55 lines
2.4 KiB
Python
55 lines
2.4 KiB
Python
import json
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from pydantic import BaseModel, Field
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from APIs.aichat import generate
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import shared.config as config
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class DishSafetyResult(BaseModel):
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visible_objects: list[str] = Field(
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description="List EVERY distinct item visible in the frame (e.g., ['ceramic bowl', 'rice', 'metal spoon handle', 'stew'])."
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)
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spoon_or_utensil_present: bool = Field(
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description="Set to True if ANY metal, plastic, or wooden spoon, fork, knife, or utensil is present anywhere in or around the container."
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)
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is_safe: bool = Field(
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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."
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)
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detected_hazards: list[str] = Field(
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description="List ONLY physical hazard items found (e.g., ['metal spoon']). Empty list [] if safe."
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)
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warning_message: str = Field(
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description="Warning statement if unsafe, otherwise empty string ''."
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)
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def check_dish_safety(image_path: str) -> dict:
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prompt = (
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"You are an expert microwave safety quality inspector analyzing a top-down camera frame.\n\n"
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"INSPECTION STEPS:\n"
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"1. Carefully examine the perimeter, edges, and inner cavity of the bowl/container.\n"
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"2. Identify any utensil handles (spoons, forks, knives) protruding from or resting in the dish, regardless of shadow or reflectivity.\n"
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"3. List all visible items in `visible_objects` first.\n"
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"4. If a utensil or metal object is present anywhere, `spoon_or_utensil_present` MUST be True and `is_safe` MUST be False.\n"
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"5. `is_safe` is True ONLY if the container holds strictly food with no utensils or metal."
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)
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response_raw = generate(
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prompt=prompt,
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images=[image_path],
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output_format=DishSafetyResult,
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should_think=False,
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)
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print(f"[Debug] Raw response from AI generator: {response_raw}")
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if isinstance(response_raw, str):
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try:
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validated_result = DishSafetyResult.model_validate_json(response_raw)
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return validated_result.model_dump()
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except Exception:
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return json.loads(response_raw)
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if isinstance(response_raw, DishSafetyResult):
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return response_raw.model_dump()
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if isinstance(response_raw, dict):
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return response_raw
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raise ValueError(f"Unexpected response type from AI generator: {type(response_raw)}") |