README.md

metadata

title: FitGenie CalorieCLIP
emoji: 🍎
colorFrom: green
colorTo: blue
sdk: docker
app_port: 8000
pinned: false
license: mit
short_description: Instant food calorie estimate API for FitGenie (~51 MAE)

FitGenie CalorieCLIP

Free-to-host instant calorie estimate from food photos for the FitGenie nutrition app.

Model	jc-builds/CalorieCLIP (CLIP ViT-B/32 + regression)
Output	Calories only (not protein/carbs/fat)
Speed	~50–200 ms on CPU
Cost	$0 on Hugging Face Spaces free tier

---

One-click deploy on Hugging Face (free)

Step 1 — Create the Space

1. Go to huggingface.co/new-space
2. Fill in:
   • Owner: your username
   • Space name: fitgenie-calorie-clip (or any name)
   • License: MIT
   • SDK: Docker
   • Hardware: CPU basic (free — 16 GB RAM)
3. Click Create Space

Step 2 — Upload these files

Upload the contents of fitgenie_calorie_clip/ to the Space repo:

app.py
model_core.py
gradio_app.py
Dockerfile
requirements.txt
README.md          ← this file (frontmatter configures the Space)

Or connect your GitHub repo and set the Space to sync from fitgenie_calorie_clip/.

Step 3 — Wait for build

First build takes 5–15 minutes (downloads CLIP + CalorieCLIP weights).

When status is Running, your API base URL is:

https://YOUR-USERNAME-fitgenie-calorie-clip.hf.space

Step 4 — Test

curl https://YOUR-USERNAME-fitgenie-calorie-clip.hf.space/health
# → {"status":"ok","model":"CalorieCLIP"}

curl -X POST https://YOUR-USERNAME-fitgenie-calorie-clip.hf.space/predict \
  -H "Content-Type: application/json" \
  -d '{"image":"'$(base64 -i food.jpg | tr -d '\n')'"}'
# → {"calories":342}

Step 5 — Wire to FitGenie backend

On Render → your fitgenie-backend service → Environment:

CALORIE_CLIP_URL=https://YOUR-USERNAME-fitgenie-calorie-clip.hf.space/predict
CALORIE_CLIP_API_KEY=choose-a-secret-string
FOOD_ANALYSIS_PROVIDER=gpt
OPENAI_API_KEY=sk-...

On the HF Space → Settings → Variables:

CALORIE_CLIP_API_KEY=same-secret-as-backend

Redeploy backend. Photo flow will show real kcal estimates instead of ~400 fallback.

Step 6 — Keep Space awake (optional, free)

Free Spaces sleep after 48 hours of no traffic.

1. Sign up at uptimerobot.com (free)
2. Add monitor: URL https://YOUR-SPACE.hf.space/health, interval 30 min

---

Optional: Gradio demo Space (browser UI)

For a visual demo (no API), create a second Space:

Setting	Value
SDK	Gradio
Hardware	CPU basic
App file	gradio_app.py

Use this README frontmatter instead:

---
title: FitGenie CalorieCLIP Demo
sdk: gradio
sdk_version: 4.44.1
app_file: gradio_app.py
hardware: cpu-basic
---

---

API reference

Endpoint	Method	Body	Response
/health	GET	—	{ "status": "ok", "model": "CalorieCLIP" }
/predict	POST	{ "image": "<base64>" }	{ "calories": 342 }

Optional header: X-API-Key: your-secret (when CALORIE_CLIP_API_KEY is set).

---

Environment variables

Variable	Default	Description
CALORIE_CLIP_API_KEY	(empty)	Require X-API-Key header
CALORIE_CLIP_MODEL_REPO	jc-builds/CalorieCLIP	HuggingFace weights repo
MAX_IMAGE_BYTES	8388608	Max upload size (8 MB)

---

Limits (free tier)

Topic	Detail
Indian food	Weak — trained on US cafeteria + 2 Indian Food-101 classes
Macros	Not supported — use GPT-4o or fitgenie_food_analysis for P/C/F
Thali / multi-item	Poor — single calorie number for whole image
Sleep	After 48h idle — use UptimeRobot

---

Local dev

pip install -r requirements.txt
uvicorn app:app --reload --port 8000
# or visual demo:
python gradio_app.py

---

License

MIT (service code) · CalorieCLIP model MIT · FitGenie integration