# Local deployment

## Desktop / local service integration
The supported entry point is `src.inference.predict_wav`. Install requirements and keep `model.h5`, `config.json`, `labels.json`, and `src/` together. There is no network dependency in the inference code.

## Android / Kernel integration notes
Kernel's supplied architecture requires all live inference to remain on-device. This package is a Python/PyTorch training artifact, not yet a drop-in Android artifact. Before shipping:
1. Freeze the audio frontend and compare it against the training implementation using a golden WAV fixture.
2. Convert the model to an Android-supported runtime (e.g. LiteRT/TFLite) and test numerical parity; do not rename `.pt`/`.h5` to `.tflite`.
3. Benchmark on the actual iQOO 15: latency, thermal/battery, noise, sample-rate path and memory.
4. Validate with field-collected grain-pest recordings and verified physical inspections.
5. Keep outputs experimental until false-positive, false-negative, uncertainty and cross-device performance are evaluated.
6. Store model version, audio quality and human-verified outcome separately. Never treat a Clean prediction as proof of pest-free grain.

## Suggested production architecture
Microphone -> signal-quality gate -> exactly matched local preprocessing -> local model -> experimental result UI -> local SQLite record. GCP services, if enabled, should sync only authorized metadata and must not receive raw audio or perform inference.