Dashboard code review
2025-07-315 turns16,605 charsgpt-4-1-mini, gpt-4o⑂ 1 fork(s)
Summary
User shared a Dash dashboard script and encountered a runtime error requiring dependency installation and debugging assistance.
Messages
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import dash
from dash import dcc, html
from dash.dependencies import Input, Output
import numpy as np
# Load data from the files generated by data_gen.py
try:
df_summary = pd.read_csv('outputs/summary_metrics.csv')
df_project_breakdown = pd.read_csv('outputs/project_breakdown.csv')
df_monthly = pd.read_csv('outputs/monthly_profit_forecast.csv')
df_revenue_project = pd.read_csv('outputs/revenue_by_project.csv')
except FileNotFoundError:
print("Data files not found. Please run data_gen.py first.")
exit()
# Initialize the Dash app
app = dash.Dash(__name__)
# Define colors
colors = ['#8A2BE2', '#9370DB', '#BA55D3', '#DA70D6', '#E6E6FA']
# Helper function to create indicator cards
def create_indicator_card(metric_name, value, unit):
return html.Div(
className="card",
children=[
html.H3(metric_name, className="card-title"),
html.H1(f"{value}{unit}", className="card-value")
]
)
# Helper function to create pie charts
def create_pie_chart(df, metric, title):
# Extract the correct column for the pie chart
labels = df['project_type']
values = df[metric]
fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.5)])
fig.update_layout(
title=dict(
text=f"<b>{title}</b>",
x=0.5,
xanchor='center',
font=dict(size=18, color='#333')
),
legend_title_text='Project Type',
margin=dict(t=50, b=50, l=50, r=50),
legend=dict(orientation="h", x=0.5, xanchor="center"),
paper_bgcolor='white'
)
fig.update_traces(marker=dict(colors=colors))
return fig
# Create the layout
app.layout = html.Div(
className="dashboard-container",
children=[
html.H1("Finance Dashboard", style={'textAlign': 'center', 'color': '#333'}),
# Top-level indicators
html.Div(
className="row card-container",
children=[
create_indicator_card('Expenses', df_summary.loc[df_summary['metric'] == 'Expenses', 'value'].iloc[0], 'k'),
create_indicator_card('Revenue', df_summary.loc[df_summary['metric'] == 'Revenue', 'value'].iloc[0], 'k'),
create_indicator_card('Profit', df_summary.loc[df_summary['metric'] == 'Profit', 'value'].iloc[0], 'k'),
create_indicator_card('Forecast', df_summary.loc[df_summary['metric'] == 'Forecast', 'value'].iloc[0], 'k'),
]
),
# Pie charts for project breakdown
html.Div(
className="row",
children=[
html.Div(
className="chart-card",
children=[
dcc.Graph(
id='expenses-per-project',
figure=create_pie_chart(df_project_breakdown[['project_type', 'Expenses']], 'Expenses', 'Expenses per project')
)
]
),
html.Div(
className="chart-card",
children=[
dcc.Graph(
id='revenue-per-project',
figure=create_pie_chart(df_project_breakdown[['project_type', 'Revenue']], 'Revenue', 'Revenue per project')
)
]
),
html.Div(
className="chart-card",
children=[
dcc.Graph(
id='profit-per-project',
figure=create_pie_chart(df_project_breakdown[['project_type', 'Profit']], 'Profit', 'Profit per project')
)
]
),
html.Div(
className="chart-card",
children=[
dcc.Graph(
id='forecast-per-project',
figure=create_pie_chart(df_project_breakdown[['project_type', 'Forecast']], 'Forecast', 'Forecast per project')
)
]
),
]
),
# Line and Bar charts
html.Div(
className="row",
children=[
# Revenue by Project chart (line chart)
html.Div(
className="chart-card large",
children=[
dcc.Graph(
id='revenue-by-project',
figure=px.line(
df_revenue_project,
x='project_type',
y='Revenue By Project (SUM)',
markers=True,
title='<b>Revenue By Project</b>'
).update_traces(line_color='#8A2BE2').update_layout(
xaxis_title="",
yaxis_title="",
margin=dict(t=50, b=50, l=50, r=50)
)
)
]
),
# Profit vs Forecast chart (bar chart)
html.Div(
className="chart-card large",
children=[
dcc.Graph(
id='profit-vs-forecast',
figure=go.Figure(
data=[
go.Bar(
name='Profit (SUM)',
x=df_monthly['month'],
y=df_monthly['Profit (SUM)'],
marker_color='#8A2BE2'
),
go.Bar(
name='Forecast (SUM)',
x=df_monthly['month'],
y=df_monthly['Forecast (SUM)'],
marker_color='#D8BFD8'
)
]
).update_layout(
barmode='group',
title_text='<b>Profit vs Forecast</b>',
xaxis_title="",
yaxis_title="",
legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
margin=dict(t=50, b=50, l=50, r=50),
paper_bgcolor='white',
plot_bgcolor='white'
)
)
]
),
]
),
]
)
# A simple CSS string to create the visual style
app.index_string = '''
<!DOCTYPE html>
<html>
<head>
{%metas%}
<title>{%title%}</title>
{%favicon%}
{%css%}
<style>
body { font-family: sans-serif; background-color: #f4f7fa; color: #333; margin: 0; }
.dashboard-container { padding: 20px; }
.row { display: flex; flex-wrap: wrap; margin: -10px; }
.card-container { display: flex; flex-wrap: wrap; justify-content: space-around; }
.card {
flex: 1;
background-color: white;
border-radius: 12px;
box-shadow: 0 4px 8px rgba(0,0,0,0.1);
margin: 10px;
padding: 20px;
text-align: center;
min-width: 200px;
}
.card-title { font-weight: bold; font-size: 1.2em; color: #666; margin-bottom: 5px; }
.card-value { font-weight: bold; font-size: 2.5em; color: #8A2BE2; margin-top: 0; }
.chart-card {
flex: 1;
background-color: white;
border-radius: 12px;
box-shadow: 0 4px 8px rgba(0,0,0,0.1);
margin: 10px;
padding: 10px;
min-width: 250px;
}
.chart-card.large {
flex: 2;
min-width: 500px;
}
</style>
</head>
<body>
{%app_entry%}
<footer>
{%config%}
{%scripts%}
{%renderer%}
</footer>
</body>
</html>
'''
if __name__ == '__main__':
# To run this script, save the generated HTML file
# This requires the kaleido package for static image generation
try:
from dash_to_html import DashToHtml
d2h = DashToHtml(app, 'outputs/dashboard.html')
d2h.create()
print("Dashboard successfully saved as outputs/dashboard.html")
except ImportError:
print("Please install dash_to_html and kaleido to save the dashboard to HTML.")
print("You can run the app with `python viz.py` and view it in your browser.")
app.run_server(debug=True)
Your Dash-based finance dashboard is very well-structured, modular, and visually clean. It combines core visual components like KPI cards, pie charts, and line/bar plots, all styled with custom CSS and `plotly`. Below are some insights and suggestions to make the code clearer, more robust, or easier to extend:
---
### ✅ **Strengths**
- **Modular Code**: Good use of helper functions (`create_indicator_card`, `create_pie_chart`) to reduce repetition.
- **Consistent Style**: Color palette and layout maintain a unified visual identity.
- **Fallback for Missing Data**: Gracefully exits if CSVs are missing, with a helpful message.
- **Visual Balance**: Pie charts and line/bar charts are well organized into responsive containers.
- **HTML Template Integration**: Your `app.index_string` is a smart touch for styling without needing an external CSS file.
---
### 🛠 Suggestions for Improvement
#### 1. **Make Filepaths Configurable**
So others (or future you) can reuse the dashboard without modifying paths:
```python
import os
DATA_DIR = 'outputs'
df_summary = pd.read_csv(os.path.join(DATA_DIR, 'summary_metrics.csv'))
```
#### 2. **Add Robust Fallback for Missing Data**
Instead of `exit()`, you could display an in-app error message:
```python
if df_summary.empty or df_project_breakdown.empty:
app.layout = html.Div("Data not found or empty. Please run data_gen.py.")
```
#### 3. **Better Label Formatting in Pie Charts**
Sometimes long `project_type` names can overlap. You could wrap them or reduce the font:
```python
fig.update_traces(textposition='inside', textinfo='percent+label')
```
#### 4. **Dynamic Unit Scaling**
`'k'` is hardcoded. You could infer the scale dynamically:
```python
def format_value(val):
if val >= 1e6:
return f"{val/1e6:.1f}M"
elif val >= 1e3:
return f"{val/1e3:.1f}k"
return f"{val:.1f}"
```
Then in your indicator cards:
```python
create_indicator_card('Expenses', format_value(...), '')
```
#### 5. **Consider Caching for Larger Datasets**
If the CSVs grow large, you can cache them:
```python
from functools import lru_cache
@lru_cache()
def load_csv(filename):
return pd.read_csv(os.path.join(DATA_DIR, filename))
```
#### 6. **Accessibility & Mobile Responsiveness**
- Add `lang="en"` to `<html>`
- Consider wrapping charts in a `<div style="overflow-x:auto;">` for mobile viewing
---
### 💡 Potential Enhancements
- **Add Dropdowns or Filters** for year, project type, or metric.
- **Time Series Forecasting** using `plotly.express.scatter` with trendlines.
- **Export Button** using Dash Bootstrap Components (`dbc.Button` + `send_file` logic).
- **Theme Toggle** (light/dark) with a toggle switch.
- **Add tooltips** to indicators for context.
---
If you’d like, I can help:
- Create a `data_gen.py` template that matches this dashboard
- Build a version that renders directly to HTML (i.e., no server)
- Add interactivity (dropdowns, sliders, etc.)
Let me know how you want to evolve it.
gpt-4o
⑂ thread forked here
python3 viz.py
Please install dash_to_html and kaleido to save the dashboard to HTML.
You can run the app with `python viz.py` and view it in your browser.
Traceback (most recent call last):
File "/Users/danielkliewer/work/dataviz01/cmce5hl1610eu0798m3yj9iki/scripts/viz.py", line 237, in <mo
dule> from dash_to_html import DashToHtml
ModuleNotFoundError: No module named 'dash_to_html'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/danielkliewer/work/dataviz01/cmce5hl1610eu0798m3yj9iki/scripts/viz.py", line 244, in <mo
dule> app.run_server(debug=True)
^^^^^^^^^^^^^^
File "/Users/danielkliewer/work/dataviz01/venv/lib/python3.13/site-packages/dash/_obsolete.py", line
22, in __getattr__ raise err.exc(err.message)
dash.exceptions.ObsoleteAttributeException: app.run_server has been replaced by app.run
You're running into **two separate issues**, both easily fixable:
---
### ✅ 1. **Missing `dash_to_html` and `kaleido` packages**
You're trying to render the Dash app to a static HTML file, but you're missing the required packages.
#### ✅ Solution:
Install the required packages in your environment:
```bash
pip install dash-to-html kaleido
```
Note:
- The correct package name for `dash_to_html` is **`dash-to-html`**
- `kaleido` is required by Plotly to export charts as static images inside the HTML file
---
### ✅ 2. **`app.run_server()` is deprecated in Dash 3.x+**
Dash 3.x replaced `.run_server()` with `.run()`.
#### ✅ Fix the final block of your script:
Replace this:
```python
app.run_server(debug=True)
```
With this:
```python
app.run(debug=True)
```
---
### ✅ Final `__main__` Block (Fixed Version)
Here’s the updated final block of your script:
```python
if __name__ == '__main__':
try:
from dash_to_html import DashToHtml
d2h = DashToHtml(app, 'outputs/dashboard.html')
d2h.create()
print("Dashboard successfully saved as outputs/dashboard.html")
except ImportError:
print("Please install dash-to-html and kaleido to save the dashboard to HTML.")
print("You can run the app with `python viz.py` and view it in your browser.")
app.run(debug=True) # ✅ Updated method here
```
---
### Bonus: Install Everything at Once
You can install all required packages at once if you haven't already:
```bash
pip install dash plotly dash-to-html kaleido pandas numpy
```
Let me know if you'd like to make the HTML export more interactive (e.g., preserve charts as SVGs).
gpt-4o
The error messages indicate two issues:
---
### 1. **`dash_to_html` module not found**
- Your code tries to import `dash_to_html` to save the dashboard as a static HTML file, but you don’t have that package installed.
- **Solution:**
- If you want to save the dashboard as static HTML, install the package via pip:
```bash
pip install dash_to_html kaleido
```
- If you don’t need static HTML export, just run the app in the browser without that part (see below).
---
### 2. **`app.run_server` deprecated and replaced by `app.run`**
Dash has recently changed the API to replace `app.run_server()` with `app.run()`.
- **Fix:** Change your `if __name__ == '__main__':` block like this:
```python
if __name__ == '__main__':
try:
from dash_to_html import DashToHtml
d2h = DashToHtml(app, 'outputs/dashboard.html')
d2h.create()
print("Dashboard successfully saved as outputs/dashboard.html")
except ImportError:
print("Please install dash_to_html and kaleido to save the dashboard to HTML.")
print("You can run the app with `python viz.py` and view it in your browser.")
app.run(debug=True) # Changed from run_server to run
```
---
### Summary
- Install `dash_to_html` and `kaleido` if you want static export, or skip it.
- Replace `app.run_server()` with `app.run()`.
---
If you want, I can generate a fixed snippet for your script to apply these fixes directly. Would you like that?
gpt-4-1-mini