Fixed handler names

handler.py

@@ -1,15 +1,23 @@
 import torch
 import importlib.util
 import sys
-import pathlib
+import os
+from typing import Dict, List, Any
 from transformers import AutoModel, AutoTokenizer
 
-class InferenceHandler:
-    def __init__(self):
+class EndpointHandler():
+    def __init__(self, path=""):
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         
         # Import custom model definition from local file
-        model_path = "cross_scorer_model.py"
+        # The file is expected to be in the same directory as handler.py, which 'path' points to
+        model_filename = "cross_scorer_model.py"
+        model_path = os.path.join(path, model_filename) 
+        
+        # Fallback if path is empty or "." and file is in CWD
+        if not os.path.exists(model_path):
+             model_path = model_filename
+
         spec = importlib.util.spec_from_file_location("cross_scorer_model", model_path)
         mod = importlib.util.module_from_spec(spec)
         sys.modules["cross_scorer_model"] = mod
@@ -20,7 +28,12 @@ class InferenceHandler:
         self.model = mod.CrossScorerCrossEncoder(encoder).to(self.device)
         
         # Load weights
-        weights_path = "reflection_scorer_weight.pt"
+        weights_filename = "reflection_scorer_weight.pt"
+        weights_path = os.path.join(path, weights_filename)
+        
+        if not os.path.exists(weights_path):
+            weights_path = weights_filename
+
         state = torch.load(weights_path, map_location=self.device)
         sd = state.get("model_state_dict", state)
         self.model.load_state_dict(sd, strict=False)
@@ -30,14 +43,24 @@ class InferenceHandler:
         # Initialize tokenizer
         self.tokenizer = AutoTokenizer.from_pretrained("roberta-base")
 
-    def handle(self, inputs: list) -> list:
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+        data args:
+            inputs (:obj: `list` | `dict`): The inputs to the model.
+        """
+        # get inputs
+        inputs = data.pop("inputs", data)
+        
+        # If inputs is a dict (single item), wrap in list to reuse logic, or handle list
+        if isinstance(inputs, dict):
+            inputs = [inputs]
+        
         results = []
         for item in inputs:
             prompt = item.get("prompt")
             response = item.get("response")
             
             if not prompt or not response:
-                # Handle missing keys gracefully, though instructions imply strict format
                 results.append({"error": "Missing prompt or response"})
                 continue