Custom endpoint handler

custom handler to revert label names from endpoint

handler.py

@@ -0,0 +1,74 @@
+from typing import Dict, List, Any
+from setfit import SetFitModel
+import numpy as np
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # load model
+        self.model = SetFitModel.from_pretrained(path)
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+         data args:
+            inputs (:obj: `str` | `PIL.Image` | `np.array`)
+            kwargs
+        Return:
+            A :obj:`list` | `dict`: will be serialized and returned
+        """
+
+        # get inputs
+        inputs = data.pop("inputs", data)
+        if isinstance(inputs, str):
+            inputs = [inputs]
+
+
+        exerciselabels = ['positive experience',
+                     'power posing',
+                     'worry vs rumination',
+                     'self-confidence',
+                     'negative emotions',
+                     'sleep',
+                     'loneliness',
+                     'imaginary friend',
+                     'perfectionism',
+                     'negative self-talk',
+                     'woop',
+                     'venting',
+                     'worry window',
+                     'act of kindness',
+                     'blowing balloons',
+                     'feeling on anger',
+                     'power of smile',
+                     'body scan',
+                     'stress enhancing thoughts',
+                     'anger ball of fire',
+                     'emotions',
+                     'lean against wall',
+                     'breathing',
+                     'crossed arms']  
+                       
+        # run normal prediction
+        preds = self.model.predict(inputs)
+        scores = self.model.predict_proba(inputs)
+        
+        label = [[el for el, p in zip(exerciselabels, ps) if p] for ps in preds]
+
+        # Modify the label array
+        modified_label = label[0]
+        
+        # Extract the positives probabilities  from each inner array
+        modified_proba = [[inner[0][1]] for item, inner in zip(scores, scores)]
+        
+        
+        score = [[el for el, p in zip(modified_proba, ps) if p] for ps in preds]
+        
+        # Modify the score array
+        modified_score = score[0]
+        
+        
+        combined_dict = {key: value for key, value in zip(modified_label, modified_score)}
+
+        output_array = [combined_dict]
+
+        # for element in combined_array:
+        return output_array