Update model_handler.py

model_handler.py

@@ -1,33 +1,34 @@
-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-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
+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()