64 lines
2.6 KiB
Python
64 lines
2.6 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 UtensilMaterialCheck(BaseModel):
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object_name: str = Field(
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description="Name of utensil or container seen (e.g., spoon, fork, tray)."
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)
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is_metal: bool = Field(
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description="True ONLY if the object is made of metal, stainless steel, or aluminum."
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)
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class DishSafetyResult(BaseModel):
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utensils_and_containers: list[UtensilMaterialCheck] = Field(
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description="List each visible non-food object and check if it is made of metal."
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)
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material_analysis: str = Field(
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description="Short description of the materials present."
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)
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detected_hazards: list[str] = Field(
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description="List objects from utensils_and_containers where is_metal is True."
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)
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is_safe: bool = Field(
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description="True if detected_hazards is completely empty []. False if ANY metal is found."
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)
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warning_message: str = Field(
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description="If is_safe is False, set to 'REMOVE METAL UTENSIL OR FOIL BEFORE MICROWAVING'. Otherwise empty ''."
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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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"Analyze this microwave dish image strictly for EQUIPMENT DAMAGE HAZARDS (Metal, Steel, Aluminum Foil).\n\n"
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"GOAL:\n"
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"Identify if any metallic item (metal spoon, fork, knife, aluminum foil, wire) is inside the dish.\n\n"
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"RULES:\n"
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"1. Examine all utensils carefully, even in dim lighting or dark spots.\n"
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"2. Spoons, forks, and knives are often STAINLESS STEEL / METAL. If a spoon or fork is visible, mark is_metal = True unless it is clearly bright colored plastic.\n"
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"3. Ignore chemical or health concerns (plastic cancer risks are IRRELEVANT). Focus ONLY on spark/fire hazards (metal, foil).\n"
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"4. If ANY utensil is metal: set is_metal=True, add it to detected_hazards, and set is_safe=False."
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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)}") |