export const ai = {
  identifyFoodFromPhoto: `Analyze this food image and identify ALL separate food items visible.

For EACH distinct item, provide:
- foodName: The food item name
- hpPredIngredients: ingredients you are confident are present
- hrPredIngredients: ingredients that might be present but you're less certain about
- boundingBox: Normalized coordinates (0-1 range) for the item location { x, y, width, height } where x,y is top-left corner
- confidence: Confidence score 0-1

Return a JSON object with an "items" array. Example:
{
  "items": [
    {"foodName": "Bulgogi", "hpPredIngredients": ["beef", "onion", "sesame"], "hrPredIngredients": ["garlic", "sugar"], "boundingBox": {"x": 0.1, "y": 0.1, "width": 0.4, "height": 0.4}, "confidence": 0.95},
    {"foodName": "Kimchi", "hpPredIngredients": ["napa cabbage", "gochugaru"], "hrPredIngredients": ["garlic", "fish sauce"], "boundingBox": {"x": 0.5, "y": 0.2, "width": 0.3, "height": 0.3}, "confidence": 0.9}
  ]
}

Return ONLY valid JSON, no other text.`,

  identifyItemFromCrop: `You are a food identification AI.

Analyze the food and provide 2-3 possible interpretations (candidates), ranked by likelihood.
IMPORTANT: Always provide at least 2 candidates when the protein/filling is not clearly visible (e.g., tacos, burritos, dumplings, fried rice, curry, noodles, sandwiches, wraps). Different proteins or preparations should be separate candidates.

For each candidate, split ingredients into:
- hpPredIngredients: ingredients you are confident are present
- hrPredIngredients: ingredients that might be present but you're less certain about

Return a JSON object with:
- candidates: Array of up to 3 candidates, each with { foodName, hpPredIngredients, hrPredIngredients }

Example response:
{
  "candidates": [
    {"foodName": "Beef Taco", "hpPredIngredients": ["tortilla", "beef", "lettuce"], "hrPredIngredients": ["sour cream", "cheese", "tomato"]},
    {"foodName": "Pork Taco", "hpPredIngredients": ["tortilla", "pork", "lettuce"], "hrPredIngredients": ["sour cream", "cheese", "tomato"]}
  ]
}

Return ONLY valid JSON, no other text.`,

  identifyIngredients: (foodName: string, hasImage: boolean) =>
    `For the food item "${foodName}", analyze ${hasImage ? 'the image and ' : ''}provide ingredients split by confidence.

Return a JSON object with:
- hpPredIngredients: ingredients you are confident are in this item
- hrPredIngredients: ingredients that might be present but you're less certain about

Keep each list to 3-8 ingredients. Return ONLY valid JSON.`,

  estimateNutrition: (
    foodName: string,
    ingredients: string[],
    allNutrients: string,
    dgaNutrients: string
  ) =>
    `Estimate nutritional values for one serving of: ${foodName}
Ingredients: ${ingredients.join(', ')}

Return a JSON object with:
- novaClass: NOVA food classification (1=unprocessed, 2=processed culinary, 3=processed, 4=ultra-processed)
- nutrition: Estimated nutritional values per serving. Include all nutrients that apply.

Available nutrient fields:
${allNutrients}

USDA Dietary Guidelines food group equivalents:
${dgaNutrients}

DGA estimation guide:
- usa_gov_dga_vegetables_cup_eq: 1 cup raw leafy = 0.5, 1 cup other veg = 1
- usa_gov_dga_fruits_cup_eq: 1 medium fruit = 1, 1/4 cup dried = 0.5
- usa_gov_dga_dairy_cup_eq: 1 cup milk/yogurt = 1, 1.5 oz cheese = 1
- usa_gov_dga_whole_grains_oz_eq: 1 slice bread = 1, 1/2 cup cooked grain = 1
- usa_gov_dga_healthy_fats_tsp_eq: 1 tbsp oil = 3, 1 oz nuts = 3

Example:
{"novaClass": 3, "nutrition": {"kcal": 450, "protein_g": 25, "carbs_g": 35, "fat_g": 20, "fiber_g": 4, "vitamin_d_mcg": 2, "omega_3_mg": 100, "usa_gov_dga_vegetables_cup_eq": 0.5}}

Return ONLY valid JSON.`,

  recommendIngredients: (params: {
    days: number;
    gapLines: string;
    recentIngredients: string;
    restrictions: string;
    preferences: string;
    conditions: string;
  }) =>
    `You are a nutrition expert. Based on ${params.days}-day nutrition data, recommend ingredients and recipes.

**${params.days}-Day Nutrition Deficiencies** (sorted by health impact):
${params.gapLines}

**Recently Eaten (AVOID recommending these)**:
${params.recentIngredients}

**User Profile**:
- Food restrictions: ${params.restrictions}
- Food preferences: ${params.preferences}
- Health conditions: ${params.conditions}

**Task**:
1. List 15-25 ingredients that address these deficiencies
2. IMPORTANT: Do NOT recommend ingredients the user recently ate (listed above)
3. Sort ingredients so eating top N provides maximum health benefit
4. Suggest 5 menu ideas using those ingredients (can be recipes to cook at home or dishes to order when eating out)

**Format**: Return valid JSON only, no markdown
{"ingredients":["..."],"foods":[{"name":"...","ingredients":["..."]}]}`,

  judgePredictions: (candidateCount: number, candidateDescriptions: string) =>
    `You are an expert food critic judging AI food identification accuracy.
I will show you a photo and ${candidateCount} descriptions generated by different AI models.
Which description best matches the photo?

Candidates:
${candidateDescriptions}

Return EXACTLY this format:
Winner: <number>
Report: <exactly 50 words explaining why that option best matches the photo.>`,
};