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")