70 lines
3.1 KiB
Python
70 lines
3.1 KiB
Python
import json
|
|
from pydantic import BaseModel, Field
|
|
from APIs.aichat import generate
|
|
import shared.config as config
|
|
|
|
|
|
# Output schema as a Pydantic model
|
|
class DishSafetyResult(BaseModel):
|
|
visible_objects: list[str] = Field(
|
|
description="List all distinct physical objects visible in or around the dish (e.g., bowl, plastic tray, liquid, spoon, cover)."
|
|
)
|
|
material_analysis: str = Field(
|
|
description="Analyze the physical material of each visible object (e.g., ceramic, polypropylene plastic, stainless steel, aluminum foil)."
|
|
)
|
|
detected_hazards: list[str] = Field(
|
|
description=(
|
|
"List ONLY explicit, high-risk microwave hazards: metal items, cutlery, aluminum foil, "
|
|
"metallic trim/gilding, Styrofoam (expanded polystyrene), or tightly sealed/unvented foil lids. "
|
|
"Do NOT include standard plastic containers, TV dinner trays, polypropylene (#5), or Tupperware as hazards. Empty if none."
|
|
)
|
|
)
|
|
is_safe: bool = Field(
|
|
description="Must be set to True if detected_hazards is empty. Otherwise False."
|
|
)
|
|
warning_message: str = Field(
|
|
description="One short sentence explaining the hazard if detected_hazards is not empty, otherwise an empty string."
|
|
)
|
|
|
|
|
|
def check_dish_safety(image_path: str) -> dict:
|
|
prompt = (
|
|
"Examine this photo of a meal intended for microwave heating.\n\n"
|
|
"Inspect all objects to determine if any CRITICAL microwave hazards exist.\n\n"
|
|
"STRICT HAZARDS TO DETECT:\n"
|
|
"- Metal utensils, cutlery, or metal objects.\n"
|
|
"- Aluminum foil, metallic packaging, or decorative metallic trim.\n"
|
|
"- Expanded Polystyrene / Styrofoam containers.\n"
|
|
"- Completely sealed non-vented foil or plastic film lids (explosion risk).\n\n"
|
|
"SAFE MATERIALS (DO NOT FLAG AS HAZARDS):\n"
|
|
"- Standard microwavable food containers, plastic meal prep trays, black plastic TV dinner trays, and polypropylene (PP / #5) plastics.\n"
|
|
"- Glass, ceramic, or paper containers.\n\n"
|
|
"Only mark is_safe as False if a clear, high-risk hazard from the strictly dangerous list above is present."
|
|
)
|
|
|
|
# 2. Pass the Pydantic class directly to generate()
|
|
response_raw = generate(
|
|
prompt=prompt,
|
|
images=[image_path],
|
|
output_format=DishSafetyResult,
|
|
should_think=False,
|
|
)
|
|
print(f"[Debug] Raw response from AI generator: {response_raw}")
|
|
|
|
# 3. Handle response parsing
|
|
if isinstance(response_raw, str):
|
|
# Parse and validate the JSON string into the Pydantic model, then return as a dict
|
|
try:
|
|
validated_result = DishSafetyResult.model_validate_json(response_raw)
|
|
return validated_result.model_dump()
|
|
except Exception:
|
|
# Fallback to standard json.loads if raw parsing is needed
|
|
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)}") |