Upload weight_predictor.py with huggingface_hub

weight_predictor.py

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+"""
+Weight predictor classes for food delivery platforms.
+"""
+import numpy as np
+
+
+class WeightPredictor:
+    """Per-item-type weight predictor using TF-IDF + Ridge regression."""
+    def __init__(self, tfidf, model):
+        self.tfidf = tfidf
+        self.model = model
+
+    def predict(self, texts):
+        """Predict weights for a list of texts."""
+        X = self.tfidf.transform(texts)
+        return np.expm1(self.model.predict(X))
+
+    def predict_single(self, text):
+        """Predict weight for a single text."""
+        return self.predict([text])[0]
+
+
+class UnifiedWeightPredictor:
+    """Unified predictor that routes to per-type models."""
+    def __init__(self, predictors, default_type="grocery"):
+        self.predictors = predictors
+        self.default_type = default_type
+
+    def predict(self, text, item_type=None):
+        """
+        Predict weight from text description.
+        item_type should be one of: menu_item, grocery, non_food
+        """
+        if item_type is None:
+            if text.startswith("[MENU_ITEM]"):
+                item_type = "menu_item"
+            elif text.startswith("[GROCERY]"):
+                item_type = "grocery"
+            elif text.startswith("[NON_FOOD]"):
+                item_type = "non_food"
+            else:
+                item_type = self.default_type
+        predictor = self.predictors.get(item_type, self.predictors.get(self.default_type))
+        return predictor.predict([text])[0]