llogs

pretrained.py

@@ -6,16 +6,16 @@ import os
 import json
 from flask import Flask, request, jsonify
 from werkzeug.utils import secure_filename
-import logging
+# import logging
 
 
-# Set up root logger, and add a file handler to root logger
-logging.basicConfig(filename = 'log_file.log',
-                    filemode='w',
-                    level = logging.DEBUG,
-                    format = '%(asctime)s:%(levelname)s:%(filename)s:%(funcName)s:%(lineno)d:%(message)s')
+# # Set up root logger, and add a file handler to root logger
+# logging.basicConfig(filename = 'log_file.log',
+#                     filemode='w',
+#                     level = logging.DEBUG,
+#                     format = '%(asctime)s:%(levelname)s:%(filename)s:%(funcName)s:%(lineno)d:%(message)s')
 
-logger = logging.getLogger()
+# logger = logging.getLogger()
 
 
 app = Flask(__name__)
@@ -27,40 +27,40 @@ def similarity():
 
     try:
 
-        logger.debug(f'receiving the json data')
+        # logger.debug(f'receiving the json data')
         data = request.get_json()
-        logger.debug(f'received the json data')
+        # logger.debug(f'received the json data')
 
         if 'text1' not in data or 'text2' not in data:
-            logger.debug(f'Error : Both text1 and text2 must be provided!')
+            # logger.debug(f'Error : Both text1 and text2 must be provided!')
             return jsonify({'error': 'Both text1 and text2 must be provided.'}), 400
 
-        logger.debug(f'extracting the sentences from the request')
+        # logger.debug(f'extracting the sentences from the request')
         sentences1 = data['text1']
         sentences2 = data['text2']
-        logger.debug(f'extracted the sentences from the request')
+        # logger.debug(f'extracted the sentences from the request')
 
-        logger.debug(f'calculating the embeddings')
+        # logger.debug(f'calculating the embeddings')
         embeddings1 = model.encode(sentences1, convert_to_tensor=True)
         embeddings2 = model.encode(sentences2, convert_to_tensor=True)
-        logger.debug(f'embeddings calculated')
+        # logger.debug(f'embeddings calculated')
 
-        logger.debug(f'calculating the cosine score')
+        # logger.debug(f'calculating the cosine score')
         cosine_scores = util.cos_sim(embeddings1, embeddings2)
-        logger.debug(f'calculated the cosine score')
+        # logger.debug(f'calculated the cosine score')
 
         print(f'{cosine_scores[0][0].item()}')
         return jsonify({'similarity_score': cosine_scores[0][0].item()}), 200
 
     except Exception as e:
-        logger.debug(f'Unknown error! : {e}')
+        # logger.debug(f'Unknown error! : {e}')
         return jsonify({'error' : str(e)}), 500
 
 
 
 if __name__ == '__main__':
 
-    logger.debug(f'loading model...')
+    # logger.debug(f'loading model...')
     print(f'loading model...')
 
     # model = SentenceTransformer("all-MiniLM-L6-v2", cache_folder='./')