🍒 CherryScript

The scripting language built for Data Science, Automation & AI — at the speed of thought.

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║   collect → process → predict → deploy  — in plain English.     ║
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📖 Table of Contents

	Section	Description
🍒	What is CherryScript?	Overview & philosophy
✨	Feature Highlights	What you can do
⚡	Quick Start	Up and running in 60 seconds
🔤	Language Syntax	Core language constructs
🗄️	Database Integration	MySQL / PostgreSQL
🤖	AI & Machine Learning	H2O AutoML pipelines
🚀	Model Deployment	REST API in one line
📦	Installation	Full setup guide
🗺️	Roadmap	What's coming next
🤝	Contributing	Join the community
📄	License	MIT

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🍒 What is CherryScript?

CherryScript is an expressive, human-readable scripting language designed to unify data collection, ML pipelines, and AI model deployment behind a single, clean syntax.

Instead of stitching together Python scripts, YAML configs, SQL queries, and deployment manifests — you write plain CherryScript and let the runtime handle the rest.

// Three lines from raw data to live API 🚀
collect images from "dataset/raw"
process using "h2o_automl"
deploy model "vision_classifier" to "http://api.myapp.com/predict"

Built by Cherry Computer Ltd. — making data science accessible, one line at a time.

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✨ Feature Highlights

🗃️ Data Collection Collect images, text, CSV, JSON from local paths or URLs Social media scraping adapters MySQL / PostgreSQL ingestion Unified collect keyword — no boilerplate	⚙️ Data Processing H2O AutoML integration out of the box Built-in DataFrame operations (filter, sort, join) Statistical functions: sum, mean, std, percentile One-liner preprocessing: process using "h2o_automl"
🤖 AI / ML Generation Train classification, regression & clustering models Auto-generate Python, PHP, or Java model wrappers Model leaderboard & performance comparison h2o.automl() — zero-config model selection	🚀 Instant Deployment Deploy any model as a FastAPI REST endpoint Health-check route auto-generated undeploy() for clean teardown One-line: deploy(model, "http://0.0.0.0:8080/predict")
🔌 Multi-Language Export Export trained models to Python, PHP, Java Ready-to-use code scaffolding Works with scikit-learn & H2O MOJO formats	🖥️ Developer Experience Interactive REPL mode (cherryscript -i) Clean CLI interface Friendly error messages .cherry syntax highlighting (VS Code extension coming soon)

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⚡ Quick Start

Install in 30 seconds

# Clone the repository
git clone https://github.com/Infinite-Networker/CherryScript.git
cd CherryScript

# Install core package
pip install -e .

# Or with ML support
pip install -e ".[ml]"

# Verify install
cherryscript --version

Your First Script

Create a file hello.cherry:

// hello.cherry — your first CherryScript program

var name = "World"
var version = 1.0

print(`Hello, ${name}! Welcome to CherryScript v${version}`)

// Variables & types
var fruits  = ["cherry", "mango", "kiwi"]
var profile = { "lang": "CherryScript", "purpose": "AI Automation" }

// Loop
for fruit in fruits {
    print("🍒", fruit)
}

// Functions
fn greet(person) {
    return `Hi ${person}, ready to automate some AI? 🚀`
}

print(greet("Developer"))

Run it:

cherryscript hello.cherry

Interactive REPL

cherryscript --interactive
# >>> var x = 42
# >>> print("The answer is", x)
# The answer is 42

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🔤 Language Syntax

Variables

var message  = "Hello CherryScript"   // mutable
let PI       = 3.14159                // constant-intent
var score    = 98.6
var active   = true
var tags     = ["ml", "automation", "ai"]
var config   = { "debug": false, "max_epochs": 100 }

Control Flow

// if / else if / else
if (score >= 90) {
    print("🏆 Grade: A")
} else if (score >= 75) {
    print("✅ Grade: B")
} else {
    print("📚 Keep studying!")
}

// for-in loop
for tag in tags {
    print("Tag:", tag)
}

// while loop
var counter = 0
while (counter < 5) {
    print("Iteration:", counter)
    counter = counter + 1
}

Functions

fn calculate_accuracy(correct, total) {
    return (correct / total) * 100
}

fn classify_model(auc) {
    if (auc >= 0.95) { return "Excellent 🏆" }
    else if (auc >= 0.85) { return "Good ✅" }
    else { return "Needs Improvement ⚠️" }
}

var accuracy = calculate_accuracy(94, 100)
print("Accuracy:", accuracy, "%")
print("Rating:", classify_model(0.96))

String Interpolation

var model_name = "ChurnPredictor"
var auc        = 0.978

print(`Model ${model_name} achieved AUC of ${auc}`)
// → Model ChurnPredictor achieved AUC of 0.978

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🗄️ Database Integration

// Connect to MySQL or PostgreSQL
var db = connect("mysql://user:password@localhost/sales_db")

// Query data
var customers = db.query("SELECT * FROM customers WHERE active = true")
print("Loaded", len(customers), "customers")

// Process results
var vip = []
for c in customers {
    if (c["lifetime_value"] > 5000) {
        vip.append(c["name"])
    }
}
print("VIP customers:", vip)

📌 Supported databases: MySQL, PostgreSQL
🔜 Coming soon: MongoDB, Redis, BigQuery, Snowflake

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🤖 AI & Machine Learning

Full AutoML Pipeline

// 1. Load your data
var db     = connect("mysql://user:pass@localhost/crm")
var data   = db.query("SELECT age, income, churn FROM customers")

// 2. Build an H2O frame
var frame  = h2o.frame(data)

// 3. Train AutoML — zero config needed
var model  = h2o.automl(frame, "churn")

// 4. Inspect the leaderboard
print("Best model:", model.name)
print("Model type:", model.model_type)
for entry in model.leaderboard {
    print(" -", entry["model_id"], "| AUC:", entry["auc"])
}

// 5. Predict on new data
var new_data    = [{ "age": 28, "income": 72000 }]
var predictions = model.predict(h2o.frame(new_data))
print("Churn probability:", predictions[0]["confidence"])

Export Models

// Generate ready-to-ship model wrappers
export to python    // → model_server.py
export to php       // → ModelPredictor.php
export to java      // → ModelPredictor.java

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🚀 Model Deployment

// Deploy model as a live REST API (FastAPI under the hood)
var endpoint = deploy(model, "http://0.0.0.0:8080/predict")

print("✅ API live at:", endpoint.url)
print("🔍 Health check:", endpoint.url.replace("/predict", "/health"))

// Call your API
// POST http://0.0.0.0:8080/predict
// Body: { "rows": [{ "age": 35, "income": 60000 }] }
// Returns: { "predictions": [{ "prediction": 1, "confidence": 0.85 }] }

// Graceful teardown
undeploy(endpoint, 5.0)
print("🛑 API offline")

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📦 Installation

Prerequisites

Requirement	Version	Notes
Python	≥ 3.8	Required
pip	≥ 21.0	Required
MySQL / PostgreSQL	Any	Optional — needed for DB features
Java 8+	8 or 11	Optional — needed for H2O AutoML

Installation Options

# Option 1: Core only (no ML dependencies)
pip install -e .

# Option 2: With database support
pip install -e ".[db]"

# Option 3: With ML support
pip install -e ".[ml]"

# Option 4: With deployment support
pip install -e ".[deploy]"

# Option 5: Everything
pip install -e ".[all]"

# Option 6: Development mode (includes testing tools)
pip install -e ".[dev]"

Verify Your Install

cherryscript --version
# → CherryScript 1.0.0

cherryscript -c 'print("🍒 CherryScript is ready!")'
# → 🍒 CherryScript is ready!

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📁 Project Structure

CherryScript/
├── 🍒 cherryscript/            # Core package
│   ├── __init__.py             # Version & exports
│   ├── parser.py               # Tokenizer & AST parser
│   ├── cli/
│   │   └── __init__.py         # CLI entry point & REPL
│   └── runtime/
│       ├── __init__.py         # Runtime exports
│       ├── interpreter.py      # Statement evaluator
│       └── adapters.py         # DB, H2O, Deploy adapters
│
├── 📚 docs/                    # Documentation
│   ├── ROADMAP.md              # Feature timeline
│   └── SYNTAX_GUIDE.md         # Full language reference
│
├── 🧪 examples/                # Sample .cherry scripts
│   ├── test.cherry             # Full feature showcase
│   ├── ml_pipeline.cherry      # ML pipeline example
│   ├── data_analysis.cherry    # Business intelligence example
│   └── deploy_api.cherry       # Deployment walkthrough
│
├── 🔬 tests/                   # Test suite
│   ├── test_parser.py
│   └── test_interpreter.py
│
├── ⚙️  .github/
│   ├── workflows/ci.yml        # GitHub Actions CI
│   ├── ISSUE_TEMPLATE/         # Bug & feature templates
│   └── pull_request_template.md
│
├── pyproject.toml              # Package configuration
├── requirements.txt            # Pinned dependencies
├── CHANGELOG.md                # Version history
├── CONTRIBUTING.md             # Contribution guide
└── LICENSE                     # MIT License

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🗺️ Roadmap

See docs/ROADMAP.md for the full detailed plan.

Quarter	Milestone	Status
Q1 2024	✅ v1.0 — Core interpreter, CLI, H2O AutoML, MySQL, REST deploy	Done
Q2 2024	🔜 v2.0 — Plugin system, VS Code extension, NoSQL support	Planned
Q3 2024	🔜 v3.0 — JIT compilation, PyTorch/TF bridge, WASM	Planned
Q4 2024	🔜 v3.5 — Enterprise: RBAC, SSO, Kubernetes deploy	Planned
Q1 2025	🔜 v4.0 — AI-assisted coding, natural language → CherryScript	Planned
Q2 2025	🔜 v5.0 — CherryStudio web IDE, model marketplace	Planned

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🤝 Contributing

We ❤️ contributors! Check out CONTRIBUTING.md for full guidelines.

# 1. Fork the repo on GitHub
# 2. Clone your fork
git clone https://github.com/<your-username>/CherryScript.git
cd CherryScript

# 3. Create a feature branch
git checkout -b feature/my-awesome-feature

# 4. Install dev dependencies
pip install -e ".[dev]"

# 5. Make your changes, run tests
pytest tests/ -v --cov=cherryscript

# 6. Commit & push
git commit -m "feat: add my awesome feature"
git push origin feature/my-awesome-feature

# 7. Open a Pull Request 🎉

🏷️ Good First Issues

Look for issues tagged good-first-issue — these are specifically selected for new contributors.

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🌟 Community & Support

Channel	Link
🐛 Bug Reports	GitHub Issues
💡 Feature Requests	GitHub Issues
💬 Discussions	GitHub Discussions
📖 Documentation	/docs
📋 Changelog	CHANGELOG.md

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📄 License

MIT License — © 2024 Cherry Computer Ltd.

Permission is hereby granted, free of charge, to any person obtaining
a copy of this software to use, copy, modify, merge, publish, distribute,
sublicense, and/or sell copies of the Software.

See LICENSE for full text.

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Made with 🍒 by Cherry Computer Ltd.

"Making Data Science Accessible, One Line at a Time."