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Implements #116 — hardware validation testing for edge crisis detector on Raspberry Pi 4 and other edge devices. Adds edge detector (keyword + optional Ollama model), crisis_resources.json, deployment docs, and two test files: - test_edge_detector.py: unit tests for keyword logic - test_edge_detector_hardware.py: hardware validation suite Hardware validation measures keyword detection (<1ms), model inference (<5s on Pi 4), offline operation, and provides reproducible benchmark via `python3 edge/detector.py --benchmark`. Re-implements the functionality from closed PR #111 with expanded tests.
104 lines
2.4 KiB
Markdown
104 lines
2.4 KiB
Markdown
# Crisis Detection on Edge Devices
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Deploy a minimal crisis detection system on low-power devices for offline use.
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## Why Edge?
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A person in crisis may not have internet. The model must run locally:
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- No cloud dependency
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- No API keys needed
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- Works on airplane mode, rural areas, network outages
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- Privacy: text never leaves the device
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## Target Hardware
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| Device | RAM | Expected Latency | Notes |
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|--------|-----|------------------|-------|
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| Raspberry Pi 4 (4GB) | 4GB | 2-5s per inference | Recommended. Use Q4_K_M quant. |
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| Raspberry Pi 3B+ | 1GB | Keyword-only | Not enough RAM for model. Use keyword detector. |
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| Old Android phone | 2-4GB | 1-3s | Termux + llama.cpp. ARM NEON optimized. |
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| Any Linux laptop | 4GB+ | <1s | Full model possible. |
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## Quick Start (Raspberry Pi 4)
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### 1. Install Ollama
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```bash
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curl -fsSL https://ollama.ai/install.sh | sh
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```
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### 2. Pull a small crisis-capable model
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```bash
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ollama pull gemma2:2b
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```
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### 3. Clone and test
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```bash
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git clone <repo-url>
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cd turboquant
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python3 edge/detector.py --text "I want to kill myself"
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```
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### 4. Hardware validation (P2 issue #116)
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Run the built-in benchmark to validate offline operation and latency:
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```bash
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# Test keyword-only (works without any model)
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python3 edge/detector.py --offline --benchmark
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# Test with model inference (requires ollama + model)
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python3 edge/detector.py --benchmark
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# Expected outputs:
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# - Keyword detection: <1ms (instant)
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# - Model inference: <5000ms on Pi 4 (5s threshold)
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# - Network independent: YES (resources cached locally)
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```
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### 5. Systemd service (optional)
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Create `/etc/systemd/system/crisis-detector.service`:
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```ini
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[Unit]
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Description=Crisis Detector Edge Service
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After=network.target
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[Service]
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Type=simple
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ExecStart=/usr/bin/python3 /path/to/turboquant/edge/detector.py --interactive
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Restart=on-failure
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User=pi
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[Install]
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WantedBy=multi-user.target
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```
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```bash
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sudo systemctl enable crisis-detector
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sudo systemctl start crisis-detector
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```
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## Model Selection
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See [docs/edge-model-selection.md](edge-model-selection.md) for detailed comparison.
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## Offline Resource Cache
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Crisis resources are stored in `edge/crisis_resources.json` and require no internet to display.
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## Crisis Resources
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When crisis is detected, the detector displays:
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- 988 Suicide & Crisis Lifeline (call/text 988)
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- Crisis Text Line (text HOME to 741741)
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- SAMHSA Helpline
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- Veterans Crisis Line
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- Self-help grounding techniques
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All resources work without internet connection.
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