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"""
VLM Food Classifier — Simulates Vision-Language Model food classification.
In production, this would call LLaVA / GPT-4V on a rendered PyBullet frame.
For the hackathon, uses pre-computed attributes from food_db.json.
The agent MUST call 'identify' before it knows a food item's properties.
Without identification, items appear as generic "Unknown food item".
"""
import json
import os
from typing import Any, Dict, Optional
class FoodClassifier:
"""Cached VLM food classifier.
Loads pre-computed food attributes from food_db.json.
In a production system, replace ``classify()`` with a real
VLM API call (e.g. LLaVA, GPT-4V) on a rendered scene frame.
"""
def __init__(self, db_path: Optional[str] = None):
if db_path is None:
db_path = os.path.join(os.path.dirname(__file__), "food_db.json")
with open(db_path, "r") as f:
self.food_db: Dict[str, Dict[str, Any]] = json.load(f)
def classify(self, food_name: str) -> Dict[str, Any]:
"""Classify a food item and return its attributes.
Args:
food_name: Name of the food item (e.g. "sambar", "rice").
Returns:
Dict with keys: type, fragility, preferred_container,
volume_ml, temperature, color, special_notes.
"""
key = food_name.lower().strip()
if key in self.food_db:
return {**self.food_db[key], "name": key, "classified": True}
return self._unknown_default(food_name)
def _unknown_default(self, food_name: str) -> Dict[str, Any]:
"""Fallback for foods not in the database."""
return {
"name": food_name,
"type": "solid",
"fragility": 0.5,
"preferred_container": "deep",
"volume_ml": 100,
"temperature": "room",
"color": "unknown",
"special_notes": "Unknown food item — classification uncertain",
"classified": False,
}
def get_all_foods(self) -> list:
"""Return list of all known food names."""
return list(self.food_db.keys())
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