New handler.

handler.py

@@ -1,28 +1,35 @@
 from typing import Dict, List, Any
 import faster_whisper
+import torch
 
-class EndpointHandler():
-    def __init__(self, path=""):
-        self.model = faster_whisper.WhisperModel('ivrit-ai/faster-whisper-v2-d3-e3', device='cuda')
+# Initialize the model and processor
+model_name_or_path = "ivrit-ai/faster-whisper-v2-d3-e3"
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
-        """
-       data args:
-            inputs (:obj: `str`)
-            date (:obj: `str`)
-      Return:
-            A :obj:`list` | `dict`: will be serialized and returned
-        """
-        # get inputs
-        #inputs = data.pop("inputs",data)
-        #date = data.pop("date", None)
+model = faster_whisper.WhisperModel(model_name_or_path, device=device)
 
-        # check if date exists and if it is a holiday
-        #if date is not None and date in self.holidays:
-        #  return [{"label": "happy", "score": 1}]
+def predict(audio_file_path):
+    return 'Bender is great!'
 
+def handler(event, context):
+    try:
+        # Parse input event
+        body = json.loads(event["body"])
+        audio_file_path = body["audio_file_path"]
+        
+        # Run prediction
+        transcription = predict(audio_file_path)
+        
+        # Create response
+        response = {
+            "statusCode": 200,
+            "body": json.dumps({"transcription": transcription})
+        }
+    except Exception as e:
+        response = {
+            "statusCode": 500,
+            "body": json.dumps({"error": str(e)})
+        }
 
-        # run normal prediction
-        #prediction = self.pipeline(inputs)
-        return {'text' : 'bender is great!'}
+    return response