README.md

metadata

title: Jeeva Embedding Service
emoji: 🐕
colorFrom: red
colorTo: yellow
sdk: docker
pinned: false

Jeeva Embedding Service

Single-purpose FastAPI service that accepts a dog photo and returns two 512-dim embedding vectors:

• face_embedding — derived from the top 40% crop of the image (heuristic face region)
• body_embedding — derived from the full image

Uses MegaDescriptor-T-224 from the wildlife-tools package.

Endpoints

POST /embed

• Accepts: multipart/form-data with a file field (image)
• Returns: { face_embedding: float[], body_embedding: float[], dims: 512 }

GET /health

• Returns: { status: "ok", model_loaded: bool, last_embed_ts: float | null }

Notes

• First request after a cold start will be slow (~60–90s on CPU) while the model loads
• All inference runs on CPU — no GPU required
• Designed for POC scale; not optimised for throughput