add custom pipeline

pipeline.py

@@ -0,0 +1,29 @@
+from typing import Dict, List, Any
+from transformers import pipeline
+import holidays
+
+class PreTrainedPipeline():
+    def __init__(self, path=""):
+        self.pipeline = pipeline("text-classification",model=path)
+        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 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

@@ -0,0 +1 @@
+holidays