title	FactoryFlow
emoji	⚙️
colorFrom	red
colorTo	yellow
sdk	docker
app_port	7860
pinned	false

FactoryFlow

Autonomous predictive maintenance and parts procurement for small manufacturers. Vibration sensor → MOMENT-1-large anomaly detection on AMD MI300X → CrewAI agents (Qwen3-8B) → Proxlock authorization → X402 programmable payment. End-to-end autonomous procurement with one human-in-the-loop step.

Built for the AMD x LabLab.ai Developer Hackathon (May 2026).

Live demo

• AMD MI300X (DigitalOcean): http://165.245.133.72:7860 — full pipeline running on AMD GPU
• Hugging Face Space (CPU mirror): https://huggingface.co/spaces/oabolade23/factoryflow
• Source: https://github.com/oabolade/factoryflow

---

What it does

Manufacturers lose ~$50k per hour of unplanned machine downtime. FactoryFlow turns a vibration sensor on the factory floor into an autonomous procurement loop:

1. A simulated RPi sensor streams 512-point FFT windows over an MCP server
2. MOMENT-1-large scores each window for bearing/gear/imbalance faults on AMD GPU hardware (MI300X via ROCm)
3. The Engineer Agent maps the dominant fault frequency to a part SKU
4. The Procurement Agent (powered by Qwen3-8B) scrapes suppliers via Apify and selects the best price-vs-RUL trade-off
5. Proxlock gates the purchase with a human authorization step
6. X402 executes the payment programmatically

From sensor spike to confirmed PO: under a minute.

---

Architecture

[RPi sensor sim]
       │  MCP / SSE
       ▼
[AMD MI300X / ROCm]
   MOMENT-1-large  → anomaly_score, rul_hours, dominant_hz
   Qwen3-8B        → agent reasoning backbone
       │
       ▼
[CrewAI Crew]
   Engineer Agent      → identify SKU from fault signature
   Procurement Agent   → Apify scrape, pick best supplier
       │
       ▼
[Proxlock] ── human-in-the-loop authorization
       │
       ▼
[X402] ── autonomous programmable payment
       │
       ▼
[Gradio HF Space] — live demo UI (this Space)

---

Prize tracks

• AMD MI300X — MOMENT inference + Qwen3-8B serving both run on AMD ROCm hardware
• Qwen / vLLM — Qwen3-8B is the CrewAI LLM backbone via vLLM (OPENAI_API_BASE swap)
• Hugging Face — this Docker Space; share the URL to drive likes
• X402 — autonomous payment execution on agent decision
• MCP — sensor stream is exposed as an MCP server with SSE transport
• Apify — supplier discovery via the apify/web-scraper actor
• MindsDB — procurement history queried via SQL+AI

---

Local run

python3.12 -m venv .venv && source .venv/bin/activate
pip install torch                           # CPU/MPS for local dev
pip install -r requirements.txt
pip install --no-deps momentfm==0.1.4       # see note below

cp .env.example .env                        # add OPENAI_API_KEY
PYTHONPATH=. python -m src.demo.app

Open http://localhost:7860. The chart auto-polls every 2 seconds.

Smoke tests by checkpoint

PYTHONPATH=. python -m src.inference.rocm_check       # device detection
PYTHONPATH=. python scripts/smoke.py                  # MOMENT inference
PYTHONPATH=. python -m src.agents.orchestrator        # full agent cycle
PYTHONPATH=. python -m src.sensor.mcp_server          # MCP SSE on :8765

---

AMD GPU evidence

Run python -m src.inference.rocm_check on the AMD cloud box. Expected output:

torch:           2.x.x+rocm6.x
backend:         rocm
torch_device:    cuda
name:            AMD Instinct MI300X
vram_gb:         192.0
runtime_version: 6.x
✓ AMD ROCm GPU detected — ready for MI300X demo run.

A screenshot of this output is included in the LabLab submission.

---

Demo mode vs live

DEMO_MODE=true (default) keeps the demo working without third-party API keys by using fixtures for Apify, mock approvals for Proxlock, and simulated transactions for X402. The UI is visually identical to live operation.

To run live, set DEMO_MODE=false and fill in: APIFY_API_TOKEN, PROXLOCK_API_KEY + PROXLOCK_DEVICE_ID, X402_API_KEY + X402_MERCHANT_ID, and (optional) MINDSDB_*.

---

Notes

• momentfm install: the package on PyPI hard-pins old numpy / transformers that conflict with CrewAI and Gradio. Install it with --no-deps after the rest of requirements.txt — the actual code works fine on modern stacks.
• HF Space hardware: this Space runs MOMENT on CPU (~1–2s per window). The live judge demo runs on a separate AMD MI300X cloud box.

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Repo layout

See CLAUDE.md and docs/architecture.md for the full layout and per-file responsibilities. memory.md tracks live build state.