from utils.utilities import Utilities
from preprocessing.event_extractor import EventExtractor
import time
if __name__ == '__main__':
# Original Code
#
# utilities = Utilities()
# event_extraction = EventExtractor()
# #
# # event_extraction.save_texts_in_file()
#
# events = event_extraction.extract_events()
#
# for event in events:
# if len(event['event_phrases']) > 0:
# utilities.save_or_append_in_csv(event, 'events.csv')
utilities = Utilities()
event_extractor = EventExtractor()
tweet_rows = event_extractor.get_unique_tweets()
tweet_sentences = event_extractor.get_tweet_sentences(tweet_rows)
events = event_extractor.extract_events2(tweet_sentences)
events = sorted(events,
key=lambda x: time.strptime(x[0], '%d-%m-%Y %H:%M'))
utilities.save_or_append_list_as_csv(events, 'events2.csv')
header = tweet_rows[0]
del tweet_rows[0]
count = 0
tweet_causal_rows = []
for tweet_row in tweet_rows:
tweet = preprocessor.preprocess(tweet_row[header.index('text')])
sentences = sent_tokenize(tweet)
# tweet_causal_pairs = []
for sentence in sentences:
sentence_causal_pair = apply_causal_rules(sentence)
if sentence_causal_pair is not None:
tweet_causal_rows.append(
[tweet_row, sentence, sentence_causal_pair])
# print(sentence)
# print(sentence_causal_pair)
# if len(sentence_causal_pair)>1:
# print(sentence)
# print(sentence_causal_pair)
# print(len(sentence_causal_pair))
# count += 1
# event_pair = extract_sentence_event_pairs(list(sentence_causal_pair))
# if len(event_pair) > 0:
# event_pairs.append(event_pair)
# print(event_pair)
# print(count)
utilities.save_or_append_list_as_csv(tweet_causal_rows, 'causal_pairs.csv')
# event_pairs = extract_batch_sentence_event_pairs(tweet_causal_rows)