CarlosCMora
Complete Cook With Me: vision + fine-tuned planner + FLUX (separate Modal endpoints), ingredient editor, readable UI
fa86b1b | title: Cook With A LLM | |
| emoji: π² | |
| colorFrom: red | |
| colorTo: yellow | |
| sdk: gradio | |
| sdk_version: 6.15.2 | |
| python_version: '3.12' | |
| app_file: app.py | |
| pinned: false | |
| license: apache-2.0 | |
| # π² Cook With Me β Multimodal Sous-Chef | |
| > *Snap your fridge. Pick a dish. Cook step by step. Check your progress with a photo.* | |
| A closed-loop multimodal cooking assistant built for the **Hugging Face Small Models / Big Adventures Hackathon (June 2026)**. | |
| --- | |
| ## How it works | |
| ``` | |
| πΈ Fridge photo βββΆ [Vision Agent] identify ingredients | |
| β | |
| βΌ | |
| [Recipe Planner] propose 3 dishes β full recipe JSON | |
| β | |
| βΌ | |
| [Nutrition Engine] per-serving macros (lookup, no hallucination) | |
| β | |
| βΌ | |
| πΈ Progress photo βββΆ [Progress Validator] go / wait / fix verdict | |
| ``` | |
| 1. **Snap** your fridge or pantry β the fine-tuned vision model identifies every ingredient. | |
| 2. **Pick** one of three AI-suggested dishes tailored to what you have. | |
| 3. **Cook** step by step with a generated recipe and per-serving nutrition info. | |
| 4. **Check** your progress by uploading a photo of your pan β the model tells you *go*, *wait*, or *fix*. | |
| --- | |
| ## Models | |
| | Role | Model | Params | Runtime | | |
| |---|---|---|---| | |
| | Vision + Planner + Validator | `openbmb/MiniCPM-V-4.6` (fine-tuned) | ~4.6B | `transformers` / ZeroGPU | | |
| **Total: ~4.6B parameters** (β€ 32B cap β β significant headroom) | |
| The ingredient-identification model is **fine-tuned** on fridge/pantry photos for higher precision. | |
| --- | |
| ## Badges targeted | |
| | Badge | Status | How | | |
| |---|---|---| | |
| | π― Well-Tuned | β | Fine-tuned MiniCPM-V-4.6 for ingredient detection, published to Hub | | |
| | π¨ Off-Brand | β | Recipe-card UI with custom CSS β Lora serif, warm parchment palette | | |
| | π‘ Sharing is Caring | β | Agent traces shared on Hub | | |
| | π Field Notes | β | Blog post: "Building a closed-loop visual cooking coach" | | |
| --- | |
| ## Architecture highlights | |
| - **Single model, three roles:** MiniCPM-V-4.6 handles vision (ingredients + progress) *and* text planning (recipe JSON generation) β no redundant model downloads. | |
| - **Closed-loop visual validation:** Flux generates step targets β user cooks β vision model compares β a real agent loop, not a wrapper. | |
| - **Hallucination-free nutrition:** macros come from a lookup table, not LLM arithmetic. | |
| - **Robust JSON extraction:** multi-strategy parser handles markdown fences, single quotes, and trailing commas so generation failures degrade gracefully. | |
| --- | |
| ## Track | |
| **Chapter One β Backyard AI** Β· "Build something for someone you actually know." | |
| Submission for the Hugging Face Hackathon Β· June 5β15, 2026. | |