DEPLOY.md

Deploying to Hugging Face Spaces

A note on hardware / ZeroGPU

This app is CPU-only by design — all local ML (intent classifier, anomaly detector, TF-IDF retrieval) runs on scikit-learn, and the LLM call goes to the remote HF Inference API rather than running locally.

If your account only offers the ZeroGPU hardware tier (some free/new accounts can't select CPU-basic for new Spaces), that's fine: app.py includes a small @spaces.GPU-decorated health-check function purely so the platform's ZeroGPU compatibility check passes at startup. It's never called on the actual request path, so it adds no latency or GPU cost — you can safely select ZeroGPU hardware when creating the Space.

Two ways to deploy: the web UI (easiest, no git needed) or the CLI/git route.

Option A — Web UI upload (fastest)

1. Go to https://huggingface.co/new-space
2. Fill in:
   • Space name: e.g. daifuku-warehouse-ai
   • License: MIT (or your choice)
   • Select the Space SDK: Gradio
   • Space hardware: CPU basic (free tier is enough for this app)
   • Visibility: Public (so you can share the link with Daifuku)
3. Click Create Space.
4. On the new Space page, click Files → Add file → Upload files, and upload the entire project folder contents (keep the folder structure: app.py, requirements.txt, README.md, src/, models/, data/, assets/). Drag-and-drop the whole folder works in most browsers.
5. Wait for the Space to build (check the Logs tab if it fails — almost always a missing/incompatible package version).
6. Once it shows "Running", your demo is live at: https://huggingface.co/spaces/<your-username>/daifuku-warehouse-ai

Option B — git (recommended if you'll keep iterating)

# 1. Install the CLI and log in (needs a token with "write" scope)
pip install huggingface_hub
huggingface-cli login

# 2. Create the Space (or create it via the web UI first, then just clone it)
huggingface-cli repo create daifuku-warehouse-ai --type space --space_sdk gradio

# 3. Clone it, copy in the project files, and push
git clone https://huggingface.co/spaces/<your-username>/daifuku-warehouse-ai
cd daifuku-warehouse-ai
cp -r /path/to/this/project/* .
git add .
git commit -m "Initial commit: Smart Warehouse AI Assistant"
git push

The Space will automatically build from requirements.txt and launch app.py (as declared in the README's YAML front matter: sdk: gradio, app_file: app.py).

Enabling the LLM (recommended before sharing with Daifuku)

By default the Space runs in retrieval-only fallback mode — it still works, but answers are extractive rather than LLM-generated. To turn on real LLM responses:

1. Create an access token at https://huggingface.co/settings/tokens (a "Read" token is sufficient for Inference API calls).
2. In your Space, go to Settings → Variables and secrets → New secret.
   • Name: HF_TOKEN
   • Value: your token
3. (Optional) Add another secret/variable LLM_MODEL_ID if you want a different hosted model than the default Qwen/Qwen2.5-7B-Instruct (any chat-capable model available via HF Inference Providers works).
4. Restart the Space (Settings → Factory reboot, or just wait — it picks up new secrets on the next restart).

Re-training / updating the models

The Space loads pre-built artifacts from models/ and data/ — it does not retrain on startup, so boot time stays fast. If you change src/data_generation.py, src/intent_model.py, or src/anomaly_model.py, regenerate everything locally before pushing:

python build_artifacts.py
git add models/ data/ assets/
git commit -m "Retrain models"
git push

Sharing with Daifuku

Once it's live, share the Space URL directly:

https://huggingface.co/spaces/<your-username>/daifuku-warehouse-ai

Consider also linking the Model Evaluation tab specifically in your application/cover letter, since it's the clearest evidence of rigorous, reproducible ML work rather than just a UI demo.