AI Data Visualizer

Automatically analyze and recommend optimal chart combinations based on data characteristics, generate beautiful interactive HTML dashboards (including line charts, bar charts, scatter plots, pie charts, etc.), support dark/light theme switching, CSV and JSON input, and data statistical summaries.

Generate interactive HTML dashboards from CSV/JSON data with one click.

Applicable Scenarios

• User provides CSV/JSON files that need visualization
• User needs a data analysis dashboard
• User mentions "data visualization", "generate chart", "plot", "chart"
• User uploads tabular data wanting intuitive display

Quick Start

bash

python3 "{SKILL_DIR}/scripts/generate_dashboard.py" data.csv -o dashboard.html
python3 "{SKILL_DIR}/scripts/generate_dashboard.py" data.json --json -o dashboard.html --theme dark

Features

• Smart Chart Recommendation: Automatically detects column types (numeric/time/categorical/text) and recommends optimal chart combinations
• 6 Chart Types: Line charts (trends), bar charts (comparison), scatter plots (correlation), pie/donut charts (proportions)
• Interactive HTML: Chart.js rendering, hover tooltips, dark/light theme toggle, responsive layout
• Statistical Summary: Automatically calculates mean, median, min/max values
• Data Table: Embedded raw data preview (limit 500 rows)

Supported Chart Selections

The script automatically selects chart strategies based on data column types. See {SKILL_DIR}/references/chart-selection.md.

CLI Options

Option	Description	Default
input	CSV or JSON file path	(required)
-o, --output	Output HTML path	dashboard.html
--json	Input is JSON format	auto-detect
--stdin	Read CSV from stdin	-
--theme	light or dark	light
--title	Dashboard title	数据可视化仪表板

Workflow

1. Receive user data — File path (CSV/JSON) or direct data content
2. Preprocess data — Clean and transform if necessary
3. Generate dashboard — Run script, output HTML
4. Inform user — Describe generated file and chart summary

If the user provides data content directly (rather than a file), write it to a temporary CSV file first, then call the script.

Dependencies

• Python 3.7+
• Chart.js 4.x (CDN loaded in HTML output)
• No pip dependencies (pure standard library)

Example

Input sales.csv:

text

Month,Product,Revenue,Units
2024-01,A,15000,120
2024-02,A,18000,145
2024-01,B,12000,95
2024-02,B,14000,110

Output: Automatically generates a dashboard with trend line charts, category comparison bar charts, and revenue proportion pie charts.