Create handler.py
Browse files- handler.py +27 -0
handler.py
ADDED
|
@@ -0,0 +1,27 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from typing import Dict, List, Any
|
| 2 |
+
from setfit import SetFitModel
|
| 3 |
+
|
| 4 |
+
|
| 5 |
+
class EndpointHandler:
|
| 6 |
+
def __init__(self, path=""):
|
| 7 |
+
# load model
|
| 8 |
+
self.model = SetFitModel.from_pretrained(path)
|
| 9 |
+
# ag_news id to label mapping
|
| 10 |
+
self.id2label = {0: "World", 1: "Sports", 2: "Business", 3: "Sci/Tech"}
|
| 11 |
+
|
| 12 |
+
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
|
| 13 |
+
"""
|
| 14 |
+
data args:
|
| 15 |
+
inputs (:obj: `str`)
|
| 16 |
+
Return:
|
| 17 |
+
A :obj:`list` | `dict`: will be serialized and returned
|
| 18 |
+
"""
|
| 19 |
+
# get inputs
|
| 20 |
+
inputs = data.pop("inputs", data)
|
| 21 |
+
if isinstance(inputs, str):
|
| 22 |
+
inputs = [inputs]
|
| 23 |
+
|
| 24 |
+
# run normal prediction
|
| 25 |
+
scores = self.model.predict_proba(inputs)[0]
|
| 26 |
+
|
| 27 |
+
return [{"label": self.id2label[i], "score": score.item()} for i, score in enumerate(scores)]
|