259 lines
8.6 KiB
Python
259 lines
8.6 KiB
Python
|
#!/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()
|