Update model_handler.py

model_handler.py

@@ -1,47 +1,8 @@
-# from sentence_transformers.cross_encoder import CrossEncoder
-# import torch
-
-# class SimilarityModelHandler:
-#     # HOLDING THE MODEL INSTANCE TO PREVENT RELOADING
-#     SIMILARITY_MODEL_INSTANCE = None
-
-#     def __init__(self):
-#         # CONSTRUCTOR: LOADING THE MODEL IF IT DOESN'T EXIST
-#         if not SimilarityModelHandler.SIMILARITY_MODEL_INSTANCE:
-#             print("INITIALIZING AND LOADING THE MODEL...")
-#             # CHECKING FOR GPU, FALLBACK TO CPU
-#             device = 'cuda' if torch.cuda.is_available() else 'cpu'
-#             print(f"SERVICE IS RUNNING ON DEVICE: {device}")
-            
-#             # LOADING THE PRE-TRAINED CROSS-ENCODER MODEL
-#             model_Name = 'cross-encoder/stsb-roberta-base'
-#             #cross-encoder/stsb-roberta-large'
-#             SimilarityModelHandler.SIMILARITY_MODEL_INSTANCE = CrossEncoder(model_Name, device=device)
-#             print("MODEL LOADED SUCCESSFULLY.")
-
-#     def calculate_Similarity(self, text_One: str, text_Two: str) -> float:
-#         """
-#         CALCULATES THE SIMILARITY SCORE BETWEEN TWO TEXTS.
-#         """
-#         # GETTING THE SCORE FROM THE MODEL( 0-1 )
-#         finalScore = self.SIMILARITY_MODEL_INSTANCE.predict([(text_One, text_Two)])
-
-#         # CONVERTING FROM NUMPY ARRAY TO A SIMPLE FLOAT
-#         return finalScore.item()
-
-
-# # CREATING A SINGLE INSTANCE TO BE USED BY THE API
-# MODEL_HANDLER = SimilarityModelHandler()
-
-
-
-
-
 import os
 from sentence_transformers.cross_encoder import CrossEncoder
 import torch
 
-# SET CACHE DIRECTORY TO A WRITABLE LOCATION
+# SETTING CACHE DIRECTORY TO A WRITABLE LOCATION
 os.environ['TRANSFORMERS_CACHE'] = '/tmp/transformers_cache'
 os.environ['HF_HOME'] = '/tmp/hf_home'