[
go.Table(header=dict(values=list(recommender_summary.columns),
fill_color='paleturquoise',
align='left'),
cells=dict(values=[
recommender_summary.job_title,
recommender_summary.job_link
],
fill_color='lavender',
align='left'))
])
st.write(fig)
"""This script uses the docx2python module, which extracts .docx headers,
footers, text, footnotes, endnotes, properties, and images to a Python object."""
from docx2python import docx2python
#Show file properties
print("Properties:")
print(docx2python('./Lab7.docx').properties)
print('\n')
print("Press enter to continue")
keypress = input()
print('\n')
#Show Header
print("Header:")
print(docx2python('./Lab7.docx').header)
print('\n')
print("Press enter to continue")
keypress = input()
print('\n')
#Show Footer
print("Footer:")
print(docx2python('./Lab7.docx').footer)
print('\n')
new_file_abs = re.sub(r'\.\w+$', '.docx', new_file_abs)
word.ActiveDocument.SaveAs(new_file_abs, FileFormat=constants.wdFormatXMLDocument)
doc.Close(False)
file = file+"x"
return file, True
path = r"C:\\PATH\\TO\\FILES\\"
for file in os.listdir(path):
if !file.endswith(".doc") and !file.endswith(".docx"):
continue
newFileCreated = False
if file.endswith(".doc"):
file, newFileCreated = save_as_docx(path,file)
filepath = path+file
content = docx2python(filepath)
lines = list(iter_paragraphs(content.document))
# if a new docx file was created, you want to delete it after getting the contents out
if newFileCreated == True:
os.remove(file)
'''
Do the file processing here
'''
print('hello')
# hi cam
from pathlib import Path
from docx2python import docx2python
word_doc = Path(
"J:\C04100_C04199\C04147_5_Semple_St_Porirua_Wellington\C04147100_Due_Diligence\007_Work\Reporting\DSI\C04147100R001_FINAL.docx"
)
print(word_doc)
word = docx2python('C04147100R001_FINAL.docx')
text = word.body
exec = text[2]
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from docx2python import docx2python
import pandas as pd
# reading word file
doc_data = docx2python('Carbonemissiondata.docx')
# get separate components of the document
headings = [
"per capita CO2 (kg per person)", "per capita CO (kg per person)",
"per capita CH4 (kg per person)"
]
# making our dataframe using pandas
df = pd.DataFrame(doc_data.body[0][1:]). \
applymap(lambda val: val[0].strip("\t"))
# retrieving original first row (columns headings)
df.columns = [val[0].strip("\t") for val in doc_data.body[0][0]]
# converting columns read from word file to float since It was found that docx 2 returns tables data as string
for i in range(3):
df[headings[i]] = df[headings[i]].astype(float)
# we will work on col. 2 and 3
X = df.iloc[:, [2, 3]].values
wcss = []
# using elbow method to detect optimum clusters numbers
from docx2python import docx2python
from docx import Document
from docx import table
from functions import writeToFile
document1 = docx2python("D:\\PycharmProjects\\docfilefinder\\MASTERRYANLABELFILE.docx")
document2 = docx2python("D:\\PycharmProjects\\docfilefinder\\RYANSTORELABELS.docx")
doc2body = document2.body
body = document1.body
count = 0
doc1list = dict()
for innerbody in body:
for row in innerbody:
if row[0][0] == "":
continue
doc1list[row[0][0]] = row[0]
doc1list[row[2][0]] = row[2]
doc1list[row[4][0]] = row
count += 1
print("Document 1: {MASTERRYANLABELFILE} List", len(doc1list), "Count : ", count, "Check", count * 3)
# for Document 2
count = 0
doc2list = dict()
for innerbody in doc2body:
for row in innerbody:
if row[0][0] == "":
continue
def loadDocx(doc):
return docx2python(doc)
def extract_image(resume):
result = docx2python(resume)
count = 0
for name, image in result.images.items():
count += 1
return count
from docx2python import docx2python
from XmlConverterAPI import Structure, Converter
import re
file = docx2python('Files/ForDocx.docx')
list = []
pattern = '\[|\]|\''
regex = re.compile(r'{}'.format(pattern))
doc = str(file.document).split("',")
for line in doc:
line = regex.sub('', line)
if '.' not in line and (line.count(' ')) <= 9:
list.append(Structure(name=Converter.BLOCK, value=line))
else:
list.append(Structure(name=Converter.TEXT, value=line))
Converter.createXML(list, "FromDocx_XML.xml")
