48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
import json
|
|
from APIs.aichat import generate
|
|
|
|
DISH_SAFETY_SCHEMA = {
|
|
"type": "object",
|
|
"properties": {
|
|
"is_safe": {
|
|
"type": "boolean",
|
|
"description": "True if no microwave hazards (metal, foil, sealed packaging) are present."
|
|
},
|
|
"warning_message": {
|
|
"type": "string",
|
|
"description": "Explanation of any hazard found, or an empty string if safe."
|
|
},
|
|
"detected_hazards": {
|
|
"type": "array",
|
|
"items": {
|
|
"type": "string"
|
|
},
|
|
"description": "List of specific hazard items detected."
|
|
}
|
|
},
|
|
"required": ["is_safe", "warning_message", "detected_hazards"]
|
|
}
|
|
|
|
def check_dish_safety(image_path: str) -> dict:
|
|
prompt = (
|
|
"Analyze this top-down photo of a dish prepared for microwave cooking. "
|
|
"Inspect the area for metal utensils, aluminum foil, metallic dish patterns, or unvented plastic wraps."
|
|
)
|
|
|
|
# Pass the schema directly to the generation call
|
|
response_raw = generate(
|
|
prompt=prompt,
|
|
images=[image_path],
|
|
output_format=DISH_SAFETY_SCHEMA # Passed as Ollama's `format` parameter
|
|
)
|
|
print(f"[Debug] Raw response from AI generator: {response_raw}")
|
|
|
|
# 1. If response_raw is already a dict, return it directly
|
|
if isinstance(response_raw, dict):
|
|
return response_raw
|
|
|
|
# 2. If it's a string or bytes, parse it
|
|
if isinstance(response_raw, (str, bytes, bytearray)):
|
|
return json.loads(response_raw)
|
|
|
|
raise ValueError(f"Unexpected response type from AI generator: {type(response_raw)}") |