- New 'Platform' section detailing Tauri 2 Android app (WebView, Deep Link OAuth, Camera)
- Add Android CI/CD info, build commands, and GitHub Actions workflow reference
- Tagline updated to include 'Rust ONNX Runtime' and 'Tauri 2 Android'
- Architecture tree now shows apps/tauri/
- Components table: Android App row with Tauri 2, Rust, WebView, Deep Link OAuth
- Tech Stack: separate 'Inference Engine' section with Rust/Axum/ONNX Runtime bolded
- Prerequisites: Java 21 + Android SDK added
- Dev commands: tauri dev and tauri android dev included
- Cakupan updated: Web + Android (Tauri 2) now in-scope
Co-Authored-By: Claude <noreply@anthropic.com>
- Root README redesigned as landing page with 7 sub-chapters
- Each child README gets navigation header + footer linking back to root
- Cross-links between Machine_Learning, ml-service, and infra READMEs
- Reduced duplication: root summarizes, children provide full detail
- Net -207 lines, cleaner structure
Co-Authored-By: Claude <noreply@anthropic.com>
- Update docker-compose.yml MODEL_PATH from best_model.keras to model.onnx
- Fix Machine_Learning/README.md TOC and section numbering (remove duplicate section 9, add Validasi Parity ONNX as section 9)
- Clarify parity validation as manual/recommended, not mandatory CI
- Update CLAUDE.md to document Rust/Axum/ONNX Runtime ML service and ONNX export workflow
- Simplify root README.md ML service section with port clarification (8001 local, 8000 container)
- Remove stale endpoint examples from root README (documented in apps/ml-service/README.md)
- Ensure no FastAPI/Uvicorn references in deployment documentation
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Update documentation to reflect migration from FastAPI/Uvicorn to Rust/Axum with ONNX Runtime:
- Root README: Update ML service description, tech stack, prerequisites, and run instructions to use Rust/Cargo instead of Python/Uvicorn
- Root README: Update model path references from best_model.keras to model.onnx
- Root README: Add model.onnx to artifact lists and generated files
- Root README: Update troubleshooting section with Rust-specific guidance
- Machine_Learning/README: Add table of contents entry for ONNX conversion
- Machine_Learning/README: Add Tahap 5 section documenting ONNX conversion with convert_onnx.py and validate_onnx_parity.py
- Machine_Learning/README: Update output table to include model.onnx with Rust ONNX Runtime usage
- apps/ml-service/README: Create comprehensive documentation for Rust Axum ONNX service including setup, endpoints, environment variables, testing, and troubleshooting
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
asepharyana