Document fullstack app commands and fix Moon config
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@@ -55,6 +55,46 @@ jupyter notebook notebook.ipynb
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There is no project test suite, lint command, or build system configured in the current repository.
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## Fullstack app commands
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The TypeScript application scaffold lives at the repository root and uses Bun workspaces with Moon tasks.
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Install dependencies:
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```bash
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bun install
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```
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Run all development tasks through Moon:
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```bash
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bun run dev
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```
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Run type checks:
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```bash
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bun run typecheck
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```
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Run production builds:
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```bash
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bun run build
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```
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Run the API directly:
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```bash
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cd apps/api && bun run start
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```
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Run the web app directly:
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```bash
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cd apps/web && bun run dev
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```
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## High-level architecture
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- `Machine_Learning/preprocessing.py` prepares the training dataset locally. It extracts three source ZIP files, merges selected class folders into `dataset/`, maps selected Mandarin labels from Dataset 3 via `desc.json`, removes known problematic image files, then creates `dataset.zip` for upload to Google Drive/Colab.
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@@ -62,6 +102,16 @@ There is no project test suite, lint command, or build system configured in the
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- `Machine_Learning/save_model.py` is the production export step. It loads `best_model/best_model.keras`, rebuilds a clean EfficientNetV2B0 architecture without training-time augmentation layers, copies weights into that model, exports `model/saved_model/`, and writes `model/model.tflite`.
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- TensorFlow.js export is intentionally done with the `tensorflowjs_converter` CLI rather than from Python to avoid protobuf/runtime conflicts documented in the README.
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## Fullstack application architecture
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The root TypeScript workspace is a Bun + Moon monorepo:
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- `apps/web/` contains the React + Vite + TypeScript frontend with React Router, TanStack Query, Zustand, Tailwind, and shadcn/ui-style components.
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- `apps/api/` contains the Elysia backend with health/status routes and Drizzle/PostgreSQL configuration.
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- `packages/shared/` contains shared TypeScript types and utilities consumed by both apps.
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The backend reads `DATABASE_URL` for Drizzle/PostgreSQL, but the initial health/status endpoints do not require a live database connection.
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## Model labels and dataset mapping
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The classifier targets four Indonesian labels:
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