Update model_handler.py

model_handler.py

@@ -1,6 +1,50 @@
+# 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
+os.environ['TRANSFORMERS_CACHE'] = '/tmp/transformers_cache'
+os.environ['HF_HOME'] = '/tmp/hf_home'
+
 class SimilarityModelHandler:
     # HOLDING THE MODEL INSTANCE TO PREVENT RELOADING
     SIMILARITY_MODEL_INSTANCE = None
@@ -14,9 +58,12 @@ class SimilarityModelHandler:
             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)
+            model_Name = 'cross-encoder/stsb-roberta-large'
+            SimilarityModelHandler.SIMILARITY_MODEL_INSTANCE = CrossEncoder(
+                model_Name, 
+                device=device,
+                cache_folder='/tmp/transformers_cache'  # EXPLICIT CACHE FOLDER
+            )
             print("MODEL LOADED SUCCESSFULLY.")
 
     def calculate_Similarity(self, text_One: str, text_Two: str) -> float: