import json
from pydantic import BaseModel, Field
from APIs.aichat import generate
import shared.config as config

class DishSafetyResult(BaseModel):
    visible_objects: list[str] = Field(
        description="List EVERY distinct item visible in the frame (e.g., ['ceramic bowl', 'rice', 'metal spoon handle', 'stew'])."
    )
    spoon_or_utensil_present: bool = Field(
        description="Set to True if ANY metal, plastic, or wooden spoon, fork, knife, or utensil is present anywhere in or around the container."
    )
    is_safe: bool = Field(
        description="Set to False if spoon_or_utensil_present is True, or if aluminum foil or metal is present. Set to True ONLY if pure food/bowl with zero utensils."
    )
    detected_hazards: list[str] = Field(
        description="List ONLY physical hazard items found (e.g., ['metal spoon']). Empty list [] if safe."
    )
    warning_message: str = Field(
        description="Warning statement if unsafe, otherwise empty string ''."
    )

def check_dish_safety(image_path: str) -> dict:
    prompt = (
        "You are an expert microwave safety quality inspector analyzing a top-down camera frame.\n\n"
        "INSPECTION STEPS:\n"
        "1. Carefully examine the perimeter, edges, and inner cavity of the bowl/container.\n"
        "2. Identify any utensil handles (spoons, forks, knives) protruding from or resting in the dish, regardless of shadow or reflectivity.\n"
        "3. List all visible items in `visible_objects` first.\n"
        "4. If a utensil or metal object is present anywhere, `spoon_or_utensil_present` MUST be True and `is_safe` MUST be False.\n"
        "5. `is_safe` is True ONLY if the container holds strictly food with no utensils or metal."
    )

    response_raw = generate(
        prompt=prompt,
        images=[image_path],
        output_format=DishSafetyResult,
        should_think=False,
    )
    print(f"[Debug] Raw response from AI generator: {response_raw}")

    if isinstance(response_raw, str):
        try:
            validated_result = DishSafetyResult.model_validate_json(response_raw)
            return validated_result.model_dump()
        except Exception:
            return json.loads(response_raw)

    if isinstance(response_raw, DishSafetyResult):
        return response_raw.model_dump()

    if isinstance(response_raw, dict):
        return response_raw

    raise ValueError(f"Unexpected response type from AI generator: {type(response_raw)}")