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PneumoOps — Hugging Face Model Hub Upload Script
=================================================
Uploads both model artifacts to the HF Model Hub for versioning.
Run this once after training, and again whenever you retrain.
Usage:
huggingface-cli login # one-time login
python scripts/upload_to_hf.py
Environment variables (override defaults):
HF_MODEL_REPO your-username/pneumoops-chestmnist
HF_TOKEN your write token (if not logged in via CLI)
"""
import os
import json
from pathlib import Path
from huggingface_hub import HfApi, upload_file, create_repo
# ─── Config ───────────────────────────────────────────────────────────────────
ROOT = Path(__file__).resolve().parents[1]
MODEL_DIR = ROOT / "models" / "chestmnist_mobilenetv3"
HF_TOKEN = os.getenv("HF_TOKEN") # optional if already logged in via CLI
MODEL_REPO = os.getenv("HF_MODEL_REPO", "") # e.g. "your-username/pneumoops-chestmnist"
if not MODEL_REPO:
print("\n⚠️ Please set HF_MODEL_REPO environment variable, e.g.:")
print(' export HF_MODEL_REPO="your-hf-username/pneumoops-chestmnist"')
raise SystemExit(1)
# ─── Files to upload ──────────────────────────────────────────────────────────
ARTIFACTS = [
("mobilenetv3_chestmnist.pth", "Model A — Baseline PyTorch checkpoint"),
("mobilenetv3_chestmnist.onnx", "Model B — Optimized ONNX artifact"),
("training_metrics.json", "Training + evaluation metrics (AUROC, AUPRC, F1)"),
("baseline_stats.json", "Pixel distribution stats for drift monitoring"),
("onnx_export_report.json", "ONNX export configuration"),
]
# ─── Model Card ───────────────────────────────────────────────────────────────
MODEL_CARD = """---
license: mit
tags:
- medical
- image-classification
- chest-xray
- mlops
- onnx
- pytorch
- mobilenetv3
datasets:
- medmnist/chestmnist
metrics:
- roc_auc
---
# PneumoOps — ChestMNIST MobileNetV3-small
This repository contains **two model versions** for the PneumoOps MLOps pipeline:
- **Model A (Baseline):** `mobilenetv3_chestmnist.pth` — Standard PyTorch checkpoint
- **Model B (Optimized):** `mobilenetv3_chestmnist.onnx` — ONNX-exported for faster inference
Both models are identical in architecture (MobileNetV3-small) and weights.
The ONNX version is used for inference time optimization in A/B testing.
## Dataset
**ChestMNIST** — 14-class multi-label chest X-ray classification
78,468 training images, 224×224 pixels, grayscale (converted to 3-channel).
## Classes (14)
Atelectasis, Cardiomegaly, Effusion, Infiltration, Mass, Nodule, Pneumonia,
Pneumothorax, Consolidation, Edema, Emphysema, Fibrosis, Pleural Thickening, Hernia
## Performance (Test Set)
| Metric | Score |
|--------|-------|
| Macro AUROC | **0.808** |
| Macro AUPRC | 0.210 |
| Micro F1 | 0.343 |
## Usage in PneumoOps
These artifacts are loaded by the FastAPI backend and selected via a weighted A/B router:
- 60% of requests → PyTorch model
- 40% of requests → ONNX model
The backend also computes a drift score using `baseline_stats.json` to detect
out-of-distribution inputs in real time.
"""
# ─── Main ─────────────────────────────────────────────────────────────────────
def main():
api = HfApi(token=HF_TOKEN)
print(f"\n📦 Creating/verifying model repository: {MODEL_REPO}")
create_repo(
repo_id=MODEL_REPO,
repo_type="model",
exist_ok=True,
token=HF_TOKEN,
)
# Write model card
card_path = MODEL_DIR / "README.md"
card_path.write_text(MODEL_CARD, encoding="utf-8")
print(" Model card written.")
# Upload model card first
print(f"\n⬆️ Uploading artifacts to https://huggingface.co/{MODEL_REPO}")
upload_file(
path_or_fileobj=str(card_path),
path_in_repo="README.md",
repo_id=MODEL_REPO,
repo_type="model",
commit_message="Add model card",
token=HF_TOKEN,
)
# Upload all artifacts
for filename, description in ARTIFACTS:
local_path = MODEL_DIR / filename
if not local_path.exists():
print(f" ⚠️ Skipping {filename} — file not found")
continue
size_mb = local_path.stat().st_size / (1024 * 1024)
print(f" Uploading {filename} ({size_mb:.1f} MB) — {description} ...")
upload_file(
path_or_fileobj=str(local_path),
path_in_repo=filename,
repo_id=MODEL_REPO,
repo_type="model",
commit_message=f"Upload {filename}",
token=HF_TOKEN,
)
print(f"\n✅ All artifacts uploaded!")
print(f" View at: https://huggingface.co/{MODEL_REPO}")
if __name__ == "__main__":
main()
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