- Integrate Convolutional Block Attention Module (CBAM) for improved feature focus
- Implement temperature scaling and confidence-based status reporting
- Automate dataset acquisition using kagglehub
- Update ONNX opset to 18 and refine preprocessing validation
- Remove binary model artifacts tracked in repo (.tflite, saved_model, tfjs model.json)
- CI now downloads best_model.keras from Hugging Face Hub (MythEclipse2737/corn-leaf-disease-classifier)
- Add save_model.py step before convert_onnx.py in CI pipeline
- Remove LFS patterns from .gitattributes (no longer needed)
- Remove lfs:true from checkout action
The model is fetched at CI time using HF_TOKEN secret — no large
binary files stored in git.
Co-Authored-By: Claude <noreply@anthropic.com>
- Add dep to requirements.txt
- Non-Colab path: download dataset_jagung.zip from Drive using file ID
if not present locally, then extract as usual
- Keeps Colab path unchanged (drive.mount)
Co-Authored-By: Claude <noreply@anthropic.com>
Notable changes:
- Add .gitattributes for LFS tracking on ML model artifacts
- Add AuthGuard to all protected routes + login/register/logout flow
- Add collapsible Sidebar replacing old Navbar
- Redesign MainLayout with sidebar + mobile header
- Add password show/hide toggle to AuthForm
- Add Logout button to MobileNav
- Update footer credit to ATLAS Project - Pijak x IBM SkillsBuild
- Update deploy.yml: generate ONNX in CI (vs HuggingFace download)
- Remove deprecated ML scripts (download/upload model .sh, .gitignore)
- Remove stale MEMORY.md
- Add ML model artifacts (TFLite, SavedModel, TFJS)
- Update notebook.ipynb training pipeline
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>
MythEclipse