menuvision

# MenuVision - Restaurant Menu Builder

Build a beautiful HTML photo menu for any restaurant from URLs, PDFs, or photos.

## When to Use

When the user wants to create a digital menu for a restaurant. Triggers: "build a menu", "create restaurant menu", "menu from PDF", "menu from photos", "digital menu", "menuvision".

## Quick Start

```
1. Extract:  URL/PDF/photo  →  menu_data.json     (Gemini Vision)
2. Generate: menu_data.json →  images/*.jpg        (Gemini Image)
3. Build:    menu_data.json + images → Menu.html   (CSS/JS inline, images relative)
```

### Example usage (ask the AI):
- "Build a menu for https://www.shoyu.at/menus"
- "Create a photo menu from this PDF" (attach file)
- "Make a digital menu from these photos of a restaurant menu"

## Pipeline Components

The AI agent creates these scripts:

| Script | Purpose |
|--------|---------|
| `extract_menu.py` | Extract menu data from URL/PDF/photo → structured JSON |
| `generate_images.py` | Generate food photos via Gemini Image |
| `build_menu.py` | Build HTML menu from JSON + images (CSS/JS inline, images as relative paths) |
| `publish_menu.py` | (Optional) Publish HTML to GitHub Pages |

---

## DATA CONTRACT (Critical)

All three pipeline stages share this exact JSON schema. The AI agent MUST use these field names — any deviation breaks the pipeline.

### menu_data.json Schema

```json
{
  "restaurant": {
    "name": "Restaurant Name (if visible)",
    "cuisine": "cuisine type (Chinese, Indian, Austrian, Japanese, etc.)",
    "tagline": "any subtitle or tagline"
  },
  "sections": [
    {
      "title": "Section Name (in primary language)",
      "title_secondary": "Section name in secondary language (if present, else empty string)",
      "category": "food or drink",
      "note": "Any section note (e.g. 'served with rice', 'Mon-Fri 11-15h')",
      "items": [
        {
          "code": "M1",
          "name": "Dish Name (primary language)",...