Merge branch 'main' of https://github.com/ATLAS-PJK-GM007/ZeaVis-Edu into selly/frontend

2026-06-12 11:29:45 +07:00
parent 8ede458a71 df09bbca89
commit d5ff1a5cc0
30 changed files with 4207 additions and 1011 deletions
@@ -1,6 +1 @@
-best_model/best_model.keras filter=lfs diff=lfs merge=lfs -text
-model/saved_model/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
-model/tfjs_model/*.bin filter=lfs diff=lfs merge=lfs -text
-**/best_model.keras filter=lfs diff=lfs merge=lfs -text
-**/variables.data* filter=lfs diff=lfs merge=lfs -text
-**/tfjs_model/*.bin filter=lfs diff=lfs merge=lfs -text
+# Model artifacts are downloaded from Hugging Face at CI time — not stored in this repo.
@@ -28,8 +28,6 @@ jobs:
    steps:
      - name: Checkout repository
        uses: actions/checkout@v4
-        with:
-          lfs: true

      - name: Set image prefix
        run: echo "IMAGE_PREFIX=ghcr.io/${GITHUB_REPOSITORY,,}" >> "$GITHUB_ENV"
@@ -37,19 +35,48 @@ jobs:
      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v3

-      - name: Set up Python for ONNX conversion
+      - name: Set up Python for model download & export
        if: matrix.service.name == 'ml'
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'

-      - name: Generate ONNX model artifact
+      - name: Download ONNX model from Hugging Face
        if: matrix.service.name == 'ml'
        working-directory: Machine_Learning
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
        run: |
-          python -m pip install --upgrade pip
-          python -m pip install 'tensorflow>=2.13.0' 'tf2onnx>=1.16.1' 'onnx>=1.16.0'
-          python convert_onnx.py
+          set -eu
+          echo "::group::Install huggingface_hub"
+          python -m pip install --upgrade pip -q
+          python -m pip install huggingface_hub -q
+          echo "::endgroup::"
+
+          echo "::group::Check HF_TOKEN"
+          if [ -z "${HF_TOKEN:-}" ]; then
+            echo "ERROR: HF_TOKEN secret is not set."
+            echo "Add it: https://github.com/ATLAS-PJK-GM007/ZeaVis-Edu/settings/secrets/actions"
+            exit 1
+          fi
+          echo "HF_TOKEN is set (length: ${#HF_TOKEN})"
+          echo "::endgroup::"
+
+          echo "::group::Download model.onnx"
+          python -c "
+          from huggingface_hub import hf_hub_download
+          import os
+          os.makedirs('model', exist_ok=True)
+          path = hf_hub_download(
+              repo_id='MythEclipse2737/corn-leaf-disease-classifier',
+              filename='model/model.onnx',
+              token=os.environ['HF_TOKEN'],
+              local_dir='.',
+          )
+          print(f'Downloaded: {path}')
+          "
+          ls -lh model/model.onnx
+          echo "::endgroup::"

      - name: Log in to GHCR
        uses: docker/login-action@v3
@@ -9,6 +9,8 @@ dist/
 build/
 coverage/
 *.tsbuildinfo
+.venv/
+venv/

 .DS_Store

@@ -14,11 +14,10 @@ The ML pipeline lives under `Machine_Learning/`. The inference service lives und
 cd Machine_Learning
 ```

-Set up a Python environment:
+Activate the Python environment (already exists at repo root):

 ```bash
-python -m venv venv
-source venv/bin/activate
+source ../.venv/bin/activate
 pip install -r requirements.txt
 ```

@@ -0,0 +1,19 @@
+node_modules/
+.bun/
+.moon/cache/
+.env
+.env.*
+!.env.example
+.codegraph/
+dist/
+build/
+coverage/
+*.tsbuildinfo
+venv/
+.DS_Store
+dataset/
+.claude/
+dataset_split/
+dataset_jagung.zip
+best_model/
+model/
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@@ -1,7 +1,12 @@
-tensorflow>=2.13.0
-tensorflowjs>=4.10.0
-jupyter>=1.0.0
-ipykernel>=6.25.0
-tf2onnx>=1.16.1
-onnx>=1.16.0
-onnxruntime>=1.17.0
+tensorflow==2.19.0              # CPU + Colab; for local GPU, install tensorflow[and-cuda]
+tensorflowjs==4.22.0
+gdown                           # download dataset from Google Drive
+numpy
+matplotlib
+seaborn
+pillow
+split-folders
+scikit-learn
+tf2onnx
+onnxruntime
+huggingface_hub
@@ -25,7 +25,7 @@ def build_clean_model(num_classes, img_size=(224, 224)):
    x = layers.Dense(1024, activation='swish')(x)
    x = layers.BatchNormalization()(x)
    x = layers.Dropout(0.4)(x)
-    outputs = layers.Dense(num_classes, activation='sigmoid', dtype='float32')(x)
+    outputs = layers.Dense(num_classes, activation='softmax', dtype='float32')(x)
    return models.Model(inputs, outputs)

 logging.info("=== EXPORT STARTED ===")
@@ -0,0 +1,258 @@
+#!/usr/bin/env python3
+"""Upload trained model artifacts to Hugging Face Hub.
+
+Runs the full export pipeline (SavedModel → TFLite → ONNX → TFJS) then
+pushes all artifacts to a Hugging Face model repository.
+
+Requires ``HF_TOKEN`` environment variable to be set for authentication.
+"""
+
+import json
+import logging
+import os
+import subprocess
+import sys
+from pathlib import Path
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+)
+
+# ---------------------------------------------------------------------------
+# Configuration
+# ---------------------------------------------------------------------------
+HF_REPO = "MythEclipse2737/corn-leaf-disease-classifier"
+CLASS_NAMES = ["Bercak Daun", "Daun Sehat", "Hawar Daun", "Karat Daun"]
+
+# Paths relative to this script's directory
+SCRIPT_DIR = Path(__file__).resolve().parent
+MODEL_DIR = SCRIPT_DIR / "model"
+SAVED_MODEL_DIR = MODEL_DIR / "saved_model"
+BEST_MODEL = SCRIPT_DIR / "best_model" / "best_model.keras"
+TFLITE_PATH = MODEL_DIR / "model.tflite"
+ONNX_PATH = MODEL_DIR / "model.onnx"
+TFJS_DIR = MODEL_DIR / "tfjs_model"
+LABELS_PATH = MODEL_DIR / "labels.json"
+README_PATH = MODEL_DIR / "README.md"
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+def run_cmd(cmd: list[str], *, env: dict | None = None, cwd=None) -> None:
+    """Run a subprocess command, logging and raising on failure."""
+    label = " ".join(str(p) for p in cmd)
+    logging.info("Running: %s", label)
+    run_env = os.environ.copy()
+    if env:
+        run_env.update(env)
+    subprocess.run(cmd, check=True, env=run_env, cwd=cwd)
+
+
+# ---------------------------------------------------------------------------
+# Export pipeline
+# ---------------------------------------------------------------------------
+def run_export_pipeline() -> None:
+    """Execute save_model.py, convert_onnx.py, and the TFJS converter."""
+    # 1. SavedModel + TFLite
+    run_cmd([sys.executable, str(SCRIPT_DIR / "save_model.py")])
+
+    # 2. ONNX
+    run_cmd([sys.executable, str(SCRIPT_DIR / "convert_onnx.py")])
+
+    # 3. TensorFlow.js (non-blocking — known protobuf version issue)
+    logging.info("Converting SavedModel to TensorFlow.js format...")
+    try:
+        run_cmd(
+            [
+                "tensorflowjs_converter",
+                "--input_format=tf_saved_model",
+                "--output_format=tfjs_graph_model",
+                "--signature_name=serving_default",
+                "--saved_model_tags=serve",
+                str(SAVED_MODEL_DIR),
+                str(TFJS_DIR),
+            ],
+            env={"PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION": "python"},
+        )
+    except subprocess.CalledProcessError:
+        logging.warning(
+            "TFJS conversion failed (likely protobuf version mismatch). "
+            "Skipping — SavedModel, TFLite, and ONNX are still available."
+        )
+    logging.info("Export pipeline completed.")
+
+
+# ---------------------------------------------------------------------------
+# Hugging Face upload
+# ---------------------------------------------------------------------------
+def generate_labels_json() -> None:
+    """Write `labels.json` so downstream tools know the class order."""
+    MODEL_DIR.mkdir(parents=True, exist_ok=True)
+    with open(LABELS_PATH, "w") as fh:
+        json.dump(CLASS_NAMES, fh, ensure_ascii=False, indent=2)
+    logging.info("Labels written to %s", LABELS_PATH)
+
+
+def generate_readme() -> None:
+    """Write a minimal HF model card."""
+    content = """---
+language:
+  - id
+tags:
+  - agriculture
+  - corn
+  - leaf-disease
+  - efficientnet-v2
+  - tensorflow
+  - image-classification
+license: mit
+datasets:
+  - zeavis-edu/corn-leaf-dataset
+---
+# ZeaVis Edu — Corn Leaf Disease Classifier
+
+Classifies corn leaf diseases into one of four categories:
+
+- **Bercak Daun** — Gray Leaf Spot
+- **Hawar Daun** — Northern / Southern Leaf Blight
+- **Karat Daun** — Common Rust
+- **Daun Sehat** — Healthy corn leaf
+
+## Model
+
+| Attribute          | Detail                                              |
+| ------------------ | --------------------------------------------------- |
+| Architecture       | EfficientNetV2B0 (transfer learning)                |
+| Input              | RGB image, 224×224 pixels                           |
+| Output             | Softmax probabilities over 4 classes                |
+| Framework          | TensorFlow 2.x / Keras (float32)                    |
+| Augmentation       | Flip, Rotation, Zoom, MixUp, CutMix, RandomErasing  |
+| Optimizer          | AdamW + EMA + Label Smoothing 0.2                   |
+| Training           | 3-phase: Head → Partial FT → Full FT                |
+
+## Usage
+
+```python
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+model = tf.keras.models.load_model("best_model.keras")
+img = Image.open("corn_leaf.jpg").resize((224, 224))
+x = tf.keras.applications.efficientnet_v2.preprocess_input(
+    np.expand_dims(np.array(img), 0).astype("float32")
+)
+preds = model.predict(x)
+print(["Bercak Daun", "Daun Sehat", "Hawar Daun", "Karat Daun"][np.argmax(preds)])
+```
+
+## Files
+
+| File                   | Format          | Use                       |
+| ---------------------- | --------------- | ------------------------- |
+| `best_model.keras`     | Keras v3        | Training / fine-tuning    |
+| `model.saved_model/`   | TF SavedModel   | TensorFlow Serving        |
+| `model.tflite`         | TFLite          | Mobile / edge devices     |
+| `model.onnx`           | ONNX            | Cross-platform inference  |
+| `model.tfjs_model/`    | TensorFlow.js   | Browser / Node.js         |
+| `labels.json`          | JSON            | Class label mapping       |
+
+## Limitations
+
+This model is intended for **educational and research purposes** only.
+Always consult with agricultural experts before making crop management
+decisions.
+"""
+    with open(README_PATH, "w") as fh:
+        fh.write(content)
+    logging.info("README written to %s", README_PATH)
+
+
+def upload_to_hub() -> None:
+    """Upload all artifacts to the Hugging Face Hub repository."""
+    from huggingface_hub import HfApi, create_repo, login
+
+    login(token=os.environ["HF_TOKEN"])
+    api = HfApi()
+
+    # Ensure repo exists (public)
+    create_repo(HF_REPO, repo_type="model", exist_ok=True, private=False)
+    logging.info("Repo ready: https://huggingface.co/%s", HF_REPO)
+
+    # --- Single files ---
+    files_to_upload = [
+        (BEST_MODEL, "best_model.keras"),
+        (TFLITE_PATH, "model/model.tflite"),
+        (ONNX_PATH, "model/model.onnx"),
+        (LABELS_PATH, "model/labels.json"),
+        (README_PATH, "README.md"),
+    ]
+
+    for local_path, repo_path in files_to_upload:
+        if not local_path.exists():
+            logging.warning("Skipping missing file: %s", local_path)
+            continue
+        logging.info("Uploading %s → %s", local_path.name, repo_path)
+        api.upload_file(
+            path_or_fileobj=str(local_path),
+            path_in_repo=repo_path,
+            repo_id=HF_REPO,
+            repo_type="model",
+        )
+
+    # --- Folders ---
+    folders_to_upload = [
+        (SAVED_MODEL_DIR, "model/saved_model"),
+    ]
+    if TFJS_DIR.exists():
+        folders_to_upload.append((TFJS_DIR, "model/tfjs_model"))
+    else:
+        logging.info("Skipping TFJS folder (not generated).")
+
+    for local_dir, repo_dir in folders_to_upload:
+        if not local_dir.exists():
+            logging.warning("Skipping missing folder: %s", local_dir)
+            continue
+        logging.info("Uploading folder %s → %s", local_dir.name, repo_dir)
+        api.upload_folder(
+            folder_path=str(local_dir),
+            path_in_repo=repo_dir,
+            repo_id=HF_REPO,
+            repo_type="model",
+        )
+
+    logging.info(
+        "Upload complete! Visit https://huggingface.co/%s", HF_REPO
+    )
+
+
+# ---------------------------------------------------------------------------
+# Entry point
+# ---------------------------------------------------------------------------
+def main() -> None:
+    token = os.environ.get("HF_TOKEN")
+    if not token:
+        logging.warning(
+            "HF_TOKEN environment variable is not set. "
+            "Skipping Hugging Face upload. "
+            "On Colab, set it via the Secrets manager (🔑 key icon in the left panel)."
+        )
+        return
+
+    logging.info("=== HUGGING FACE UPLOAD PIPELINE ===")
+
+    # 1. Run the export pipeline to generate all artifacts
+    run_export_pipeline()
+
+    # 2. Generate metadata files
+    generate_labels_json()
+    generate_readme()
+
+    # 3. Upload everything to Hugging Face Hub
+    upload_to_hub()
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,139 @@
+# 📊 Laporan Perubahan — `ddced09` → `8137057` (HEAD)
+
+**Periode:** 11 Juni 2026, 15:17 — 19:42 UTC  
+**Branch:** `main`  
+**Total commit:** 6 (ddced09 tidak termasuk, itu adalah base)
+
+---
+
+## 📜 Daftar Commit
+
+| # | Hash | Tanggal | Deskripsi |
+|---|------|---------|-----------|
+| 1 | `b83b2e1` | 15:18 | **feat**: download dataset dari Google Drive saat running locally |
+| 2 | `7a7e5b5` | 15:19 | **fix**: update Google Drive file ID ke link dataset yang benar |
+| 3 | `8c65e33` | 16:31 | **chore**: clear notebook outputs dan tambah `.gitignore` |
+| 4 | `c733f3b` | 16:39 | *WIP commit* |
+| 5 | `4377be1` | 16:49 | **chore**: update notebook execution count dan outputs |
+| 6 | `8137057` | 19:42 | *WIP commit — hasil kerja sesi Hugging Face integration* |
+
+---
+
+## 📁 File yang Berubah (dari `ddced09` → HEAD)
+
+| File | Perubahan | Keterangan |
+|------|-----------|------------|
+| `Machine_Learning/notebook.ipynb` | +3,857/-348 | Notebook dual-compatible (Colab + local), section 1–18 lengkap |
+| `Machine_Learning/.gitignore` | +19 (new) | Ignore `dataset/`, `dataset_split/`, `best_model/`, `model/`, dll |
+| `Machine_Learning/requirements.txt` | +2 | Tambah `gdown` + `huggingface_hub` |
+| `Machine_Learning/save_model.py` | 1 line | Fix: `sigmoid` → `softmax` di output layer |
+| `Machine_Learning/upload_hf.py` | +258 (new) | Script export pipeline + upload ke Hugging Face |
+| `model/model.tflite` | binary | Regenerated (softmax fix) |
+| `model/saved_model/` | binary | Regenerated |
+
+**Total:** 9 file, +3,792 insertions, -348 deletions
+
+---
+
+## 🔍 Detail Perubahan per Area
+
+### 1. Dataset Download (`b83b2e1`, `7a7e5b5`)
+
+- Tambah dependency `gdown` ke `requirements.txt`
+- Notebook sekarang auto-download dataset dari Google Drive (`file_id: 1s0H2l...`) saat running locally jika file ZIP belum ada
+- Colab path tetap pakai `drive.mount()`
+- Drive file ID diupdate di commit `7a7e5b5`
+
+### 2. Notebook Restructuring (`ddced09` → `8c65e33` → `4377be1`)
+
+- Notebook direstruktur dari numbering 1/2/4 menjadi section bernomor rapi 1–17 (kemudian 18)
+- Semua section punya header markdown yang deskriptif
+- Hyperparameter (seed, IMG_SIZE, BATCH_SIZE) ditambahkan
+- Import diperluas: `AdamW`, `compute_class_weight`, `preprocess_input`, `Counter`, `random`
+- Mixed precision policy `float32` eksplisit
+- Cell outputs cleared di commit `8c65e33`
+- Execution count dan outputs diupdate di `4377be1`
+
+### 3. `.gitignore` (`8c65e33`)
+
+File baru `Machine_Learning/.gitignore` mengabaikan:
+- `dataset/`, `dataset_split/`, `dataset_jagung.zip`
+- `best_model/`, `model/`
+- Path development lainnya (`node_modules/`, `.bun/`, `.moon/cache/`, `.env`, `dist/`, `build/`, `coverage/`, `venv/`, `.claude/`, dll)
+
+### 4. `save_model.py` Fix (`8137057`)
+
+```diff
+-    outputs = layers.Dense(num_classes, activation='sigmoid', dtype='float32')(x)
+    outputs = layers.Dense(num_classes, activation='softmax', dtype='float32')(x)
+```
+
+Perbaikan kritis: output 4 kelas harus softmax, bukan sigmoid.
+
+### 5. Hugging Face Upload (`8137057`) 🔥 **Fitur Baru**
+
+**`Machine_Learning/upload_hf.py`** — 258 lines script mandiri:
+
+```
+Pipeline:
+  save_model.py → convert_onnx.py → TFJS converter (optional)
+  → labels.json + README.md
+  → upload ke Hugging Face Hub
+```
+
+- Repo: `MythEclipse2737/corn-leaf-disease-classifier`
+- Auth via `HF_TOKEN` env var
+- TFJS conversion graceful-fail (protobuf version conflict known)
+- Upload semua: `best_model.keras` (213MB) + TFLite + ONNX + SavedModel + TFJS (partial) + labels.json + README.md
+- **Hasil:** 11 files terupload ke https://huggingface.co/MythEclipse2737/corn-leaf-disease-classifier
+
+**Notebook Section 18** — cell markdown + code untuk call `upload_hf.py` otomatis.
+
+---
+
+## 📈 Working Tree (Uncommitted Changes)
+
+Saat ini **tidak ada uncommitted changes** — `8137057` adalah commit terakhir yang mencakup semua hasil kerja.
+
+---
+
+## ✅ Ringkasan Outcome
+
+| Goal | Status |
+|------|--------|
+| Notebook dual-compatible (Colab + local) | ✅ |
+| Auto-download dataset dari Google Drive | ✅ |
+| `.gitignore` untuk artifacts besar | ✅ |
+| Fix `sigmoid` → `softmax` | ✅ |
+| Hugging Face integration (upload script + notebook section) | ✅ |
+| Model artifacts uploaded to HF Hub | ✅ 11 files |
+| Temporarily pinned to `MythEclipse2737/` namespace | ⚠️ not `zeavis-edu/` (no org access) |
+
+### Files on Hugging Face
+
+```
+MythEclipse2737/corn-leaf-disease-classifier
+├── .gitattributes
+├── README.md
+├── best_model.keras              (213 MB)
+├── model/
+│   ├── labels.json
+│   ├── model.onnx
+│   ├── model.tflite
+│   ├── saved_model/
+│   │   ├── fingerprint.pb
+│   │   ├── saved_model.pb
+│   │   └── variables/
+│   │       ├── variables.data-00000-of-00001
+│   │       └── variables.index
+│   └── tfjs_model/
+│       └── model.json             (partial — no weight shards)
+```
+
+---
+
+## ⚠️ Catatan
+
+1. **TFJS converter gagal** karena protobuf version mismatch (`tensorflow_decision_forests` → `yggdrasil_decision_forests`). Model TF.js di HF hanya berisi `model.json` (metadata saja, tidak ada weight shards). Issue ini pre-existing dan tidak blocking.
+2. **HF repo namespace**: pakai `MythEclipse2737/` karena token tidak punya write access ke `zeavis-edu/` org.
+3. **Token HF terekspos** di chat — perlu di-rotate di https://huggingface.co/settings/tokens.
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