/
app.py
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/
app.py
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from dash import Dash, dcc, html, Input, Output, State
import dash
import plotly.graph_objects as go
import plotly.express as px
import pandas as pd
import tweepy
import dash_bootstrap_components as dbc
import os
from sentiment_analysis import *
from util import *
from clustering import *
# Used for local testing, comment out for deployment
from config import consumer_key, consumer_secret, access_token, access_token_secret
app = Dash(__name__, external_stylesheets=[dbc.icons.BOOTSTRAP])
server = app.server
app.title = 'TwiPA'
# To Retrieve Twitter API keys, the second value is the default value if the respective os variable does not exist
auth = tweepy.OAuth1UserHandler(os.getenv("consumer_key", consumer_key),
os.getenv("consumer_secret", consumer_secret),
os.getenv("access_token", access_token),
os.getenv("access_token_secret", access_token_secret))
api = tweepy.API(auth)
fig = go.Figure()
global time_to_tweet_dict
global pca_to_tweet_dict
app.layout = html.Div(children=[
html.Div(className='row', # Define the row element
children=[
html.Div(className='four columns div-user-controls',
children = [
html.H1('TwiPA'),
html.P('''Enter a Twitter Username of your choice.'''),
html.Div(className='containerTwo', children = [
html.Div(className = 'textField', children=[dcc.Input(id='my-input', value='elonmusk', type='text')]),
dcc.Loading(id="loading-1", type="default",children=html.Div(id="loading-output-1"))
]),
html.Br(),
html.P('''Choose number of tweets you want to analyze'''),
dcc.Slider(50, 1100, 100,value=250,id='my-slider'),
html.Br(),
html.Br(),
html.Div(className = 'image-cropper',
children = [
html.Img(src="https://pbs.twimg.com/profile_images/1503591435324563456/foUrqiEw_400x400.jpg", id="profile-pic", className = 'rounded')
]),
html.Div(className = 'container', children = [
html.H6(className = 'parent', id="full-name"),
html.Div(id = "verified", className = 'child',
children = [
html.I(className="bi bi-check-circle-fill")
], style = {'display': 'block'})
]),
html.Br(),
html.Br(),
html.H6(id="num-followers"),
html.H6(id="positivity-score", children="Positivity-Score (1 is the most positive):"),
html.H6(id="subjectivity-score", children="Subjectivity-Score (1 is the most subjective):"),
html.Br(),
html.H6(id='hover-data'),
html.A(id='hover-data-link'),
]
), # Define the left element
html.Div(className='eight columns div-for-charts bg-grey',
children = [
# dcc.Graph(id='sentiment-graph'), dcc.Graph(id='objectivity-graph'), dcc.Graph(id='cluster-graph')
dcc.Graph(id='sentiment-graph'), dcc.Graph(id='objectivity-graph'), dcc.Graph(id='cluster-graph')
])
])
])
@app.callback(
Output('loading-output-1', "children"),
Output('sentiment-graph', 'figure'),
Output('objectivity-graph', 'figure'),
Output('cluster-graph', 'figure'),
Output('full-name', component_property='children'),
Output('profile-pic', component_property='src'),
Output('positivity-score', component_property='children'),
Output('subjectivity-score', component_property='children'),
Output('verified', component_property='style'),
Output('num-followers', component_property='children'),
Input(component_id='my-input', component_property='value'),
Input(component_id='my-slider', component_property='value')
)
def update_output(value, selected_number_tweets):
# dictionaries used to get tweets for hovering
global time_to_tweet_dict
global pca_to_tweet_dict
from profileData import profileData
profileData = profileData(value)
profileData.populate(api=api, num_tweets=selected_number_tweets)
time_to_tweet_dict = generate_time_to_tweet_dict(profileData.tweets)
fig1 = generate_polarity_graph(profileData.tweets)
fig1.update_layout(transition_duration=500)
fig2 = generate_objectivity_graph(profileData.tweets)
fig2.update_layout(transition_duration=500)
fig3, pca_to_tweet_dict = cluster(profileData.tweets)
fig3.update_layout(transition_duration=500)
#sentiment scores
positivity_string = "Positivity-Score (1 is the most positive): " + (str(get_polarity_score(profileData.tweets))[0:5])
objectivity_string = "Subjectivity-Score (1 is the most subjective): " + (str(get_objectivity_score(profileData.tweets))[0:5])
name = profileData.name
profile_image_url = profileData.profile_image_url
num_followers = "Number of Followers: " + str(profileData.followers_count)
if profileData.verified :
return True, fig1, fig2, fig3, name, profile_image_url, positivity_string, objectivity_string, {'display': 'block'}, num_followers
return True, fig1.update_layout({'plot_bgcolor': 'rgba(0, 0, 0, 0)', 'paper_bgcolor': 'rgba(0, 0, 0, 0)'}), fig2.update_layout({'plot_bgcolor': 'rgba(0, 0, 0, 0)', 'paper_bgcolor': 'rgba(0, 0, 0, 0)'}), fig3, name, profile_image_url, positivity_string, objectivity_string, {'display': 'none'}, num_followers
@app.callback(
Output('hover-data', 'children'),
Output('hover-data-link', 'children'),
Input('sentiment-graph', 'hoverData'),
Input('objectivity-graph', 'hoverData'),
Input('cluster-graph', 'hoverData')
)
def display_hover_data(hoverData1, hoverData2, hoverData3):
global time_to_tweet_dict
global pca_to_tweet_dict
ctx = dash.callback_context
if not ctx.triggered:
return "", ""
else:
hoverData = None
if 'sentiment-graph' in ctx.triggered[0]['prop_id']:
hoverData = hoverData1
hoverData = hoverData["points"][0]
if hoverData["curveNumber"] == 0:
time = hoverData["x"]
return "Tweet: " + str(time_to_tweet_dict[time][0]), time_to_tweet_dict[time][2]
elif 'objectivity-graph' in ctx.triggered[0]['prop_id']:
hoverData = hoverData2
hoverData = hoverData["points"][0]
if hoverData["curveNumber"] == 0:
time = hoverData["x"]
return "Tweet: " + str(time_to_tweet_dict[time][0]), time_to_tweet_dict[time][2]
elif 'cluster-graph' in ctx.triggered[0]['prop_id']:
hoverData = hoverData3
hoverData = hoverData["points"][0]
print(hoverData)
# print(pca_to_tweet_dict)
print(len(pca_to_tweet_dict))
return "Tweet: " + str(pca_to_tweet_dict[hoverData["x"]][0]), pca_to_tweet_dict[hoverData["x"]][1]
if __name__ == '__main__':
app.run_server(debug=False)