Refactor handler.py to use relative path for model initialization

handler.py

@@ -5,10 +5,10 @@ from FlagEmbedding import BGEM3FlagModel
 
 
 class EndpointHandler():
-    def __init__(self, path="./"):
+    def __init__(self, path="."):
         # load the optimized model
         # モデルの準備
-        self.model = BGEM3FlagModel("./", use_fp16=False)
+        self.model = BGEM3FlagModel(path, use_fp16=False)
 
 
     def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
@@ -24,14 +24,9 @@ class EndpointHandler():
         inputs = data.pop("inputs", data)
         parameters = data.pop("parameters", None)
         
-        sparse_embs = []
-        
-        # result = self.model.encode(inputs, return_dense=False, return_sparse=True)
-        # sparse_vectors = result["lexical_weights"]
-        # for sparse_vector in sparse_vectors:
-        #     sparse_values = [value for value in sparse_vector.values()]
-        #     sparse_dimensions = [int(key) for key in sparse_vector.keys()]
-        #     sparse_embs.append((sparse_values, sparse_dimensions))
+        result = self.model.encode(inputs, return_dense=False, return_sparse=True)
+        sparse_vectors = result["lexical_weights"]
+        # defaultdict(<class 'int'>, {'6': 0.09558245, '192661': 0.33248675})}
 
         # pass inputs with all kwargs in data
         # if parameters is not None:
@@ -41,6 +36,6 @@ class EndpointHandler():
         # postprocess the prediction
         return [
             [
-                {"label": 0, "score": 0.5}
+                { "outputs": sparse_vectors}
             ]
         ]