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Flavormancer — Flavor-Prediction Training & Demo Setup (R620 / single box)

Scope: this is the Python training + demo track (Track A in ARCHITECTURE.md) — the model pipeline and the quick FastAPI/HTML workbench, run on the R620. The product app the team builds is .NET/React and lives in /api + /frontend; it consumes the ONNX models this training pipeline produces. Train here, ship there.

Target box: Dell R620, Linux, no GPU → CPU only. The taste + demo stack is light (RDKit, scikit-learn, skl2onnx) and installs cleanly — no version-fussy deep-learning dependencies. Everything below assumes a fresh, isolated env.

0. Prereqs

1. Create an isolated workspace

mkdir -p ~/flavormancer-train && cd ~/flavormancer-train
python3 -m venv .venv && source .venv/bin/activate
# (or: mamba create -n flavor python=3.12 -y && mamba activate flavor)

2. Install the stack (light — no GPU, no DeepChem)

pip install rdkit pandas scikit-learn numpy openpyxl pyarrow
# openpyxl: read the ChemTastesDB .xlsx  |  pyarrow: read/write the .parquet files
pip install skl2onnx onnxruntime    # export + self-validate the taste ONNX models
pip install pubchempy               # name -> SMILES, for the demo UX
pip install umap-learn              # for the flavor-space map

Aroma is deferred (see docs/AROMA.md) — no commercially usable public odor data is good enough to train it. The heavy OpenPOM/DeepChem GNN stack is therefore not installed here. When licensed (PMP 2001) or customer data exists, add it then; the aroma pipeline (build_aroma_dataset.py + train_aroma.py) is committed and ready to run.

3. Get the taste data

# ChemTastesDB v2.0 — PRIMARY source, CC-BY-4.0, ~4075 molecules, multi-class taste:
curl -L -o ChemTastesDB_database.xlsx \
  "https://zenodo.org/records/14963136/files/ChemTastesDB_database.xlsx?download=1"

# SweetenersDB v2.0 — sweetness INTENSITY regressor. MIT, from the authors' own lab:
git clone --depth 1 https://github.com/chemosim-lab/SweetenersDB.git
# relative-to-sucrose sweetness (SMILES + logS); the build script reads it if present.

Other taste sources are auto-merged only if present, but note the licensing decisions in docs/SOURCES.md: cosylab/bittersweet is AGPL and FlavorDB is NonCommercial — both are off by default and not used.

4. Sanity check

python - <<'PY'
import rdkit, sklearn, skl2onnx
from rdkit import Chem
print("rdkit ok:", Chem.MolToSmiles(Chem.MolFromSmiles("c1ccccc1")))
PY

5. Build the dataset, then train

python build_taste_dataset.py   # merges sources -> taste_master.parquet (+ sweet_intensity.parquet)
python train_taste.py           # taste heads (sklearn) — minutes, even merged
python export_onnx.py           # export taste models to ONNX (+ roundtrip self-validation)

train_taste.py trains one head per taste that clears the data threshold (sweet/bitter/umami train; sour also gets a small-data indicative head; salty stays a validated rule), plus a sweetness-intensity regressor. Prints AUROC / R² you can quote. Saves to taste_models/.

6. One molecule's full flavor read (taste runs today)

python predict.py "OC(=O)CC(O)(CC(=O)O)C(=O)O"   # citric acid → sour=True, low sweet/bitter

Returns every taste head present + sweetness intensity + sour/salty flags + the physicochemical / stability / chemesthesis / safety packs. substitute() provides the nearest-neighbor substitution search. This is what the workbench screen calls.

7. Run the demo workbench

pip install fastapi "uvicorn[standard]"
uvicorn app:app --host 0.0.0.0 --port 8000   # then open http://<r620-ip>:8000/

Notes

  • The taste/demo stack is light and CPU-only — no CUDA, no DeepChem.
  • Keep raw data + model artifacts small; the whole project sits well under a few GB.
  • Aroma, when it comes, trains on licensed/customer data and exports to ONNX (RandomForest) or runs behind a thin Python sidecar (OpenPOM GNN) — built then, not now. See docs/AROMA.md.