a

Machine_Learning/.gitignore

@@ -15,3 +15,5 @@ dataset/
 .claude/
 dataset_split/
 dataset_jagung.zip
+best_model/
+model/

Machine_Learning/model/saved_model/fingerprint.pb

@@ -1 +1 @@
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Machine_Learning/requirements.txt

@@ -9,3 +9,4 @@ split-folders
 scikit-learn
 tf2onnx
 onnxruntime
+huggingface_hub

Machine_Learning/upload_hf.py

@@ -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()