import zipfile

import xml.etree.ElementTree as ET

import pandas as pd

import os

import matplotlib.pyplot as plt

class RE:

def __init__(self):

self.bold_text = []

os.chdir('/home/saul/Desktop/generative-AI/RE/')

print(os.getcwd())

def get_bold_text_from_odt(self, odt_file):

print(odt_file)

#bold_text = []

# Open the ODT file as a zip archive

with zipfile.ZipFile(odt_file, 'r') as odt_zip:

print('ODT_ZIP ', odt_zip)

# Extract content.xml from the ODT archive

with odt_zip.open('content.xml') as content_file:

# Parse content.xml using ElementTree

tree = ET.parse(content_file)

root = tree.getroot()

print('Root Tag ', root.tag)

print('Root Attrib ', root.attrib)

#for body in root.iter('{urn:oasis:names:tc:opendocument:xmlns:text:1.0}body'):

for child in root:

print(child.tag, child.attrib)

for body in child.iter('{urn:oasis:names:tc:opendocument:xmlns:office:1.0}body'):

print('BODY ', body)

print(body.attrib.get('{urn:oasis:names:tc:opendocument:xmlns:office:1.0}body', ''))

# Find all text:span elements with a text:style-name attribute containing "emphasis"

for span in root.iter('{urn:oasis:names:tc:opendocument:xmlns:text:1.0}span'):

#print('span :', span)

#print('root :', root)

style_name = span.attrib.get('{urn:oasis:names:tc:opendocument:xmlns:text:1.0}style-name', '')

#unknown = root.attrib.get('{urn:oasis:names:tc:opendocument:xmlns:office:1.0}document-content')

#print('unkown :', unknown)

#print('style_name :', style_name.lower())

if 'strong' in style_name.lower():

# Get the text content of the span

text = ''.join(span.itertext())

#print('Text :', text)

self.bold_text.append(text)

print(self.bold_text)

#subject_freqs = self.__get_subject_freq(self.bold_text)

return self.bold_text

def __get_subject_freq(self, topics):

frequency = {}

# iterating over the list

for item in topics:

# checking the element in dictionary

if item in frequency:

# incrementing the counr

frequency[item] += 1

else:

# initializing the count

frequency[item] = 1

#sort frequencies

sorted_freq = sorted(frequency.items(), key=lambda x:x[1], reverse=True)

sorted_freq = dict(sorted_freq )

#self.__convert_dict_to_dataframe(sorted_freq)

#printing the frequency

#print(sorted_freq)

return sorted_freq

def __convert_dict_to_dataframe(self, freq):

#print(freq)

freq_dataframe = pd.DataFrame(list(freq.items()))

freq_dataframe.columns =['Name', 'Freq']

print(freq_dataframe.head())

#freq_dataframe.to_csv('subject_freq.csv', index=False)

#freq_dataframe.to_csv('cb_freq.csv', index=False)

#os.chdir('/home/saul/Desktop/generative-AI/RE/')

#

#odt_file = 'Gen_AI_Real_estate.odt'

#odt_file = 'use_of_cb_in_re_AU.odt'

odt_file = 'USE_CASE_1.odt'

re = RE()

strong_in_odt = re.get_bold_text_from_odt(odt_file)

#print(strong_in_odt)