Add AI-powered nutrition and plan modules

Introduces DSPy-based nutrition and plan generation modules, including image analysis for nutritional info and personalized diet/exercise plans. Adds new API endpoints for health metrics/goals, nutrition image analysis, and plan management. Updates models, schemas, and backend structure to support these features, and includes initial training data and configuration for prompt optimization.

backend/app/ai/nutrition.py

@@ -1,25 +1,107 @@
+import base64
+
 import dspy
 from pydantic import BaseModel, Field
 
+from app.config import settings
+
+
 class NutritionalInfo(BaseModel):
-    name: str
-    calories: float
-    protein: float
-    carbs: float
-    fats: float
+    reasoning: str = Field(description="Step-by-step reasoning for the nutritional estimates")
+    name: str = Field(description="Name of the food item")
+    calories: float = Field(description="Estimated calories")
+    protein: float = Field(description="Estimated protein in grams")
+    carbs: float = Field(description="Estimated carbohydrates in grams")
+    fats: float = Field(description="Estimated fats in grams")
     micros: dict | None = None
 
+
 class ExtractNutrition(dspy.Signature):
-    """Extract nutritional information from a food description."""
+    """Extract nutritional information from a food description.
+
+    You must first provide a detailed step-by-step reasoning analysis of the ingredients,
+    portions, AND preparation methods (cooking oils, butter, sauces) before estimating values.
+    Verify if the caloric totals match the sum of macros (multiplying protein/carbs by 4, fats by 9).
+    """
+
     description: str = dspy.InputField(desc="Description of the food or meal")
-    nutritional_info: NutritionalInfo = dspy.OutputField(desc="Nutritional information as a structured object")
+    nutritional_info: NutritionalInfo = dspy.OutputField(desc="Nutritional information with reasoning")
+
+
+class AnalyzeFoodImage(dspy.Signature):
+    """Analyze the food image to estimate nutritional content.
+
+    1. Identify all food items and estimated portion sizes.
+    2. CRITICAL: Account for hidden calories from cooking fats, oils, and sauces (searing, frying).
+    3. Reason step-by-step about the total composition before summing macros.
+    """
+
+    image: dspy.Image = dspy.InputField(desc="The food image")
+    description: str = dspy.InputField(desc="Additional user description", default="")
+    nutritional_info: NutritionalInfo = dspy.OutputField(desc="Nutritional information with reasoning")
+
 
 class NutritionModule(dspy.Module):
     def __init__(self):
         super().__init__()
         self.extract = dspy.ChainOfThought(ExtractNutrition)
+        self.analyze_image = dspy.ChainOfThought(AnalyzeFoodImage)
+
+        # Load optimized prompts if available
+        import os
+
+        compiled_path = os.path.join(os.path.dirname(__file__), "nutrition_compiled.json")
+        if os.path.exists(compiled_path):
+            self.load(compiled_path)
+            print(f"Loaded optimized DSPy prompts from {compiled_path}")
+        else:
+            print("No optimized prompts found, using default zero-shot.")
 
     def forward(self, description: str):
-        return self.extract(description=description)
+        pred = self.extract(description=description)
+
+        # Assertion: Check Macro Consistency
+        calc_cals = (
+            (pred.nutritional_info.protein * 4) + (pred.nutritional_info.carbs * 4) + (pred.nutritional_info.fats * 9)
+        )
+
+        # dspy.Suggest is not available in dspy>=3.1.0
+        # dspy.Suggest(
+        #     abs(calc_cals - pred.nutritional_info.calories) < (pred.nutritional_info.calories * 0.20),
+        #     f"The sum of macros ({calc_cals:.1f}) should match the total calories "
+        #     f"({pred.nutritional_info.calories}). Check your math.",
+        # )
+        return pred
+
+    def forward_image(self, image_url: str, description: str = ""):
+        image = dspy.Image(image_url)
+        pred = self.analyze_image(image=image, description=description)
+
+        # Assertion: Check Macro Consistency
+        calc_cals = (
+            (pred.nutritional_info.protein * 4) + (pred.nutritional_info.carbs * 4) + (pred.nutritional_info.fats * 9)
+        )
+
+        # dspy.Suggest is not available in dspy>=3.1.0
+        # dspy.Suggest(
+        #     abs(calc_cals - pred.nutritional_info.calories) < (pred.nutritional_info.calories * 0.20),
+        #     f"The sum of macros ({calc_cals:.1f}) should match the total calories "
+        #     f"({pred.nutritional_info.calories}). Check your math.",
+        # )
+        return pred
+
 
 nutrition_module = NutritionModule()
+
+
+def analyze_nutrition_from_image(image_bytes: bytes, description: str = "") -> NutritionalInfo:
+    if not settings.OPENAI_API_KEY:
+        raise ValueError("OpenAI API Key not set")
+
+    # Convert to base64 data URI
+    base64_image = base64.b64encode(image_bytes).decode("utf-8")
+    image_url = f"data:image/jpeg;base64,{base64_image}"
+
+    # Use DSPy module
+    result = nutrition_module.forward_image(image_url=image_url, description=description)
+    return result.nutritional_info