| # Deploying Bio-Bite to a Hugging Face Space |
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| ## 1. Create the Space |
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| 1. Go to [huggingface.co](https://huggingface.co) → profile picture → **New Space** |
| 2. **Space name:** `bio-bite` (owner: `benjac8`) |
| 3. **License:** MIT |
| 4. **SDK:** **Gradio** |
| 5. **Hardware:** **ZeroGPU** ⚠️ *this is the important one* — the free CPU tier is far too slow for a 3B model |
| 6. **Visibility:** Public |
| 7. Create |
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| > ZeroGPU is free but requires a verified email and an account older than 30 days, with a limit of 2 ZeroGPU Spaces per free account. If ZeroGPU isn't offered, see *Troubleshooting* below. |
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| ## 2. Upload the four files |
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| **Files** tab → **Add file → Upload files**, then drag in all of these: |
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| | File | Size | Purpose | |
| |---|---|---| |
| | `app.py` | 13 KB | The application | |
| | `requirements.txt` | <1 KB | Dependencies | |
| | `README.md` | 3 KB | Space card (its YAML header configures the Space) | |
| | `biobite_embeddings.parquet` | 22 MB | Precomputed embeddings (uploads via Git LFS automatically) | |
| | `recovery_guidance.json` | 2 KB | The coded recovery science | |
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| Commit. The Space will start building — watch the **Logs** tab. |
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| First build takes ~5–10 minutes (installing torch/transformers, then downloading the models on first run). |
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| ## 3. Optional: the live-data bonus |
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| **Settings → Variables and secrets → New secret** |
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| - Name: `SPOONACULAR_API_KEY` |
| - Value: your free key from [spoonacular.com/food-api](https://spoonacular.com/food-api) |
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| The app then shows a real photo of the generated dish. Without the key it simply skips the image — nothing breaks. |
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| ## 4. Test it |
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| Click a **Quick Starter**, then **Generate My Bio-Bite**. Expect ~15–25 seconds for the first response (model warm-up), faster afterwards. |
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| Verify: |
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| - [ ] 3 recipe cards appear with sensible macros |
| - [ ] The detected recovery state matches the description |
| - [ ] The generated recipe respects the constraint (e.g. no salmon when you said "no salmon") |
| - [ ] "Why this works" mentions relevant nutrients |
| - [ ] Tomorrow's plan has timed bullets |
| - [ ] Disclaimer is visible |
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| ## 5. Submit |
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| Put both links on Moodle: |
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| - **Dataset:** `https://huggingface.co/datasets/benjac8/bio-bite-recovery-nutrition` |
| - **Space:** `https://huggingface.co/spaces/benjac8/bio-bite` |
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| --- |
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| ## Troubleshooting |
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| **Build fails on `spaces` import** — that's normal locally; on a ZeroGPU Space the package is preinstalled. If you're on CPU hardware, the app falls back automatically (just slowly). |
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| **"GPU quota exceeded"** — the free ZeroGPU allowance is ~5 minutes of GPU time per day (roughly 15–20 requests). Don't burn it on casual testing; save it for the demo. It resets daily. |
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| **Out of memory** — reduce `max_new_tokens` in `app.py` (760 → 500), or switch `GEN_MODEL` to `Qwen/Qwen2.5-1.5B-Instruct` (note: it failed JSON validation in benchmarking, so quality will drop). |
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| **Model returns unexpected format** — the app catches this and asks the user to press again. It's occasional and expected with sampling; pressing again resolves it. |
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| **Slow first request** — the models download on first run (~6 GB). Subsequent requests are much faster. Warm the Space up a few minutes before presenting. |
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| ## Demo-day checklist |
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| - Open the Space ~10 minutes early and run one query to warm it up |
| - Have the dataset page open in a second tab |
| - Know your headline numbers: 10,000 rows · precision@3 = 0.806 · 3 embedding models compared · 3 generators benchmarked |
| - Be ready to explain: *why the science is coded rather than generated*, and *why the 3B model was both faster and more reliable than the smaller ones* |
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