Taipy

# MISSION: Scaffold "Taipy" Production Architecture
You are an expert Senior Staff Software Architect and Full-Stack Engineer. Your mission is to scaffold and implement a production-grade, highly reliable, and modular codebase following the proven architecture of **Taipy**.
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## 1. PROJECT SPECIFICATIONS & BENCHMARK
- **Reference Architecture**: Taipy
- **What It Does**: Open-source Python application platform combining interactive frontend generation with scenario and data pipeline orchestration.
- **Domain & Category**: AI Application Framework
- **Production Scale**: 6-20 people
- **Development Mode**: HYBRID
- **Architectural Rationale**: Taipy enables Python data scientists to build full-stack web applications and data DAG pipelines using pure Python bindings.
- **Live Website Reference**: https://www.taipy.io
- **Source Repository**: https://github.com/Avaiga/taipy
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## 2. PRODUCTION TECH STACK
- **Full Stack Array**: Python, TypeScript, React, Flask, Material UI, SQLAlchemy, Pandas
- **Primary Language(s)**: Python, TypeScript, CSS, JavaScript, Jupyter Notebook, Jinja
- **License of the reference repo**: Apache-2.0
- **Frontend**: React — Reactive UI rendering runtime compiled with React Compiler and Webpack; Material UI — Component library powering dashboard controls, date pickers, and tree navigation; TypeScript — Web client islands, custom data visualizers, and Arrow deserialization bridges
- **Backend & APIs**: Python — Core scenario orchestrator, execution DAG engine, and server-side state runtime; Flask — Embedded web framework serving API endpoints and Socket.IO bidirectional channels
- **Data & persistence**: SQLAlchemy — Relational persistence adapter supporting scenario and pipeline metadata storage; Pandas — High-throughput tabular data manipulation and transformation pipelines
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## 3. KEY ARCHITECTURAL DECISIONS (audited from https://github.com/Avaiga/taipy @ 5bcb574)
1. **Modular Architecture Separating Core and GUI**: Decouples pipeline orchestration and scenario management in taipy-core from web presentation in taipy-gui, allowing headless or interactive execution.
2. **Bidirectional WebSockets with Flask-SocketIO**: Synchronizes Python variable states directly with React frontend components over WebSockets without manual REST endpoint boilerplate.
3. **Columnar Serialization via Apache Arrow**: Transmits large tabular datasets and dataframes to browser clients using Apache Arrow binary streaming for rapid visual rendering.
4. **DAG Workflow Orchestration with NetworkX**: Models computational pipelines as directed acyclic graphs to manage execution task dependencies, caching, and conditional execution.
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## 4. NON-NEGOTIABLE ARCHITECTURAL GUARDRAILS
1. **Monorepo & Modular Separation**:
   - Structure as a Turborepo monorepo with strict package boundaries:
     - `apps/web`: Application UI, routing, layouts, and server endpoints.
     - `packages/ui`: Shared design tokens, CSS variables, and Radix UI primitive components.
     - `packages/db`: Database schemas, client singleton, declarative migrations, and seed scripts.
     - `packages/config`: Shared TypeScript, ESLint, and build configurations.
2. **Strict Type Safety & Zero `any` Policy**:
   - Enable `strict: true`, `noImplicitAny: true`, and `strictNullChecks: true`.
   - Validate ALL external inputs, API request bodies, and query parameters with **Zod** schemas before execution.
3. **Frontend & Rendering Guidelines**:
   - Isolate interactive UI state to leaf components. Keep core pages lightweight and performant.
4. **Design System & Aesthetics**:
   - Keep every color, radius, shadow and font in a single token file (CSS variables) and consume tokens everywhere; never hardcode hex values in components.
   - Prefer crisp 1px borders and one subtle shadow scale over blurry default shadows. Pair one sans-serif for body/headings with one monospace for tags, badges, metrics, and code.
5. **Data Layer & Reliability**:
   - Write declarative schema definitions with foreign keys, composite indexes on queried filters, and automated timestamp triggers.
   - Use connection pooling and prepared statements for serverless database execution.
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## 5. STEP-BY-STEP SCAFFOLDING ROADMAP
- **Phase 1: Workspace & Root Config**: Initialize package manager, monorepo configuration (`turbo.json`, `tsconfig.base.json`, `package.json`).
- **Phase 2: Database Schema & Client**: Set up the data layer (SQLAlchemy, Pandas): client, connection pool, models, and migration scripts.
- **Phase 3: Design Tokens & UI Primitives**: Build accessible `Button`, `Input`, `Card`, `Badge`, and layout wrappers inside `packages/ui`.
- **Phase 4: Core Application Routes & Handlers**: Implement primary authentication, user session handling, and application routes.
- **Phase 5: Quality Assurance & Build Verification**: Run `tsc --noEmit`, ESLint, Prettier, and smoke test suites to ensure zero compilation or runtime errors.
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## 6. EXECUTION INSTRUCTIONS
1. Review all specifications, architectural guardrails, and stack choices above.
2. Present the full monorepo directory tree structure.
3. Systematically generate the complete, production-ready codebase according to the 5-phase roadmap above — starting with the root workspace setup, followed by the database schema, UI design system package, and full-stack application routes until the repository is fully scaffolded and ready to run.