README.md

metadata

title: Smart Warehouse AI Assistant
emoji: 🏭
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 5.9.1
app_file: app.py
pinned: false
license: mit
short_description: LLM warehouse copilot with predictive maintenance

🏭 Smart Warehouse AI Assistant

An LLM-powered, retrieval-grounded AI copilot for automated warehouse operations -- built as a portfolio / application project. Combines an LLM assistant (RAG), an intent classifier, an inventory/order query layer, and an Isolation-Forest predictive-maintenance model, with a full Model Evaluation tab reporting real accuracy/F1/ROC-AUC metrics on held-out test data.

👉 Live demo: add your Space URL here once deployed, e.g. https://huggingface.co/spaces/<your-username>/daifuku-warehouse-ai

Tabs

1. 💬 AI Assistant — ask free-text warehouse-ops questions; answers are grounded via TF-IDF retrieval over a small knowledge base and generated by a hosted LLM (Hugging Face Inference API), with a transparent retrieval-only fallback if no API key is configured.
2. 📦 Inventory & Order Query — natural-language queries over synthetic inventory / order tables (SKU, zone, order-id extraction).
3. ⚠️ Predictive Maintenance — Isolation Forest anomaly detector over conveyor/crane motor sensor readings (temperature, vibration, current, belt speed).
4. 📊 Model Evaluation — accuracy, macro-F1, confusion matrices, ROC-AUC, retrieval hit-rate, and latency benchmarks, all computed on held-out data by build_artifacts.py.
5. ℹ️ About — project write-up, architecture diagram, tech stack.

Quick start (local)

git clone <this-repo>
cd daifuku-warehouse-ai
pip install -r requirements.txt

# (re)generate datasets, train models, produce evaluation plots/metrics
python build_artifacts.py

# run the app
python app.py

Open the printed local URL (usually http://127.0.0.1:7860).

Enabling full LLM responses

The app works out of the box in retrieval-only fallback mode (no external API calls). To enable real LLM-generated answers:

1. Create a Hugging Face access token: https://huggingface.co/settings/tokens
2. Set it as an environment variable / Space secret named HF_TOKEN.
3. (Optional) Set LLM_MODEL_ID to override the default model (Qwen/Qwen2.5-7B-Instruct) with any chat-capable model available via HF Inference Providers.

export HF_TOKEN=hf_xxxxxxxxxxxxxxxxxxxx
python app.py

Deploying to Hugging Face Spaces

See DEPLOY.md for full step-by-step instructions.

Project structure

daifuku-warehouse-ai/
├── app.py                  # Gradio app (5 tabs)
├── build_artifacts.py      # generates data, trains models, evaluates, saves plots
├── requirements.txt
├── src/
│   ├── data_generation.py  # synthetic intent / inventory / sensor datasets
│   ├── knowledge_base.py   # warehouse-ops knowledge base (RAG source docs)
│   ├── retriever.py        # TF-IDF retriever
│   ├── intent_model.py     # intent classifier (train/predict)
│   ├── anomaly_model.py    # Isolation Forest anomaly detector
│   ├── llm_client.py       # HF Inference API client + fallback
│   └── inventory_db.py     # NL -> structured query helpers
├── models/                 # trained model artifacts (.joblib)
├── data/                   # generated datasets + evaluation JSON
└── assets/                 # evaluation plots (confusion matrices, ROC curve)

Evaluation summary

See the in-app Model Evaluation tab for full details (confusion matrices, per-class precision/recall, retrieval hit-rate table, latency benchmark). Headline numbers from the included run:

Component	Metric	Score
Intent classifier	Accuracy	~99%
Intent classifier	Macro F1	~99%
Anomaly detector	F1	~97%
Anomaly detector	ROC-AUC	~1.00
RAG retriever	Hit-rate@2	100%

(Computed on synthetic, held-out test data — see the Evaluation tab for methodology notes.)

License

MIT — feel free to fork and adapt.