remove custom inference handler

handler.py

@@ -1,77 +0,0 @@
-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',
-                     'energy traffic light',
-                     'boundaries',
-                     'Inner strength']  
-                       
-        # 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 preds