| """ |
| Usage example for the hybrid weight predictor. |
| |
| Requirements: |
| pip install huggingface_hub joblib scikit-learn |
| |
| This uses a hybrid approach: |
| 1. Explicit weight extraction from text (e.g., "500g", "2 liter") |
| 2. Knowledge base lookup for known FMCG brands and fast food items |
| 3. ML model fallback for unknown items |
| """ |
| from huggingface_hub import hf_hub_download |
| import joblib |
| import sys |
| import os |
|
|
| |
| sys.path.insert(0, os.path.dirname(__file__)) |
|
|
| |
| from hybrid_weight_predictor import HybridWeightPredictor, build_hybrid_predictor |
|
|
| def predict_weight(text, item_type=None): |
| """ |
| Predict item weight in grams. |
| |
| Args: |
| text: Item description. Should start with [MENU_ITEM], [GROCERY], or [NON_FOOD] |
| item_type: "menu_item", "grocery", or "non_food" (auto-detected from text if None) |
| |
| Returns: |
| Predicted weight in grams (float) |
| """ |
| predictor = build_hybrid_predictor() |
| return predictor.predict(text, item_type) |
|
|
|
|
| if __name__ == "__main__": |
| |
| examples = [ |
| ("[GROCERY] coca cola can 330ml", "grocery"), |
| ("[GROCERY] coca cola", "grocery"), |
| ("[GROCERY] pepsi 1 liter bottle", "grocery"), |
| ("[GROCERY] kelloggs corn flakes 500g", "grocery"), |
| ("[GROCERY] oreo cookies 154g", "grocery"), |
| ("[GROCERY] heinz ketchup 570ml", "grocery"), |
| ("[GROCERY] mars bar 51g", "grocery"), |
| ("[GROCERY] snickers 2 pack 96g", "grocery"), |
| ("[GROCERY] red bull 4 pack", "grocery"), |
| ("[GROCERY] tide laundry detergent 1.5kg", "grocery"), |
| ("[MENU_ITEM] large pizza", "menu_item"), |
| ("[MENU_ITEM] cheeseburger", "menu_item"), |
| ("[MENU_ITEM] double cheeseburger", "menu_item"), |
| ("[MENU_ITEM] big mac", "menu_item"), |
| ("[MENU_ITEM] french fries", "menu_item"), |
| ("[MENU_ITEM] large fries", "menu_item"), |
| ("[MENU_ITEM] chicken nuggets", "menu_item"), |
| ("[MENU_ITEM] burrito", "menu_item"), |
| ("[MENU_ITEM] caesar salad", "menu_item"), |
| ("[MENU_ITEM] caesar salad large", "menu_item"), |
| ("[MENU_ITEM] pho", "menu_item"), |
| ("[MENU_ITEM] ramen", "menu_item"), |
| ("[MENU_ITEM] sushi platter", "menu_item"), |
| ("[MENU_ITEM] medium pizza", "menu_item"), |
| ("[MENU_ITEM] combo meal", "menu_item"), |
| ("[MENU_ITEM] milkshake", "menu_item"), |
| ("[MENU_ITEM] iced coffee", "menu_item"), |
| ("[MENU_ITEM] family meal", "menu_item"), |
| ("[MENU_ITEM] sliders", "menu_item"), |
| ("[NON_FOOD] laptop computer", "non_food"), |
| ("[NON_FOOD] water bottle", "non_food"), |
| ] |
|
|
| print("=== Weight Predictions ===\n") |
| for text, item_type in examples: |
| weight = predict_weight(text, item_type) |
| print(f" {text:55s} -> {weight:8.1f}g") |
|
|
