def extract_article_callback(update: Update, context: CallbackContext):
cid = update.callback_query.message.chat.id
mid = update.callback_query.message.message_id
text = update.callback_query.message.text
q = context.chat_data['query']
section_name = context.chat_data['current_section']
storage_article = context.chat_data[section_name].get(q)
context.bot.edit_message_reply_markup(message_id=mid,
chat_id=cid,
reply_markup=muting_kb)
context.bot.answerCallbackQuery(callback_query_id=update.callback_query.id,
text=answer_query_text)
toolkit = Toolkit()
if storage_article is not None and storage_article.get(
'extract_article_file'):
context.bot.edit_message_reply_markup(message_id=mid,
chat_id=cid,
reply_markup=None)
file = context.bot.send_document(
chat_id=cid,
document=storage_article.get('extract_article_file'),
reply_markup=kb_dict[section_name])
context.chat_data.update({'messages_ids': [file.message_id]})
return
else:
context.chat_data[section_name].update({q: {'url': q}})
article_info = toolkit.extract_article_info(q)
current_article = {
'title': article_info.get('title', 'unknown'),
'text': article_info.get('article', 'unknown'),
'author': article_info.get('author', 'unknown'),
'date': article_info.get('publishDate', 'unknown'),
'tags': article_info.get('tags', 'unknown')
}
name = f'url_extract_article_{cid}'
path = toolkit.create_document(current_article, name)
context.bot.edit_message_reply_markup(message_id=mid,
chat_id=cid,
reply_markup=None)
file = context.bot.send_document(chat_id=cid,
document=open(path, 'rb'),
reply_markup=kb_dict[section_name])
context.chat_data.update({'file_ids': [file.message_id]})
context.chat_data[section_name][q].update(
{'extract_article_file': file.document.file_id})
os.remove(path)
def extract_entity_callback(update: Update, context: CallbackContext):
cid = update.callback_query.message.chat.id
mid = update.callback_query.message.message_id
text = update.callback_query.message.text
q = context.chat_data['query']
section_name = context.chat_data['current_section']
storage_article = context.chat_data[section_name].get(q)
toolkit = Toolkit()
context.bot.edit_message_reply_markup(message_id=mid,
chat_id=cid,
reply_markup=muting_kb)
context.bot.answerCallbackQuery(callback_query_id=update.callback_query.id,
text=answer_query_text)
if storage_article is not None and storage_article.get('entity_file'):
context.bot.edit_message_reply_markup(message_id=mid,
chat_id=cid,
reply_markup=None)
file = context.bot.send_document(
chat_id=cid,
document=storage_article.get('entity_file'),
reply_markup=kb_dict[section_name])
context.chat_data.update({'messages_ids': [file.message_id]})
return
else:
if validators.url(q):
context.chat_data[section_name].update({q: {'url': q}})
article_info = toolkit.extract_entity({'url': q})['entities']
else:
context.chat_data[section_name].update({q: {'text': q}})
try:
article_info = toolkit.extract_entity({'text': q})['entities']
except:
update.callback_query.message.reply_text(nothing_text)
return
current_article = article_info
name = f'url_extract_entity_{cid}'
path = toolkit.create_document(current_article, name)
context.bot.edit_message_reply_markup(message_id=mid,
chat_id=cid,
reply_markup=None)
file = context.bot.send_document(chat_id=cid,
document=open(path, 'rb'),
reply_markup=kb_dict[section_name])
context.chat_data.update({'file_ids': [file.message_id]})
context.chat_data[section_name][q].update(
{'entity_file': file.document.file_id})
os.remove(path)
def classify_callback(update: Update, context: CallbackContext):
cid = update.callback_query.message.chat.id
mid = update.callback_query.message.message_id
text = update.callback_query.message.text
q = context.chat_data['query']
section_name = context.chat_data['current_section']
storage_article = context.chat_data[section_name].get(q)
toolkit = Toolkit()
context.bot.edit_message_reply_markup(message_id=mid,
chat_id=cid,
reply_markup=muting_kb)
context.bot.answerCallbackQuery(callback_query_id=update.callback_query.id,
text=answer_query_text)
if storage_article is not None and storage_article.get('categories'):
current_article = storage_article
else:
if validators.url(q):
context.chat_data[section_name].update({q: {'url': q}})
current_article = {'url': q}
else:
context.chat_data[section_name].update({q: {'text': q}})
current_article = {'text': q}
summarized_categories = current_article.get('categories')
if summarized_categories is None:
summarized_categories = toolkit.classify(current_article)['categories']
context.chat_data[section_name][q].update(
{'categories': summarized_categories})
convert_summarized_categories = categories_title
if not summarized_categories:
mid = context.bot.send_message(chat_id=cid,
text=nothing_text,
parse_mode=ParseMode.HTML)
context.chat_data.update({'file_ids': [mid.message_id]})
return
for index, value in enumerate(summarized_categories):
convert_summarized_categories += categories_text.format(
index + 1, value['label'], value['confidence'])
context.bot.edit_message_reply_markup(message_id=mid,
chat_id=cid,
reply_markup=None)
context.bot.send_message(chat_id=cid,
text=convert_summarized_categories,
reply_markup=kb_dict[section_name],
parse_mode=ParseMode.HTML)
def extract_sentiment_callback(update: Update, context: CallbackContext):
cid = update.callback_query.message.chat.id
mid = update.callback_query.message.message_id
text = update.callback_query.message.text
q = context.chat_data['query']
section_name = context.chat_data['current_section']
storage_article = context.chat_data[section_name].get(q)
toolkit = Toolkit()
context.bot.edit_message_reply_markup(message_id=mid,
chat_id=cid,
reply_markup=muting_kb)
context.bot.answerCallbackQuery(callback_query_id=update.callback_query.id,
text=answer_query_text)
if storage_article is not None and storage_article.get('polarity'):
current_article = {
'polarity': storage_article['polarity'],
'polarity_confidence': storage_article['polarity_confidence'],
'subjectivity': storage_article['subjectivity'],
'subjectivity_confidence':
storage_article['subjectivity_confidence']
}
else:
if validators.url(q):
context.chat_data[section_name].update({q: {'url': q}})
article_info = toolkit.extract_sentiment({'url': q})
else:
context.chat_data[section_name].update({q: {'text': q}})
article_info = toolkit.extract_sentiment({'text': q})
current_article = {
'polarity': article_info['polarity'],
'polarity_confidence': article_info['polarity_confidence'],
'subjectivity': article_info['subjectivity'],
'subjectivity_confidence': article_info['subjectivity_confidence']
}
context.chat_data[section_name][q].update(current_article)
summarize_sentiment = sentiment_title
for key, value in current_article.items():
key = key.replace('_', ' ')
summarize_sentiment += sentiment_text.format(key, value)
context.bot.edit_message_reply_markup(message_id=mid,
chat_id=cid,
reply_markup=None)
mid = context.bot.send_message(chat_id=cid,
text=summarize_sentiment,
reply_markup=kb_dict[section_name],
parse_mode=ParseMode.HTML)
context.chat_data.update({'file_ids': [mid.message_id]})
def hashtag_callback(update: Update, context: CallbackContext):
cid = update.callback_query.message.chat.id
mid = update.callback_query.message.message_id
text = update.callback_query.message.text
q = context.chat_data['query']
section_name = context.chat_data['current_section']
storage_article = context.chat_data[section_name].get(q)
toolkit = Toolkit()
context.bot.edit_message_reply_markup(message_id=mid,
chat_id=cid,
reply_markup=muting_kb)
context.bot.answerCallbackQuery(callback_query_id=update.callback_query.id,
text=answer_query_text)
if storage_article is not None and storage_article.get('hashtags'):
current_article = storage_article
else:
if validators.url(q):
context.chat_data[section_name].update({q: {'url': q}})
current_article = {'url': q}
else:
context.chat_data[section_name].update({q: {'text': q}})
current_article = {'text': q}
summarized_hashtags = current_article.get('hashtags')
if summarized_hashtags is None:
summarized_hashtags = toolkit.suggest_hashtag(
current_article)['hashtags']
context.chat_data[section_name][q].update(
{'hashtags': summarized_hashtags})
convert_summarized_hashtags = hashtags_title
for index, value in enumerate(summarized_hashtags):
convert_summarized_hashtags += hashtags_text.format(index + 1, value)
convert_summarized_hashtags += f'\n{"".join(summarized_hashtags)}'
context.bot.edit_message_reply_markup(message_id=mid,
chat_id=cid,
reply_markup=None)
mid = context.bot.send_message(
chat_id=cid,
text=convert_summarized_hashtags,
parse_mode=ParseMode.HTML,
reply_markup=kb_dict[section_name],
)
context.chat_data.update({'file_ids': [mid.message_id]})
def summarize_file_callback(update: Update, context: CallbackContext):
cid = update.callback_query.message.chat.id
mid = update.callback_query.message.message_id
text = update.callback_query.message.text
q = context.chat_data['query']
file_name = q['file_name']
file_id = q['file_id']
section_name = context.chat_data['current_section']
storage_article = context.chat_data[section_name].get(file_name)
toolkit = Toolkit()
context.bot.edit_message_reply_markup(message_id=mid,
chat_id=cid,
reply_markup=muting_kb)
context.bot.answerCallbackQuery(callback_query_id=update.callback_query.id,
text=answer_query_text)
if storage_article is not None and storage_article.get('sentences'):
current_article = storage_article
else:
path = f'{os.getcwd()}/doc_storage/{file_name}'
with open(path, 'wb') as f:
context.bot.get_file(file_id).download(out=f)
content = str(textract.process(path))[2:]
context.chat_data[section_name].update(
{file_name: {
'file_id': file_id
}})
current_article = {'text': content}
os.remove(path)
summarized_sentences = current_article.get('sentences', [])
if not summarized_sentences:
text_parts, title = get_parts_of_text(current_article['text'])
for part in text_parts:
try:
summarized_sentences += toolkit.summarize({
'text': part,
'title': title
})['sentences']
except:
update.callback_query.message.reply_text(error_text)
context.chat_data[section_name][file_name].update(
{'sentences': summarized_sentences})
name = f'file_summarize_{cid}'
path = toolkit.create_document({'sentences': summarized_sentences}, name)
context.bot.edit_message_reply_markup(message_id=mid,
chat_id=cid,
reply_markup=None)
file = context.bot.send_document(chat_id=cid,
document=open(path, 'rb'),
reply_markup=kb_dict[section_name])
context.chat_data.update({'file_ids': [file.message_id]})
context.chat_data[section_name][file_name].update(
{'summarize_file': file.document.file_id})
os.remove(path)
def extract_sentiment_file_callback(update: Update, context: CallbackContext):
cid = update.callback_query.message.chat.id
mid = update.callback_query.message.message_id
text = update.callback_query.message.text
q = context.chat_data['query']
file_name = q['file_name']
file_id = q['file_id']
section_name = context.chat_data['current_section']
storage_article = context.chat_data[section_name].get(file_name)
toolkit = Toolkit()
context.bot.edit_message_reply_markup(message_id=mid,
chat_id=cid,
reply_markup=muting_kb)
context.bot.answerCallbackQuery(callback_query_id=update.callback_query.id,
text=answer_query_text)
if storage_article is not None and storage_article.get('polarity'):
current_article = {
'polarity': storage_article['polarity'],
'polarity_confidence': storage_article['polarity_confidence'],
'subjectivity': storage_article['subjectivity'],
'subjectivity_confidence':
storage_article['subjectivity_confidence']
}
else:
path = f'{os.getcwd()}/doc_storage/{file_name}'
with open(path, 'wb') as f:
context.bot.get_file(file_id).download(out=f)
content = str(textract.process(path))[:5000]
context.chat_data[section_name].update(
{file_name: {
'file_id': file_id
}})
article_info = toolkit.extract_sentiment({'text': content})
current_article = {
'polarity': article_info['polarity'],
'polarity_confidence': article_info['polarity_confidence'],
'subjectivity': article_info['subjectivity'],
'subjectivity_confidence': article_info['subjectivity_confidence']
}
os.remove(path)
context.chat_data[section_name][file_name].update(current_article)
summarize_sentiment = sentiment_title
for key, value in current_article.items():
key = key.replace('_', ' ')
summarize_sentiment += sentiment_text.format(key, value)
context.bot.edit_message_reply_markup(message_id=mid,
chat_id=cid,
reply_markup=None)
mid = context.bot.send_message(chat_id=cid,
text=summarize_sentiment,
reply_markup=kb_dict[section_name],
parse_mode=ParseMode.HTML)
context.chat_data.update({'file_ids': [mid.message_id]})
def extract_entity_file_callback(update: Update, context: CallbackContext):
cid = update.callback_query.message.chat.id
mid = update.callback_query.message.message_id
text = update.callback_query.message.text
q = context.chat_data['query']
file_name = q['file_name']
file_id = q['file_id']
section_name = context.chat_data['current_section']
storage_article = context.chat_data[section_name].get(file_name)
toolkit = Toolkit()
context.bot.edit_message_reply_markup(message_id=mid,
chat_id=cid,
reply_markup=muting_kb)
context.bot.answerCallbackQuery(callback_query_id=update.callback_query.id,
text=answer_query_text)
if storage_article is not None and storage_article.get('entity_file'):
context.bot.edit_message_reply_markup(message_id=mid,
chat_id=cid,
text=text,
reply_markup=None)
file = context.bot.send_document(
chat_id=cid,
document=storage_article.get('entity_file'),
reply_markup=kb_dict[section_name])
context.chat_data.update({'file_ids': [file.message_id]})
return
path = f'{os.getcwd()}/doc_storage/{file_name}'
with open(path, 'wb') as f:
context.bot.get_file(file_id).download(out=f)
content = str(textract.process(path))[2:]
context.chat_data[section_name].update({file_name: {'file_id': file_id}})
current_article = {'text': content}
os.remove(path)
text_parts, title = get_parts_of_text(current_article['text'])
summarize_entities = {}
for part in text_parts:
try:
part_entity = toolkit.extract_entity({
'text': part,
'title': title
})['entities']
except:
update.callback_query.message.reply_text(error_text)
return
for name, value in part_entity.items():
if summarize_entities.get(name):
summarize_entities[name] = list(
set(summarize_entities[name] + value))
else:
summarize_entities.update({name: value})
name = f'file_extract_entity_{cid}'
path = toolkit.create_document(summarize_entities, name)
context.bot.edit_message_reply_markup(message_id=mid,
chat_id=cid,
reply_markup=None)
file = context.bot.send_document(chat_id=cid,
document=open(path, 'rb'),
reply_markup=kb_dict[section_name])
context.chat_data.update({'file_ids': [file.message_id]})
context.chat_data[section_name][file_name].update(
{'entity_file': file.document.file_id})
os.remove(path)