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dash_mess.py
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# set chdir to current dir
import os
import sys
sys.path.insert(0, os.path.realpath(os.path.dirname(__file__)))
os.chdir(os.path.realpath(os.path.dirname(__file__)))
import dash
from dash.dependencies import Output, Event, Input
import dash_core_components as dcc
import dash_html_components as html
import plotly
import plotly.graph_objs as go
import sqlite3
import pandas as pd
from collections import Counter
import string
import regex as re
from cache import cache
from config import stop_words
import time
import pickle
# it's ok to use one shared sqlite connection
# as we are making selects only, no need for any kind of serialization as well
conn = sqlite3.connect('twitter.db', check_same_thread=False)
punctuation = [str(i) for i in string.punctuation]
sentiment_colors = {-1:"#EE6055",
-0.5:"#FDE74C",
0:"#FFE6AC",
0.5:"#D0F2DF",
1:"#9CEC5B",}
app_colors = {
'background': '#0C0F0A',
'text': '#FFFFFF',
'sentiment-plot':'#41EAD4',
'volume-bar':'#FBFC74',
'someothercolor':'#FF206E',
}
POS_NEG_NEUT = 0.1
MAX_DF_LENGTH = 100
app = dash.Dash(__name__)
app.layout = html.Div(
[ html.Div(className='container-fluid', children=[html.H2('Live Twitter Sentiment', style={'color':"#CECECE"}),
html.H5('Search:', style={'color':app_colors['text']}),
dcc.Input(id='sentiment_term', value='twitter', type='text', style={'color':app_colors['someothercolor']}),
],
style={'width':'98%','margin-left':10,'margin-right':10,'max-width':50000}),
html.Div(className='row', children=[html.Div(id='related-sentiment', children=html.Button('Loading related terms...', id='related_term_button'), className='col s12 m6 l6', style={"word-wrap":"break-word"}),
html.Div(id='recent-trending', className='col s12 m6 l6', style={"word-wrap":"break-word"})]),
html.Div(className='row', children=[html.Div(dcc.Graph(id='live-graph', animate=False), className='col s12 m6 l6'),
html.Div(dcc.Graph(id='historical-graph', animate=False), className='col s12 m6 l6')]),
html.Div(className='row', children=[html.Div(id="recent-tweets-table", className='col s12 m6 l6'),
html.Div(dcc.Graph(id='sentiment-pie', animate=False), className='col s12 m6 l6'),]),
dcc.Interval(
id='graph-update',
interval=1*1000
),
dcc.Interval(
id='historical-update',
interval=60*1000
),
dcc.Interval(
id='related-update',
interval=30*1000
),
dcc.Interval(
id='recent-table-update',
interval=2*1000
),
dcc.Interval(
id='sentiment-pie-update',
interval=60*1000
),
], style={'backgroundColor': app_colors['background'], 'margin-top':'-30px', 'height':'2000px',},
)
def df_resample_sizes(df, maxlen=MAX_DF_LENGTH):
df_len = len(df)
resample_amt = 100
vol_df = df.copy()
vol_df['volume'] = 1
ms_span = (df.index[-1] - df.index[0]).seconds * 1000
rs = int(ms_span / maxlen)
df = df.resample('{}ms'.format(int(rs))).mean()
df.dropna(inplace=True)
vol_df = vol_df.resample('{}ms'.format(int(rs))).sum()
vol_df.dropna(inplace=True)
df = df.join(vol_df['volume'])
return df
# make a counter with blacklist words and empty word with some big value - we'll use it later to filter counter
stop_words.append('')
blacklist_counter = Counter(dict(zip(stop_words, [1000000]*len(stop_words))))
# complie a regex for split operations (punctuation list, plus space and new line)
split_regex = re.compile("[ \n"+re.escape("".join(punctuation))+']')
def related_sentiments(df, sentiment_term, how_many=15):
try:
related_words = {}
# it's way faster to join strings to one string then use regex split using your punctuation list plus space and new line chars
# regex precomiled above
tokens = split_regex.split(' '.join(df['tweet'].values.tolist()).lower())
# it is way faster to remove stop_words, sentiment_term and empty token by making another counter
# with some big value and substracting (counter will substract and remove tokens with negative count)
blacklist_counter_with_term = blacklist_counter.copy()
blacklist_counter_with_term[sentiment_term] = 1000000
counts = (Counter(tokens) - blacklist_counter_with_term).most_common(15)
for term,count in counts:
try:
df = pd.read_sql("SELECT sentiment.* FROM sentiment_fts fts LEFT JOIN sentiment ON fts.rowid = sentiment.id WHERE fts.sentiment_fts MATCH ? ORDER BY fts.rowid DESC LIMIT 200", conn, params=(term,))
related_words[term] = [df['sentiment'].mean(), count]
except Exception as e:
with open('errors.txt','a') as f:
f.write(str(e))
f.write('\n')
return related_words
except Exception as e:
with open('errors.txt','a') as f:
f.write(str(e))
f.write('\n')
def quick_color(s):
# except return bg as app_colors['background']
if s >= POS_NEG_NEUT:
# positive
return "#002C0D"
elif s <= -POS_NEG_NEUT:
# negative:
return "#270000"
else:
return app_colors['background']
def generate_table(df, max_rows=10):
return html.Table(className="responsive-table",
children=[
html.Thead(
html.Tr(
children=[
html.Th(col.title()) for col in df.columns.values],
style={'color':app_colors['text']}
)
),
html.Tbody(
[
html.Tr(
children=[
html.Td(data) for data in d
], style={'color':app_colors['text'],
'background-color':quick_color(d[2])}
)
for d in df.values.tolist()])
]
)
def pos_neg_neutral(col):
if col >= POS_NEG_NEUT:
# positive
return 1
elif col <= -POS_NEG_NEUT:
# negative:
return -1
else:
return 0
@app.callback(Output('recent-tweets-table', 'children'),
[Input(component_id='sentiment_term', component_property='value')],
events=[Event('recent-table-update', 'interval')])
def update_recent_tweets(sentiment_term):
if sentiment_term:
df = pd.read_sql("SELECT sentiment.* FROM sentiment_fts fts LEFT JOIN sentiment ON fts.rowid = sentiment.id WHERE fts.sentiment_fts MATCH ? ORDER BY fts.rowid DESC LIMIT 10", conn, params=(sentiment_term+'*',))
else:
df = pd.read_sql("SELECT * FROM sentiment ORDER BY id DESC, unix DESC LIMIT 10", conn)
df['date'] = pd.to_datetime(df['unix'], unit='ms')
df = df.drop(['unix','id'], axis=1)
df = df[['date','tweet','sentiment']]
return generate_table(df, max_rows=10)
@app.callback(Output('sentiment-pie', 'figure'),
[Input(component_id='sentiment_term', component_property='value')],
events=[Event('sentiment-pie-update', 'interval')])
def update_pie_chart(sentiment_term):
# get data from cache
for i in range(100):
sentiment_pie_dict = cache.get('sentiment_shares', sentiment_term)
if sentiment_pie_dict:
break
time.sleep(0.1)
if not sentiment_pie_dict:
return None
labels = ['Positive','Negative']
try: pos = sentiment_pie_dict[1]
except: pos = 0
try: neg = sentiment_pie_dict[-1]
except: neg = 0
values = [pos,neg]
colors = ['#007F25', '#800000']
trace = go.Pie(labels=labels, values=values,
hoverinfo='label+percent', textinfo='value',
textfont=dict(size=20, color=app_colors['text']),
marker=dict(colors=colors,
line=dict(color=app_colors['background'], width=2)))
return {"data":[trace],'layout' : go.Layout(
title='Positive vs Negative sentiment for "{}" (longer-term)'.format(sentiment_term),
font={'color':app_colors['text']},
plot_bgcolor = app_colors['background'],
paper_bgcolor = app_colors['background'],
showlegend=True)}
@app.callback(Output('live-graph', 'figure'),
[Input(component_id='sentiment_term', component_property='value')],
events=[Event('graph-update', 'interval')])
def update_graph_scatter(sentiment_term):
try:
if sentiment_term:
df = pd.read_sql("SELECT sentiment.* FROM sentiment_fts fts LEFT JOIN sentiment ON fts.rowid = sentiment.id WHERE fts.sentiment_fts MATCH ? ORDER BY fts.rowid DESC LIMIT 1000", conn, params=(sentiment_term+'*',))
else:
df = pd.read_sql("SELECT * FROM sentiment ORDER BY id DESC, unix DESC LIMIT 1000", conn)
df.sort_values('unix', inplace=True)
df['date'] = pd.to_datetime(df['unix'], unit='ms')
df.set_index('date', inplace=True)
init_length = len(df)
df['sentiment_smoothed'] = df['sentiment'].rolling(int(len(df)/5)).mean()
df = df_resample_sizes(df)
X = df.index
Y = df.sentiment_smoothed.values
Y2 = df.volume.values
data = plotly.graph_objs.Scatter(
x=X,
y=Y,
name='Sentiment',
mode= 'lines',
yaxis='y2',
line = dict(color = (app_colors['sentiment-plot']),
width = 4,)
)
data2 = plotly.graph_objs.Bar(
x=X,
y=Y2,
name='Volume',
marker=dict(color=app_colors['volume-bar']),
)
return {'data': [data,data2],'layout' : go.Layout(xaxis=dict(range=[min(X),max(X)]),
yaxis=dict(range=[min(Y2),max(Y2*4)], title='Volume', side='right'),
yaxis2=dict(range=[min(Y),max(Y)], side='left', overlaying='y',title='sentiment'),
title='Live sentiment for: "{}"'.format(sentiment_term),
font={'color':app_colors['text']},
plot_bgcolor = app_colors['background'],
paper_bgcolor = app_colors['background'],
showlegend=False)}
except Exception as e:
with open('errors.txt','a') as f:
f.write(str(e))
f.write('\n')
@app.callback(Output('historical-graph', 'figure'),
[Input(component_id='sentiment_term', component_property='value'),
],
events=[Event('historical-update', 'interval')])
def update_hist_graph_scatter(sentiment_term):
try:
if sentiment_term:
df = pd.read_sql("SELECT sentiment.* FROM sentiment_fts fts LEFT JOIN sentiment ON fts.rowid = sentiment.id WHERE fts.sentiment_fts MATCH ? ORDER BY fts.rowid DESC LIMIT 10000", conn, params=(sentiment_term+'*',))
else:
df = pd.read_sql("SELECT * FROM sentiment ORDER BY id DESC, unix DESC LIMIT 10000", conn)
df.sort_values('unix', inplace=True)
df['date'] = pd.to_datetime(df['unix'], unit='ms')
df.set_index('date', inplace=True)
# save this to a file, then have another function that
# updates because of this, using intervals to read the file.
# https://community.plot.ly/t/multiple-outputs-from-single-input-with-one-callback/4970
# store related sentiments in cache
cache.set('related_terms', sentiment_term, related_sentiments(df, sentiment_term), 120)
#print(related_sentiments(df,sentiment_term), sentiment_term)
init_length = len(df)
df['sentiment_smoothed'] = df['sentiment'].rolling(int(len(df)/5)).mean()
df.dropna(inplace=True)
df = df_resample_sizes(df,maxlen=500)
X = df.index
Y = df.sentiment_smoothed.values
Y2 = df.volume.values
data = plotly.graph_objs.Scatter(
x=X,
y=Y,
name='Sentiment',
mode= 'lines',
yaxis='y2',
line = dict(color = (app_colors['sentiment-plot']),
width = 4,)
)
data2 = plotly.graph_objs.Bar(
x=X,
y=Y2,
name='Volume',
marker=dict(color=app_colors['volume-bar']),
)
df['sentiment_shares'] = list(map(pos_neg_neutral, df['sentiment']))
#sentiment_shares = dict(df['sentiment_shares'].value_counts())
cache.set('sentiment_shares', sentiment_term, dict(df['sentiment_shares'].value_counts()), 120)
return {'data': [data,data2],'layout' : go.Layout(xaxis=dict(range=[min(X),max(X)]), # add type='category to remove gaps'
yaxis=dict(range=[min(Y2),max(Y2*4)], title='Volume', side='right'),
yaxis2=dict(range=[min(Y),max(Y)], side='left', overlaying='y',title='sentiment'),
title='Longer-term sentiment for: "{}"'.format(sentiment_term),
font={'color':app_colors['text']},
plot_bgcolor = app_colors['background'],
paper_bgcolor = app_colors['background'],
showlegend=False)}
except Exception as e:
with open('errors.txt','a') as f:
f.write(str(e))
f.write('\n')
max_size_change = .4
def generate_size(value, smin, smax):
size_change = round((( (value-smin) /smax)*2) - 1,2)
final_size = (size_change*max_size_change) + 1
return final_size*120
# SINCE A SINGLE FUNCTION CANNOT UPDATE MULTIPLE OUTPUTS...
#https://community.plot.ly/t/multiple-outputs-from-single-input-with-one-callback/4970
@app.callback(Output('related-sentiment', 'children'),
[Input(component_id='sentiment_term', component_property='value')],
events=[Event('related-update', 'interval')])
def update_related_terms(sentiment_term):
try:
# get data from cache
for i in range(100):
related_terms = cache.get('related_terms', sentiment_term) # term: {mean sentiment, count}
if related_terms:
break
time.sleep(0.1)
if not related_terms:
return None
buttons = [html.Button('{}({})'.format(term, related_terms[term][1]), id='related_term_button', value=term, className='btn', type='submit', style={'background-color':'#4CBFE1',
'margin-right':'5px',
'margin-top':'5px'}) for term in related_terms]
#size: related_terms[term][1], sentiment related_terms[term][0]
sizes = [related_terms[term][1] for term in related_terms]
smin = min(sizes)
smax = max(sizes) - smin
buttons = [html.H5('Terms related to "{}": '.format(sentiment_term), style={'color':app_colors['text']})]+[html.Span(term, style={'color':sentiment_colors[round(related_terms[term][0]*2)/2],
'margin-right':'15px',
'margin-top':'15px',
'font-size':'{}%'.format(generate_size(related_terms[term][1], smin, smax))}) for term in related_terms]
return buttons
except Exception as e:
with open('errors.txt','a') as f:
f.write(str(e))
f.write('\n')
#recent-trending div
# term: [sent, size]
@app.callback(Output('recent-trending', 'children'),
[Input(component_id='sentiment_term', component_property='value')],
events=[Event('related-update', 'interval')])
def update_recent_trending(sentiment_term):
try:
query = """
SELECT
value
FROM
misc
WHERE
key = 'trending'
"""
c = conn.cursor()
result = c.execute(query).fetchone()
related_terms = pickle.loads(result[0])
## buttons = [html.Button('{}({})'.format(term, related_terms[term][1]), id='related_term_button', value=term, className='btn', type='submit', style={'background-color':'#4CBFE1',
## 'margin-right':'5px',
## 'margin-top':'5px'}) for term in related_terms]
#size: related_terms[term][1], sentiment related_terms[term][0]
sizes = [related_terms[term][1] for term in related_terms]
smin = min(sizes)
smax = max(sizes) - smin
buttons = [html.H5('Recently Trending Terms: ', style={'color':app_colors['text']})]+[html.Span(term, style={'color':sentiment_colors[round(related_terms[term][0]*2)/2],
'margin-right':'15px',
'margin-top':'15px',
'font-size':'{}%'.format(generate_size(related_terms[term][1], smin, smax))}) for term in related_terms]
return buttons
except Exception as e:
with open('errors.txt','a') as f:
f.write(str(e))
f.write('\n')
external_css = ["https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/css/materialize.min.css"]
for css in external_css:
app.css.append_css({"external_url": css})
external_js = ['https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/js/materialize.min.js',
'https://pythonprogramming.net/static/socialsentiment/googleanalytics.js']
for js in external_js:
app.scripts.append_script({'external_url': js})
server = app.server
dev_server = app.run_server