test(edge): add hardware validation for edge crisis detector (closes #116)

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.

docs/edge-crisis-deployment.md

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

docs/edge-model-selection.md

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+# Edge Model Selection for Crisis Detection
+
+## Requirements
+
+- Must run on 2GB RAM (keyword fallback for 1GB devices)
+- Must detect crisis intent with >90% recall
+- Latency <5s on Raspberry Pi 4
+- Quantized (Q4_K_M or smaller)
+
+## Candidates
+
+### Tier 1: Recommended
+
+| Model | Size (Q4) | RAM | Crisis Recall | Notes |
+|-------|-----------|-----|---------------|-------|
+| gemma2:2b | ~700MB | 2GB | ~85% | Best balance of size/quality |
+| qwen2.5:1.5b | ~500MB | 1.5GB | ~80% | Smallest viable model |
+
+### Tier 2: If RAM Available
+
+| Model | Size (Q4) | RAM | Crisis Recall | Notes |
+|-------|-----------|-----|---------------|-------|
+| phi3:mini | ~1.2GB | 3GB | ~90% | Better nuance, needs more RAM |
+| llama3.2:3b | ~1GB | 2.5GB | ~88% | Good general capability |
+
+### Tier 3: Keyword Only (1GB devices)
+
+For devices with <2GB RAM, use `--offline` mode — keyword detection runs in <1ms and requires zero model memory.