Data Viz Renderer
Generate self-contained HTML/SVG infographics from JSON data. Four supported types:
- Stats Cards — KPI big numbers + trend arrows + icons
- Comparison Chart — Grouped bar chart with multiple series
- Flow Diagram — Step-by-step process with numbering, icons, and connecting arrows
- Dashboard — Mixed layout: stat cards + bar chart + donut chart + flow
Output is a fully self-contained HTML file (all CSS/SVG inline, no external dependencies), ready to open directly in a browser.
Usage
Basic Usage
bashpython3 scripts/build_infographic.py config.json
Also supports reading from stdin:
bashcat config.json | python3 scripts/build_infographic.py
The script outputs a JSON status to stdout and writes the generated HTML to the path specified in the output field.
JSON Configuration Format
Common fields:
| Field | Type | Required | Description |
|---|---|---|---|
title | string | No | Infographic title |
subtitle | string | No | Subtitle |
type | string | Yes | stats / comparison / flow / dashboard |
palette | string | No | Color palette (default: auto) |
data | object/array | Yes | Data content (format depends on type) |
output | string | No | Output file path (default: infographic.html) |
footer | string | No | Footer text |
Color Palettes
Available values: auto (automatically chosen based on data), ocean, sunset, forest, berry, vibrant, corporate, pastel, earth
Data Format by Type
1. stats — Stat Cards
json{ "type": "stats", "data": [ { "label": "Total Revenue", "value": "$1.2M", "icon": "money", "trend": "+12.5%", "trend_dir": "up" }, { "label": "Users", "value": "45,230", "icon": "users", "trend": "+8.2%", "trend_dir": "up" } ] }
icon options: users, user, money, percent, globe, clock, check, star, target, zap, chart-bar, chart-pie, database, rocket, shield, heart, light, search, mail, settings, flag, trending-up, trending-down
trend_dir: up (green upward arrow) or down (red downward arrow)
2. comparison — Bar Chart Comparison
json{ "type": "comparison", "data": { "chart_title": "Quarterly Revenue Comparison", "categories": ["Q1", "Q2", "Q3", "Q4"], "series": [ {"name": "2024", "values": [320, 410, 380, 520]}, {"name": "2025", "values": [380, 490, 450, 610]} ] } }
3. flow — Flow Diagram
json{ "type": "flow", "data": [ {"step": 1, "title": "Requirements", "description": "Gather user needs", "icon": "search"}, {"step": 2, "title": "Design", "description": "Create technical plan", "icon": "light"}, {"step": 3, "title": "Development", "description": "Code and test", "icon": "settings"}, {"step": 4, "title": "Launch", "description": "Deploy to production", "icon": "rocket"} ] }
4. dashboard — Mixed Dashboard
json{ "type": "dashboard", "data": { "stats": [ {"label": "DAU", "value": "12.3K", "icon": "users", "trend": "+5%", "trend_dir": "up"}, {"label": "Conversion Rate", "value": "3.8%", "icon": "target", "trend": "-0.2%", "trend_dir": "down"} ], "chart": { "chart_title": "Monthly Trend", "categories": ["Jan", "Feb", "Mar", "Apr"], "series": [{"name": "DAU", "values": [10200, 11500, 11800, 12300]}] }, "breakdown": [ {"label": "iOS", "value": 45}, {"label": "Android", "value": 38}, {"label": "Web", "value": 17} ], "flow": [ {"step": 1, "title": "Sign Up", "description": ""}, {"step": 2, "title": "Activate", "description": ""}, {"step": 3, "title": "Retain", "description": ""} ] } }
Output Format
The script outputs a JSON result to stdout:
json{ "status": "success", "output": "/absolute/path/to/infographic.html", "type": "stats", "title": "My Infographic", "palette": "auto", "size_bytes": 8432 }
On error:
json{ "status": "error", "errors": ["Missing required field: data"] }
Design Highlights
- Zero external dependencies: Pure Python standard library, no pip install needed
- Self-contained output: HTML with all CSS and SVG inline, no network required
- Responsive layout: Works on both desktop and mobile browsers
- Professional palettes: 8 preset color schemes + automatic selection
- 24+ built-in icons: Common SVG icons, no font files needed
- CJK-friendly: Font stack includes Noto Sans SC, PingFang SC, Microsoft YaHei
Use Cases
- Visualization modules in data reports
- Product data dashboards
- Business process illustrations
- Quarterly/monthly data comparisons
- Team KPI displays
Dependencies
- Python 3.7+ (standard library only)

