Create pipeline.py

pipeline.py

@@ -0,0 +1,33 @@
+import json
+from typing import Any, Dict, List
+
+import sklearn
+import os
+import joblib
+import numpy as np
+
+
+
+class PreTrainedPipeline():
+    def __init__(self, path: str):
+        # load the model
+        self.model = joblib.load((os.path.join(path, "pipeline.pkl"))
+
+    def __call__(self, inputs: str) -> List[Dict[str, float]]:
+        """
+        Args:
+            inputs (:obj:`str`):
+                a string containing some text
+        Return:
+            A :obj:`list`:. The object returned should be a list of one list like [[{"label": 0.9939950108528137}]] containing:
+                - "label": A string representing what the label/class is. There can be multiple labels.
+                - "score": A score between 0 and 1 describing how confident the model is for this label/class.
+        """
+        predictions = self.model.predict_proba([inputs])
+        labels = []
+        for cls in predictions[0]:
+          labels.append({
+                  "label": f"LABEL_{cls}",
+                  "score": predictions[0][cls],
+              })
+        return labels