holysheet 1.0.0

Python-first report compiler that generates beautiful, portable, interactive React dashboards.

📊 HolySheet

Python-first report compiler that turns raw data into stunning,
interactive React dashboards — zero frontend knowledge required.

📖 Documentation • Screenshots • Quickstart • Installation • Block Reference • Examples • Architecture • Development

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📸 See It In Action

Don't take our word for it — see what HolySheet generates:

🌙 Dark Theme

☀️ Light Theme

👔 Executive Theme

_{All dashboards above are generated from Python code — zero frontend knowledge required.
Each is a single, self-contained HTML file that opens in any browser.}

📊 Basic Report KPI cards + line chart · Light theme

💼 Sales Analytics Tabbed charts + KPIs · Executive theme

🏢 Portfolio Report Columns layout + data table · Dark theme

🚀 Full Showcase 37 blocks, all chart types · Dark theme

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✨ What is HolySheet?

HolySheet generates stunning, self-contained, interactive HTML dashboards powered by React + Apache ECharts — without requiring Node.js, npm, or any frontend tooling.

Write your dashboard in Python. Get a gorgeous interactive report as a single HTML file.
No server. No dependencies. Just open it in a browser. Holy Sheet, that's easy!

⚡ Key Highlights

• 🧱 57 block types — 18 charts, 6 metrics, 14 content/data, 8 layout, 9 interactive, AI insights + SQL
• 🎨 3 premium themes — Dark, Light, and Executive with full design systems
• 📦 Single-file export — One .html file (~1.5 MB) with everything embedded
• 🐼 Any data source — Pandas, Polars, dicts, lists, Google Sheets
• 🤖 AI-powered insights — OpenAI, Anthropic, Google Gemini integration
• 🔐 Enterprise features — Password protection, expiring reports, PDF export
• 🔧 Developer tools — CLI, hot-reload dev server, linting, report diff
• 📱 Responsive & PWA — Works on mobile, supports offline mode

from holysheet import Report, KPI, LineChart, DataTable

report = Report(title="Executive Portfolio Report", theme="dark")

report.add(KPI(label="Revenue", value=1_250_000, unit="€", delta="+12%", status="positive"))
report.add(LineChart(title="Revenue Trend", data=revenue_df, x="date", y="revenue"))
report.add(DataTable(title="Projects", data=projects_df))

report.export_html("report.html")  # ← That's it. Open in any browser.

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🙏 Why HolySheet?

😩 The Problem	😇 The HolySheet Way
Dashboards require complex frontend setup	Zero frontend knowledge needed
Reports need a running server	Self-contained HTML files — open anywhere
Visualization libraries produce basic charts	Enterprise-grade React UI with interactive ECharts
Sharing reports is painful	Single HTML file — email it, Slack it, embed it
Python-to-dashboard tools look dated	Modern Material UI design with dark/light/executive themes
Data wrangling across libraries	Native Pandas, Polars, dict, and list support

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🚀 Quickstart

1. Install

pip install holysheet

2. Build a dashboard

from holysheet import Report, KPI, LineChart, BarChart, PieChart, DataTable, Section

report = Report(
    title="Q4 Business Review",
    subtitle="Revenue & Operations Dashboard",
    theme="dark",
    author="Data Team",
)

# KPI cards — they auto-arrange in a responsive grid
report.add(KPI(label="Total Revenue", value=2_450_000, unit="€", delta="+18%", status="positive"))
report.add(KPI(label="Active Clients", value=142, delta="+12", status="positive"))
report.add(KPI(label="Churn Rate", value=3.2, unit="%", delta="-0.5%", status="positive"))
report.add(KPI(label="NPS Score", value=72, delta="+5", status="positive"))

# Charts — pass any DataFrame or list of dicts
report.add(LineChart(title="Monthly Revenue", data=revenue_data, x="month", y="revenue"))
report.add(BarChart(title="Revenue by Region", data=region_data, x="region", y="revenue"))
report.add(PieChart(title="Revenue Split", data=split_data, name="category", value="amount"))

# Searchable, paginated data table
report.add(DataTable(title="Top Clients", data=clients_data, columns=["name", "revenue", "status"]))

# Export → a single portable HTML file
report.export_html("q4_review.html")

3. Open & share

open q4_review.html   # macOS
xdg-open q4_review.html  # Linux
start q4_review.html  # Windows

The HTML file is fully standalone — no server, no internet, no Node.js. Send it via email, upload to S3, embed in Confluence — it just works.

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📦 Installation

# Core (zero extras)
pip install holysheet

# With Pandas support
pip install holysheet[pandas]

# With PDF export
pip install holysheet[pdf]

# With AI insights (OpenAI / Anthropic / Google)
pip install holysheet[ai]

# With cloud publishing (S3 / GCS)
pip install holysheet[cloud]

# With Google Sheets data source
pip install holysheet[gsheets]

# Everything
pip install holysheet[all]

Requirements:

• 🐍 Python 3.11+
• 🚫 No Node.js required
• 🚫 No frontend build step
• 🚫 No running server

Core dependencies: pydantic v2 · jinja2 · orjson · loguru · click

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🧱 Block Reference

HolySheet ships with 57 block types organized into seven categories.

🖼️ Block Preview — Charts & Data Tables

18 chart types: line, bar, pie, radar, heatmap, funnel, and more

Interactive tables with search, sort, pagination & conditional formatting

🖼️ Block Preview — KPI Cards Across Themes

_{KPI cards automatically adapt to your chosen theme}

📊 Charts (18)

Block	Description	Key Props
LineChart	Multi-series line chart	data, x, y, series, annotations
AreaChart	Filled area chart	data, x, y, series, annotations
BarChart	Grouped/stacked bar chart	data, x, y, series, annotations
PieChart	Pie / donut chart	data, name, value
ScatterChart	Scatter / bubble plot	data, x, y, size, category
RadarChart	Radar / spider chart	data, indicators
GaugeChart	Speedometer gauge	value, min, max, thresholds
FunnelChart	Conversion funnel	data, name, value
TreemapChart	Hierarchical treemap	data, name, value, category
HeatmapChart	2D heatmap with color gradient	data, x, y, value
CandlestickChart	Financial OHLC chart	data, x, open, close, low, high
SankeyChart	Flow / energy diagram	nodes, links
WaterfallChart	Waterfall / bridge chart	data, category, value
BoxPlotChart	Statistical box plot	data, categories
MapChart	Geographical scatter	data, lat, lng, value, name
GanttChart	Project timeline 🆕	tasks [{name, start, end, progress}]
DAGChart	Directed acyclic graph 🆕	nodes, edges, layout
CorrelationMatrix	Correlation heatmap 🆕	matrix, labels

🧠 AI & Data Sources (3)

Block	Description	Key Props
AIInsight	LLM-powered data narrative 🆕	data, provider, prompt, api_key
GoogleSheet	Google Sheets data source 🆕	spreadsheet_id, sheet_name, range
SqlBlock	Client-side SQL queries 🆕	query, data, output

📈 Metrics (6)

Block	Description	Key Props
KPI	Key metric card with delta + tooltips	label, value, unit, delta, status, tooltip_detail
Metric	Compact inline metric	label, value, unit, icon
ProgressBar	Progress indicator	label, value, max, color
StatComparison	Side-by-side comparison	title, items
Scorecard	Conditional color metric grid 🆕	data, columns, thresholds
DataProfile	Auto-EDA summary cards 🆕	columns [{name, dtype, count, ...}]

📝 Content (12)

Block	Description	Key Props
DataTable	Searchable table + conditional formatting	data, columns, formatting, downloadable
Markdown	Rich text content	content
CodeBlock	Syntax-highlighted code	code, language, title
Image	Image display	src, alt, caption
Alert	Callout / notification	severity, title, message
Timeline	Vertical event timeline	events [{date, title, description}]
Callout	Styled quote / highlight	content, author, variant
JsonViewer	Interactive JSON tree	data, collapsed_depth
UserCard	Team member card	name, role, avatar_url, stats
StatusList	Status indicators list	items [{label, status, value}]
InfoList	Key-value pair display	items [{key, value, icon}]
Sparkline	Tiny inline chart	data, color, show_area
NarrationBlock	Voice narration (Web Speech API) 🆕	text, autoplay

📐 Layout (8)

Block	Description	Key Props
Section	Group blocks with a heading	title, description, children
Columns	Multi-column responsive grid	children, widths
Tabs	Tabbed content panels	tabs (list of {label, children})
Divider	Visual separator line	label, variant
Accordion	Collapsible content panels	panels (list of {title, children})
Stepper	Process / wizard steps	steps [{label, description, status}]
TagList	Colored tag/badge chips	tags [{label, color}]
Compare	Side-by-side comparison layout 🆕	left_label, right_label, left_children, right_children

🎮 Interactive (9)

Block	Description	Key Props
Slider	Range slider input	label, min, max, default_value
NumberInput	Numeric input field	label, default_value, step
Toggle	On/off switch	label, default_value
Dropdown	Select from options	label, options, default_value
TextInput	Text / textarea input	label, placeholder, multiline
CheckboxGroup	Multiple checkboxes	label, options, default_values
RadioGroup	Single-select radio buttons	label, options, default_value
Embed	Iframe embed	url, height, aspect_ratio
Video	HTML5 video player	src, poster, controls

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🎨 Themes

Three built-in themes ship out of the box:

report = Report(title="Report", theme="dark")       # 🌙 Deep dark, vibrant accents
report = Report(title="Report", theme="light")      # ☀️ Clean, professional, airy
report = Report(title="Report", theme="executive")  # 👔 Premium serif with rich greens

Each theme defines a complete design system: colors, typography (Inter / Georgia), spacing, shadows, and an 8-color chart palette.

🌙 Dark Glassmorphism cards, vibrant accents Ideal for internal dashboards

☀️ Light Clean, professional, print-ready Ideal for client-facing reports

👔 Executive Gold accents, serif typography Ideal for board presentations

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📚 Examples

Minimal Status Page

from holysheet import Report, KPI, Markdown, Alert

report = Report(title="System Status", theme="dark")

report.add(Alert(severity="success", title="All Systems Operational", message="Last checked: 2 minutes ago"))
report.add(KPI(label="Uptime", value=99.97, unit="%", status="positive"))
report.add(KPI(label="Response Time", value=142, unit="ms", status="neutral"))
report.add(KPI(label="Error Rate", value=0.03, unit="%", delta="-0.01%", status="positive"))
report.add(Markdown(content="Monitored endpoints: **API**, **Auth**, **CDN**, **Database**"))

report.export_html("status.html")

Executive Dashboard with Sections & Columns

from holysheet import Report, KPI, LineChart, BarChart, DataTable, Section, Columns, Markdown

report = Report(
    title="AIFlow Executive Report",
    subtitle="Portfolio risk and delivery intelligence",
    theme="executive",
    author="Strategy Team",
)

# Executive summary
report.add(Markdown(content="""
## Executive Summary

Portfolio health remains strong with 42 active projects delivering on schedule.
Risk-adjusted returns are trending positively, with a 12% improvement in delivery confidence.
"""))

# Note: Variables like risk_df, team_df, projects_df should be your DataFrames

# KPI grid inside a section
report.add(Section(
    title="Key Metrics",
    children=[
        KPI(label="Active Projects", value=42, delta="+3", status="positive"),
        KPI(label="On-Track", value=87, unit="%", status="positive"),
        KPI(label="At-Risk", value=5, status="negative"),
        KPI(label="Budget Utilization", value=76, unit="%", status="neutral"),
    ],
))

# Side-by-side charts
report.add(Columns(children=[
    LineChart(title="Risk Score Trend", data=risk_df, x="date", y="score"),
    BarChart(title="Delivery by Team", data=team_df, x="team", y="delivered"),
]))

# Detailed data
report.add(DataTable(
    title="Project Details",
    data=projects_df,
    columns=["project", "owner", "risk", "status", "completion"],
))

report.export_html("executive_report.html")

Multi-Chart Analytics with Tabs

from holysheet import Report, Tabs, LineChart, BarChart, PieChart, FunnelChart

report = Report(title="Sales Analytics", theme="dark")

report.add(Tabs(tabs=[
    {
        "label": "📈 Trends",
        "children": [
            LineChart(title="Monthly Sales", data=sales_df, x="month", y="total"),
            LineChart(title="Customer Growth", data=growth_df, x="month", y="customers"),
        ],
    },
    {
        "label": "📊 Breakdown",
        "children": [
            BarChart(title="Sales by Region", data=region_df, x="region", y="sales"),
            PieChart(title="Product Mix", data=product_df, name="product", value="revenue"),
        ],
    },
    {
        "label": "🔄 Pipeline",
        "children": [
            FunnelChart(title="Sales Funnel", data=funnel_df, name="stage", value="count"),
        ],
    },
]))

report.export_html("sales_analytics.html")

🖼️ Example Output — Sales Dashboard (Executive Theme)

_{Generated by examples/sales_dashboard.py — a single self-contained HTML file}

💡 More examples in the examples/ directory — including a full showcase with every block type.

---

📤 Export Modes

Standalone HTML (default)

report.export_html("report.html")

Generates a single, self-contained HTML file (~1.5 MB) with embedded React, CSS, and data. Zero external dependencies. Open directly in any browser.

Folder Export

report.export_folder("dist/")

Generates a deployable folder structure:

dist/
  index.html       ← Entry point
  assets/
    app.js         ← React bundle
    app.css        ← Styles
  report.json      ← Dashboard spec

Ideal for hosting on a web server, S3, or CDN.

PDF Export

report.export_pdf("report.pdf", landscape=True, margin="0.5in")

Requires Playwright (pip install holysheet[pdf]) or Chrome/Chromium.

JSON Export

report.export_json("report.json")

Exports just the dashboard specification as JSON. Useful for debugging, version control, or feeding into external rendering pipelines.

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🗄️ Data Formats

HolySheet auto-detects and converts data from multiple formats:

# ✅ List of dicts
data = [{"name": "Alice", "score": 95}, {"name": "Bob", "score": 87}]

# ✅ Dict of lists
data = {"name": ["Alice", "Bob"], "score": [95, 87]}

# ✅ Pandas DataFrame
import pandas as pd
data = pd.DataFrame({"name": ["Alice", "Bob"], "score": [95, 87]})

# ✅ Polars DataFrame
import polars as pl
data = pl.DataFrame({"name": ["Alice", "Bob"], "score": [95, 87]})

All formats are normalized to records internally via holysheet.data.to_records().

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💻 CLI

# Validate a report spec
holysheet validate report.json

# Serve a report locally (opens browser)
holysheet serve report.json

# Hot-reload dev server — auto-refreshes on Python script changes
holysheet dev my_report.py --port 8000

# Lint a report for best practices
holysheet lint my_report.py --strict

# Compare two report versions
holysheet diff old_report.json new_report.json

# Show version
holysheet version

# Publish to S3 or Google Cloud Storage
holysheet publish report.html -t s3://my-bucket/reports/q4.html --public
holysheet publish report.html -t gs://my-bucket/reports/q4.html

---

🔥 Advanced Features

Custom Themes

from holysheet import Report, Theme

brand = Theme(name="acme", primary="#FF6B00", background="#0A0A0F", font="Satoshi")
report = Report(title="Acme Report", theme=brand)

Multi-Page Reports

report.add_page("Overview", [KPI(label="Revenue", value="$1.2M")])
report.add_page("Details", [DataTable(title="Breakdown", data=df)])

Chart Annotations

report.add(LineChart(
    title="Revenue", data=df, x="month", y="revenue",
    annotations=[{"x": "Mar", "text": "Product Launch", "color": "#22d3ee"}]
))

Global Filters

report.add_filter("region", type="dropdown", options=["NA", "EU", "APAC"])

Jupyter Integration

report.show()  # Renders inline in Jupyter notebook

Password Protection & Expiry

report.export_html("secure.html", password="s3cret")  # AES-256 encrypted
Report(title="Temp", expires="2025-12-31")  # Auto-expires

Report Templates

from holysheet.templates import SalesDashboard, ExecutiveSummary, OpsMonitor

blocks = SalesDashboard(data={"kpis": {"revenue": "$1.2M", "deals_won": 42}})

Anomaly Detection

report.add(LineChart(
    title="Server Latency", data=metrics, x="time", y="latency_ms",
    anomaly_detection=True,  # Auto-detect and annotate outliers
))

AI-Powered Insights

from holysheet import AIInsight
report.add(AIInsight(title="Key Findings", data=df, provider="openai"))

SQL Block

from holysheet import SqlBlock
report.add(SqlBlock(
    query="SELECT region, SUM(revenue) FROM data GROUP BY region",
    data=sales_df,
))

Voice Narration

from holysheet import NarrationBlock
report.add(NarrationBlock(text=report.auto_narrate()))

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🏗️ Architecture

Python API  →  Pydantic v2 Schema  →  JSON Spec  →  React Renderer  →  HTML Dashboard

HolySheet operates in two distinct phases:

🔧 Build Time (Python — your machine)

1. You define blocks using the Python API
2. HolySheet validates everything with Pydantic v2 models
3. Generates a versioned JSON dashboard specification
4. Injects the spec into a prebuilt React application
5. Exports a self-contained HTML file via Jinja2 templates

🌐 Runtime (Browser — any machine)

1. Browser opens the HTML file (no server needed)
2. React reads the embedded dashboard spec from <script id="report-data">
3. Renders each block through a component registry (type → React component)
4. Charts become interactive via Apache ECharts
5. Tables support real-time search and pagination

The key insight: The React app is prebuilt and bundled inside the Python package. End users never need Node.js, npm, or any frontend tooling.

Project Structure

HolySheet/
  src/holysheet/           # Python package
    __init__.py            #   Public API (20 block types + Report)
    blocks.py              #   Pydantic v2 block models
    schema.py              #   Report schema model
    report.py              #   Main Report class + export methods
    data.py                #   Data normalization (pandas/polars/dict/list)
    exporters.py           #   HTML / folder / JSON exporters
    themes.py              #   Theme system (light / dark / executive)
    exceptions.py          #   Custom exception hierarchy
    cli.py                 #   Click-based CLI (validate, serve, version)
    renderer/              #   Prebuilt React assets (JS + CSS)
    templates/             #   Jinja2 HTML templates

  frontend/                # React source (development only)
    src/
      components/          #   React block components
      theme.ts             #   MUI theme definitions
      registry.tsx         #   Block type → component mapping
      types.ts             #   TypeScript interfaces

  tests/                   # Python test suite
  examples/                # Example scripts

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🛠️ Development

Prerequisites

• Python 3.11+
• Node.js 18+ (frontend development only)
• Make

Setup

git clone https://github.com/UnicoLab/HolySheet.git
cd HolySheet

# Full development setup (frontend + Python)
make dev

# Or step by step:
make frontend-install   # Install frontend npm dependencies
make frontend-build     # Build React app → src/holysheet/renderer/
make install            # Install Python package in editable mode

Common Commands

make test              # Run Python test suite
make lint              # Lint with ruff
make typecheck         # Type-check with mypy (strict mode)
make format            # Auto-format with ruff
make build             # Build distributable wheel + sdist
make clean             # Clean all build artifacts

Releases

HolySheet uses python-semantic-release with conventional commits:

Prefix	Effect
feat:	Minor version bump
fix: / perf:	Patch version bump
BREAKING CHANGE:	Major version bump

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🗺️ Roadmap

• 📊 Advanced chart types (Sankey, Gantt, DAG, Correlation Matrix)
• 🔍 Interactive filters and global filter bar
• 📑 Multi-page tabbed report navigation
• 🎨 Custom theme API + enterprise branding
• 🔐 Password-protected & expiring reports
• 📥 CSV download buttons on tables & charts
• 📓 Jupyter notebook integration
• 🎯 Chart annotations (vertical lines, point markers)
• 📽️ Presentation mode (sections as slides)
• 🌗 Dark/Light theme toggle in viewer
• 🏗️ Report templates (SalesDashboard, ExecutiveSummary, OpsMonitor)
• 🔧 Hot-reload dev server + report linting + report diff CLI
• 🤖 AI narrative blocks (AIInsight with OpenAI / Anthropic / Google)
• 📄 PDF export (Playwright / Chrome)
• 🗄️ SQL Block (client-side query engine)
• 🔊 Voice narration (Web Speech API)
• 📈 Anomaly detection on charts (IQR + MAD)
• ☁️ Cloud publish CLI (S3 / GCS)
• 📊 Google Sheets data source
• 🔄 Cross-block reactive filtering
• 📜 Virtual scrolling for large tables
• 🗺️ Report navigator (minimap)
• 📱 PWA mode + responsive layouts
• 📊 PowerPoint export
• 🧩 Custom React component injection
• 💬 Local chatbot over report data

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🤝 Contributing

Contributions are welcome! Here's how to get started:

1. Fork the repository
2. Create a feature branch: git checkout -b feat/amazing-feature
3. Write your changes with tests
4. Check everything passes: make lint && make typecheck && make test
5. Commit with conventional commits: feat:, fix:, docs:, etc.
6. Open a Pull Request

See the Contributing Guide for detailed instructions.

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📄 License

MIT License — see LICENSE for details.

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Built with ❤️ by UnicoLab

_{Holy Sheet, that's a beautiful dashboard! 🙌}

_{PyPI · Documentation · Issues · Discussions}