Déduire les ingrédients et estimer les calories à partir d'images de nourriture

• •Compte Google et informations d'identification Gmail API
• •Token Bot Telegram

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        "text": "=あなたは料理と栄養の専門家です。提供された料理の画像を分析して、以下の情報を日本語で提供してください：\n\n1. 料理名\n2. 推定される主な食材とその分量（グラム）\n3. 各食材のカロリー\n4. 合計推定カロリー\n5. 栄養バランスの簡単な評価\n\n画像URL: {{ $json.message.photo[0].file_id }}",
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        "jsCode": "// AI Agentから出力されたJSON形式の料理分析結果を取得します。\nconst analysisResult = $input.first().json.output;\n\n// 結果が存在しない、または期待した形式でない場合はエラーを防ぐための処理\nif (!analysisResult || !analysisResult.dishName) {\n  return [{\n    json: {\n      subject: \"料理分析エラー\",\n      htmlBody: \"<p>AIによる分析結果を取得できませんでした。</p>\"\n    }\n  }];\n}\n\n// 1. メールの件名を作成\nconst subject = `【料理分析レポート】 ${analysisResult.dishName}`;\n\n// 2. HTMLメールの本文を生成\nlet htmlBody = `\n<!DOCTYPE html>\n<html lang=\"ja\">\n<head>\n  <meta charset=\"UTF-8\">\n  <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n  <style>\n    body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, 'Helvetica Neue', Arial, sans-serif; line-height: 1.6; color: #333; background-color: #f9f9f9; margin: 0; padding: 0; }\n    .container { max-width: 600px; margin: 20px auto; padding: 25px; background-color: #ffffff; border: 1px solid #e0e0e0; border-radius: 12px; box-shadow: 0 4px 8px rgba(0,0,0,0.05); }\n    h1 { font-size: 26px; color: #2c3e50; border-bottom: 3px solid #3498db; padding-bottom: 10px; margin-top: 0; }\n    h2 { font-size: 20px; color: #e74c3c; margin-bottom: 15px; }\n    h3 { font-size: 18px; color: #34495e; margin-top: 30px; border-bottom: 1px solid #eeeeee; padding-bottom: 8px;}\n    p { margin-top: 0; color: #555; }\n    table { width: 100%; border-collapse: collapse; margin-top: 15px; }\n    th, td { text-align: left; padding: 12px; border-bottom: 1px solid #dddddd; }\n    th { background-color: #f2f2f2; color: #333; }\n    tr:last-child td { border-bottom: none; }\n    .total-calories { text-align: center; background-color: #fff3cd; padding: 15px; border-radius: 8px; margin-top: 20px; }\n    .evaluation { background-color: #eafaf1; padding: 15px; border-left: 5px solid #2ecc71; border-radius: 5px; margin-top: 20px;}\n    .footer { text-align: center; margin-top: 25px; font-size: 12px; color: #999; }\n  </style>\n</head>\n<body>\n  <div class=\"container\">\n    <h1>${analysisResult.dishName}</h1>\n    \n    <div class=\"total-calories\">\n      <h2>合計推定カロリー: ${analysisResult.totalCalories} kcal</h2>\n    </div>\n\n    <h3>詳細な食材リスト</h3>\n    <table>\n      <thead>\n        <tr>\n          <th>食材名</th>\n          <th>分量 (g)</th>\n          <th>カロリー (kcal)</th>\n        </tr>\n      </thead>\n      <tbody>\n`;\n\n// 各食材の情報をテーブルの行として追加\nif (analysisResult.ingredients && analysisResult.ingredients.length > 0) {\n  analysisResult.ingredients.forEach(ingredient => {\n    htmlBody += `\n        <tr>\n          <td>${ingredient.name}</td>\n          <td>${ingredient.amount}</td>\n          <td>${ingredient.calories}</td>\n        </tr>\n    `;\n  });\n}\n\nhtmlBody += `\n      </tbody>\n    </table>\n\n    <h3>栄養バランスの評価</h3>\n    <div class=\"evaluation\">\n      <p>${analysisResult.nutritionEvaluation}</p>\n    </div>\n\n    <div class=\"footer\">\n      <p>このレポートはAIによって自動生成されました。</p>\n    </div>\n  </div>\n</body>\n</html>\n`;\n\n// 3. 後続のGmailノードで使えるようにデータを返す\nreturn [{\n  json: {\n    subject: subject,\n    htmlBody: htmlBody\n  }\n}];"
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        "content": "## 🗒️ Sticky 1: Overview\n**What this template does**\n- Accepts a **food image** and runs **AI-based ingredient inference** → **calorie estimation** → **concise summary**\n- Input can be an **image URL** or **Base64 image** (via Webhook or a messaging app)\n\n**Why it’s useful**\n- Quickly generates a calorie rough estimate and a short nutrition comment for logging, sharing, or feedback loops\n\n**No hardcoded secrets**\n- All API keys must be set via **Credentials/Environment Variables** (never hardcode keys in nodes)"
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        "content": "## 🗒️ Sticky 2: Prerequisites & Credentials\n**Connections**\n- LLM (OpenAI or compatible) with **vision** capability\n- One input channel (Webhook **or** Telegram/LINE, etc.)\n\n**Environment Variables (examples)**\n- `LLM_MODEL` (e.g., `gpt-4o` or any vision-capable model)\n- `LLM_TEMPERATURE` (e.g., `0.3`)\n- `WEBHOOK_SECRET` (optional, if you validate requests)\n\n**Scopes / Permissions**\n- If image URLs are external, ensure they are publicly accessible or use signed/temporary URLs"
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        "content": "## 🗒️ Sticky 3: Input Format (Sample)\n**Expected JSON**\n```json\n{\n  \"imageUrl\": \"https://example.com/dish.jpg\"\n  // or\n  // \"imageBase64\": \"data:image/jpeg;base64,....\"\n}\n```\n**Notes**\n- Template assumes **one image** per request (extend with arrays for multiple images)\n- If you receive platform-specific payloads (e.g., Telegram `file_id`, LINE message objects), **normalize** them first to `imageUrl` or `imageBase64` before calling the LLM"
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      "id": "1bd2ee7d-a5f9-42f4-9974-4698fb05b553",
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      "type": "n8n-nodes-base.stickyNote",
      "position": [
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        "content": "## 🗒️ Sticky 4: Model & Prompt Policy\n**Model**\n- Use a **vision-capable** LLM (e.g., `gpt-4o`)\n- Keep `temperature` low (0.2–0.3) for stable outputs\n\n**Prompt Rules**\n- The model must return **strict JSON only**, with the fields below (no extra text)\n- Keep names concise (e.g., \"grilled chicken\", \"white rice\", \"mixed salad\")\n- Provide amounts in grams when possible (estimates are fine)"
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      "typeVersion": 1
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    {
      "id": "84f0f90c-e819-4022-a76e-b63353ca9968",
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        "content": "## 🗒️ Sticky 5: Output Schema (Fixed)\n**JSON structure**\n```json\n{\n  \"dishName\": \"string\",\n  \"ingredients\": [\n    { \"name\": \"string\", \"amount\": 0, \"calories\": 0 }\n  ],\n  \"totalCalories\": 0,\n  \"nutritionEvaluation\": \"string\"\n}\n```\n**Validation**\n- If fields are empty/missing, route to an **error branch** and return a helpful message for the user"
      },
      "typeVersion": 1
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    {
      "id": "fe6e558b-4f6a-4c0a-9b06-e3939b07be3f",
      "name": "Note 6 : Étapes de test",
      "type": "n8n-nodes-base.stickyNote",
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        "content": "## 🗒️ Sticky 6: Test Steps (Under 1 Minute)\n1) Turn on the **Webhook** node and copy the **Test URL**\n2) Send a sample payload with a valid `imageUrl`\n3) Confirm the run returns the **strict JSON** structure\n4) If using Telegram/Slack replies, authenticate and send a test message to verify delivery"
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    {
      "id": "24bbd216-9c8b-4a4f-b446-dd82585117fd",
      "name": "Note 7 : Erreurs & Limites",
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        "content": "## 🗒️ Sticky 7: Error Handling & Limits\n**Retries**\n- For LLM calls, implement **exponential backoff** on 429/5xx\n- If the image fetch fails, **retry**, then go to **error branch**\n\n**Limits**\n- Watch for model **context/vision limits**; downscale/thumbnail images if needed\n- When rate-limited, use **Delay** + **Retry** to recover gracefully"
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    {
      "id": "55619913-3b70-43d8-ab38-f5b8eb4a6375",
      "name": "Note 8 : Sécurité",
      "type": "n8n-nodes-base.stickyNote",
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        "content": "## 🗒️ Sticky 8: Security & PII\n- Store API keys/tokens in **Credentials/Env Vars** (never in node fields)\n- If image URLs are private, use **signed/temporary URLs**\n- **Do not log** personal data; mask/remove PII from execution logs\n- Use **HTTPS only** for all external requests"
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      "name": "Note 9 : Livraison",
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        "content": "## 🗒️ Sticky 9: Delivery Options (No Gmail)\n- Reply via **Telegram/Slack** or return via **HTTP Response**\n- Optionally log results to **Google Sheets** or **Notion** for history/analytics\n- If email is required, use **SMTP** or a non-Gmail provider (per your stack)"
      },
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    {
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        "content": "## 🗒️ Sticky 10: Extensibility (Review Bonus)\n- **Multilingual output** (ja/en) switch\n- Normalize serving units (piece/slice) → **gram conversion**\n- Categorize components (main dish / side / sauce)\n- Add **evidence-based** nutrition comments (e.g., high fat/high carb) with short rationale"
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