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/scripts/optimize_nutrition.py

@@ -0,0 +1,41 @@
+from dspy.teleprompt import BootstrapFewShot
+
+from app.ai.nutrition import nutrition_module
+from app.core.ai_config import configure_dspy
+from scripts.nutrition_data import train_examples
+
+# 0. Configure DSPy
+configure_dspy()
+
+# 1. Define Validated Examples (The "Train Set")
+# ... (rest of the file) ...
+
+
+# 2. Define a Metric
+def validate_nutrition(example, pred, trace=None):
+    # Check if the predicted calories are within 15% of the actual calories
+    actual_cals = example.nutritional_info.calories
+    pred_cals = pred.nutritional_info.calories
+
+    threshold = 0.15
+    lower = actual_cals * (1 - threshold)
+    upper = actual_cals * (1 + threshold)
+
+    return lower <= pred_cals <= upper
+
+
+# 3. Setup the Optimizer
+teleprompter = BootstrapFewShot(metric=validate_nutrition, max_bootstrapped_demos=8, max_labeled_demos=8)
+
+# 4. Compile (Optimize) the Module
+print("Optimizing... (this calls the LLM for each example)")
+compiled_nutrition = teleprompter.compile(nutrition_module, trainset=train_examples)
+
+# 5. Save validity
+# Correct path relative to backend/ directory
+compiled_nutrition.save("app/ai/nutrition_compiled.json")
+print("Optimization complete! Saved to app/ai/nutrition_compiled.json")
+
+# 6. Usage
+# To use the optimized version in production, you would load it:
+# nutrition_module.load("backend/app/ai/nutrition_compiled.json")