yahiu

.gitattributes

@@ -0,0 +1,32 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

example_input.json

@@ -1 +0,0 @@
-{"inputs": "https://media.lesechos.com/api/v1/images/view/5dadc9de8fe56f4ddf251469/1280x720/0602095339508-web-tete.jpg", "date": "2022-08-08"}

handler.py

@@ -1,29 +0,0 @@
-from typing import Dict, List, Any
-import holidays
-import random
-
-class EndpointHandler():
-    def __init__(self, path=""):
-        self.pipeline = lambda x: random.random()
-        self.holidays = holidays.US()
-
-    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)
-
-        # 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}]
-
-
-        # run normal prediction
-        prediction = self.pipeline(inputs)
-        return prediction

requirements.txt

@@ -1,3 +0,0 @@
-holidays
-torch
-torchvision