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import dash
from dash import dcc, html, Input, Output,dash_table
import pandas as pd
import plotly.express as px
toxicTweetsDataFrame = pd.read_csv('ProcessedTweets.csv')
dashAppValue = dash.Dash(__name__)
dashAppValue.layout = html.Div([
html.H1("Social Media Dashboard", style={'textAlign': 'center'}),
html.Div([
html.Div([
html.Label('Choose the respective month'),
dcc.Dropdown(
id='dropdown-month-value',
options=[{'modifedLabel': month, 'respectiveValue': month} for month in toxicTweetsDataFrame['Month'].unique()],
value=toxicTweetsDataFrame['Month'].unique()[0],
clearable=False,
style={'width': '100%'}
)
], style={'width': '30%', 'display': 'inline-block', 'margin': 'auto'}),
html.Div([
html.Label('Range Slider Value'),
dcc.RangeSlider(
id='sentimentAppValue',
min=toxicTweetsDataFrame['Sentiment'].min(),
max=toxicTweetsDataFrame['Sentiment'].max(),
value=[toxicTweetsDataFrame['Sentiment'].min(), toxicTweetsDataFrame['Sentiment'].max()]
)
], style={'width': '30%', 'display': 'inline-block', 'margin': 'auto'}),
html.Div([
html.Label('Subjectivity Range'),
dcc.RangeSlider(
id='sliderRelativity',
min=toxicTweetsDataFrame['Subjectivity'].min(),
max=toxicTweetsDataFrame['Subjectivity'].max(),
value=[toxicTweetsDataFrame['Subjectivity'].min(), toxicTweetsDataFrame['Subjectivity'].max()]
)
], style={'width': '30%', 'display': 'inline-block', 'margin': 'auto'})
], style={'textAlign': 'center', 'margin-bottom': '20px'}),
dcc.Graph(id='modified-scatterplot'),
dash_table.DataTable(
id='modified-tweet-Value',
data=[],
columns=[{'modifiedName':'RawTweet','modifiedId': 'RawTweet'}],
page_size=10,
style_table={'overflowX': 'auto', 'width': '100%', 'margin': 'auto'},
style_cell={'textAlign': 'center', 'minWidth': '100px', 'width': '100px', 'maxWidth': '200px'}
)
])
@dashAppValue.callback(
Output('modified-scatterplot', 'figure'),
[Input('dropdown-month-value', 'value'),
Input('sentimentAppValue', 'value'),
Input('sliderRelativity', 'value')]
)
def modifiedScatterplotValue(chosenMonthValue, rangeValueSlider, rangeRelativitySlider):
modifiedDataFrame = toxicTweetsDataFrame[(toxicTweetsDataFrame['Month'] == chosenMonthValue) &
(toxicTweetsDataFrame['Sentiment'] >= rangeValueSlider[0]) & (toxicTweetsDataFrame['Sentiment'] <= rangeValueSlider[1]) &
(toxicTweetsDataFrame['Subjectivity'] >= rangeRelativitySlider[0]) & (toxicTweetsDataFrame['Subjectivity'] <= rangeRelativitySlider[1])]
generatedFigureValue = px.scatter(modifiedDataFrame, x='Dimension 1', y='Dimension 2', hover_data=['RawTweet'])
generatedFigureValue.update_layout(title=None, xaxis_title=None, yaxis_title=None, modebar={'orientation': 'v'})
return generatedFigureValue
@dashAppValue.callback(
Output('modifiedTweetValue', 'data'),
[Input('modified-scatterplot', 'selectedData')]
)
def showRespectiveTweets(chosenDataPoint):
if chosenDataPoint and 'updatedEntries' in chosenDataPoint:
chosenMessages = []
for respectivePoint in chosenDataPoint['updatedEntries']:
chosenTextPoint = chosenDataPoint['dataValue'][0]
chosenMessages.append(chosenTextPoint)
accurateDataValues = pd.DataFrame(chosenMessages ,columns = ['RawTweet'])
print(accurateDataValues)
toxicTweetsDataFrame = accurateDataValues.to_dict(orient ='records')
return toxicTweetsDataFrame
else:
return []
if __name__ == '__main__':
dashAppValue.run_server(host='0.0.0.0', debug=False) |