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-model-selection.md

@@ -0,0 +1,28 @@
+# 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.