Create handler.py

handler.py

@@ -0,0 +1,38 @@
+import json
+import joblib
+import numpy as np
+from lightgbm import LGBMRegressor
+
+# Load the model once when the endpoint starts
+model = joblib.load("lgbm_model.joblib")  # <-- Your saved model
+
+def preprocess(inputs):
+    """
+    Convert JSON input into the appropriate format for LightGBM model
+    inputs: dict or list of dicts
+    """
+    # If input is a single dict, convert it to a list
+    if isinstance(inputs, dict):
+        inputs = [inputs]
+    
+    # Convert to numpy array (feature order must match training!)
+    feature_order = sorted(inputs[0].keys())
+    X = np.array([[sample[f] for f in feature_order] for sample in inputs])
+    return X
+
+def predict(inputs):
+    X = preprocess(inputs)
+    preds = model.predict(X)
+    # Return a list of predictions
+    return preds.tolist()
+
+def handle(inputs):
+    """
+    Function called by Hugging Face Inference Endpoint
+    """
+    # If input is a JSON string
+    if isinstance(inputs, str):
+        inputs = json.loads(inputs)
+    
+    preds = predict(inputs)
+    return {"predictions": preds}