phantom-ship: services.ollama + qwen2.5:3b-instruct for bon extraction
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1 changed files with 17 additions and 2 deletions
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@ -394,10 +394,22 @@ in
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};
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};
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# bon — receipt scanner Mini App (camera capture + gallery + OCR).
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# Ollama — local LLM runtime, used by bon's structured-data extraction
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# step. Listens on 127.0.0.1:11434 only (not exposed over ZT). The
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# qwen2.5:3b-instruct model is pre-pulled at boot via loadModels.
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services.ollama = {
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enable = true;
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host = "127.0.0.1";
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port = 11434;
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# ~2.5 GB on disk after Q4_K_M quantization. Phantom-ship has plenty.
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loadModels = [ "qwen2.5:3b-instruct" ];
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};
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# bon — receipt scanner Mini App (camera capture + gallery + OCR + extract).
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# Code rsync'd from ~/python-projects/26_bon/ to /home/danny/bon/
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# Images on disk under /home/danny/.local/share/bon/images/<user_id>/
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# OCR via tesseract (binary on PATH; server uses subprocess directly).
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# Structured extraction via local Ollama (qwen2.5:3b-instruct).
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systemd.services.bon = let
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pythonEnv = pkgs.python3.withPackages (ps: with ps; [
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fastapi
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@ -405,6 +417,7 @@ in
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python-telegram-bot
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python-multipart
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pillow
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httpx # for the Ollama HTTP call from extract.py
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]);
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# English-only for now — Danish receipts in DK are mostly English chars
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# plus prices, which `eng` handles fine. Add more languages later if
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@ -414,7 +427,7 @@ in
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};
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in {
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description = "bon FastAPI server (receipt scanner)";
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after = [ "network-online.target" ];
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after = [ "network-online.target" "ollama.service" ];
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wants = [ "network-online.target" ];
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wantedBy = [ "multi-user.target" ];
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path = [ pythonEnv tesseractEng ];
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@ -422,6 +435,8 @@ in
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SHIPYARD_BOT_TOKEN_FILE = "/home/danny/.secrets/telegram-bot-token-shipyard";
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BON_DB_PATH = "/home/danny/.local/share/bon/bon.db";
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BON_IMAGES_DIR = "/home/danny/.local/share/bon/images";
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BON_OLLAMA_URL = "http://127.0.0.1:11434";
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BON_OLLAMA_MODEL = "qwen2.5:3b-instruct";
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};
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serviceConfig = {
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WorkingDirectory = "/home/danny/bon";
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