Upload folder using huggingface_hub

FeatureEngineering.py

@@ -1,11 +1,9 @@
 
+# FeatureEngineering.py
 from sklearn.base import BaseEstimator, TransformerMixin
 import numpy as np
-from catboost import CatBoostRegressor
 import pandas as pd
-# ---------------------------
-# Custom Feature Engineering Transformer
-# ---------------------------
+
 class FeatureEngineering(BaseEstimator, TransformerMixin):
     def __init__(self):
         pass
@@ -20,6 +18,9 @@ class FeatureEngineering(BaseEstimator, TransformerMixin):
         X_['Year'] = X_['Date'].dt.year
         X_['Month'] = X_['Date'].dt.month
         X_['Weekday'] = X_['Date'].dt.weekday
-        X_['IsSpecialEvent'] = X_['SpecialEvent'].apply(lambda x: 0 if x=="" else 1)
         X_['SlotHour'] = X_['Slot'].str.split(":").str[0].astype(int)
+
+        # Keep categorical SpecialEvent as-is, and also add an indicator if needed
+        X_['IsSpecialEvent'] = X_['SpecialEvent'].apply(lambda x: 0 if pd.isna(x) or x == "" else 1)
+
         return X_

app.py

@@ -91,8 +91,11 @@ def predict_capacity_batch():
         logger.info("Batch predictions: %s", clean_predictions)
 
         output_df = pd.DataFrame({
+            "BU": input_data["BU"],
             "Date": input_data["Date"],
             "Store": input_data["Store"],
+            "LocationType": input_data["LocationType"],
+            "Slot": input_data["Slot"],
             "Predicted_Capacity": clean_predictions
         })
 

catbooster_model_v1_0.joblib

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d20a6e0fe08874508db62951b496151b06da2b57b78a1b5166e2a2090e067f71
-size 1673030
+oid sha256:f32fa4df0f600bd98ca97a9bb80e8c126ec56365d151004af44ee6fb20424ade
+size 1119827