def download_tweet_labels_file():
lines = stats_generator.generate_text_with_report_by_tweets(
mmu.get_df_with_all().sort_values('insert_time'))
with open('labels.txt', 'w') as the_file:
for line in lines:
the_file.write(line + '\n')
return send_from_directory(directory='', filename="labels.txt")
def get_stats_heatmap():
flights = mmu.get_df_with_all()
flights = pd.pivot_table(flights,
values='tweet_id',
index=['label'],
columns=['username'],
aggfunc=len)
fig, ax = plt.subplots(figsize=(12, 3))
sns.heatmap(flights, annot=True, fmt=".0f")
fig.savefig('output.png')
return send_file("output.png", mimetype='image/gif', cache_timeout=0)
def get_labelled_tweets():
tweets = mmu.get_df_with_all()
tweets = tweets[tweets.label != ''].sort_values(by=['update_time'],
ascending=False)
list_to_return = []
for index, row in tweets.iterrows():
d = row.to_dict()
d['update_time'] = d['update_time'].isoformat()
d['note'] = ''
d['tweet'] = d['tweet_content']
d.pop('_id', None)
d.pop('tweet_content', None)
list_to_return.append(d)
return jsonify({'tweets': list_to_return})
def get_stats_detailed():
tweets = mmu.get_df_with_all()
return jsonify(group_by_counts(tweets))
def download():
mmu.get_df_with_all().to_json('results.json')
return send_from_directory(directory='', filename="results.json")