README.md

metadata

title: hsFAST ML Service
emoji: 🧬
colorFrom: blue
colorTo: indigo
sdk: docker
app_port: 7860
pinned: false

hsFAST ML Service

FastAPI service serving the ESM2-35M + LoRA protein stability (ΔG) model for the Enzyme Stability ML Prediction Platform.

This is the ML inference backend only — it is called server-to-server by the Node/Express API, not directly by the browser.

Endpoints

• GET /health — liveness + model status
• GET /model/info — architecture + training metadata
• POST /predict — ΔG for a single sequence (negative ΔG = more stable)
• POST /predict/batch — ΔG for up to 100 sequences
• POST /suggest — residue-level ΔΔG scan → ranked stabilizing mutations + hotspots
• GET /dataset/stats — training dataset statistics

Notes

• The ESM2 weights are bundled in models/best_model.pt (Git LFS).
• The tokenizer is pre-cached at build time (see Dockerfile) so runtime offline mode works.
• First request after the Space wakes from sleep takes ~30–60 s (model load).