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import os
import pandas as pd
import dash
from dash import dcc,html
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output
import plotly.graph_objs as go
# Load the data from the CSV files
dataframes = []
for filename in os.listdir('output'):
if filename.endswith('.csv'):
df = pd.read_csv(os.path.join('output', filename), sep=';')
dataframes.append(df)
df = pd.concat(dataframes)
# Create the Dash app
app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
# Define the app layout
app.layout = dbc.Container([
dbc.Row([
dbc.Col([
html.H1('Token Analysis'),
dcc.Dropdown(
id='token-dropdown',
options=[{'label': i, 'value': i} for i in df['tokenSymbol'].unique()],
value='MANA'
),
# Add more filters here
], width=5),
dbc.Col([
dcc.Graph(id='token-graph')
], width=7)
])
])
# Define the callback to update the graph
@app.callback(
Output('token-graph', 'figure'),
[Input('token-dropdown', 'value')]
)
def update_graph(selected_token):
filtered_df = df[df['tokenSymbol'] == selected_token]
# filtered_df['timeStamp'] = pd.to_datetime(filtered_df['timeStamp'], unit='s')
# filtered_df['value'] = filtered_df['value'].astype(float) / 1e18
figure = go.Figure(
data=[
go.Scatter(
x=filtered_df['timeStamp'],
y=filtered_df['value'],
mode='lines',
name='Value over time'
)
],
layout=go.Layout(
title='Token Value Over Time',
yaxis=dict(
title='Value ('+selected_token+')', # Change this to 'Value (USD)' if the values are in USD
),
showlegend=True,
legend=go.layout.Legend(
x=0,
y=1.0
),
margin=go.layout.Margin(l=40, r=0, t=40, b=30)
)
)
return figure
if __name__ == '__main__':
app.run_server(debug=True)
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