Adding logs for model loading

src/service/service.py

@@ -36,7 +36,7 @@ try:
     if getattr(sys, 'frozen', False):
         application_path = sys._MEIPASS
     else:
-        application_path = os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..'))
+        application_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..')
 
         
     log=CustomLogger()
@@ -59,34 +59,35 @@ try:
     # print(os.listdir())
     check_point = 'toxic_debiased-c7548aa0.ckpt'
     log.info(f"device is: {device}")
-
-    #remove later
-    value = "/Responsible-Ai-Moderation-Models/", 'models/detoxify/'+ check_point
-    log.info(f"test log: {value}")
-    
     log.info(f"checkpoint Path is: {os.path.join(application_path, 'models/detoxify/'+ check_point)}")
-
-    log.info(f"huggingface_config_path Path is: {os.path.join(application_path, 'models/detoxify/'+ check_point)}")    
-    toxicityModel = Detoxify(checkpoint=os.path.join("/Responsible-Ai-Moderation-Models/", 'models/detoxify/'+ check_point),
+    log.info(f"huggingface_config_path Path is: {os.path.join(application_path, 'models/detoxify/'+ check_point)}") 
+    log.info("Loading toxicity model")
+    toxicityModel = Detoxify(checkpoint=os.path.join(application_path, 'models/detoxify/'+ check_point),
                         device=device,
-                        huggingface_config_path=os.path.join("/Responsible-Ai-Moderation-Models/", 'models/detoxify'))
-    tokenizer = AutoTokenizer.from_pretrained(os.path.join("/Responsible-Ai-Moderation-Models/", "models/detoxify"))
-
+                        huggingface_config_path=os.path.join(application_path, 'models/detoxify'))
+    tokenizer = AutoTokenizer.from_pretrained(os.path.join(application_path, "models/detoxify"))
+    log.info("Toxicity model loaded")
 
+    log.info("Loading prompt injection model")
     PromptModel_dberta = AutoModelForSequenceClassification.from_pretrained(os.path.join(application_path, "models/dbertaInjection")).to(device)
     Prompttokens_dberta = AutoTokenizer.from_pretrained(os.path.join(application_path, "models/dbertaInjection"))
     promtModel = pipeline("text-classification", model=PromptModel_dberta, tokenizer=Prompttokens_dberta, device=device)
+    log.info("Loaded prompt injection model")
 
     #topictokenizer_Facebook = AutoTokenizer.from_pretrained("../models/facebook")
     #topicmodel_Facebook = AutoModelForSequenceClassification.from_pretrained("../models/facebook").to(device)
 
+    log.info("Loading restricted topic model")
     topictokenizer_dberta = AutoTokenizer.from_pretrained(os.path.join(application_path,"models/restricted-dberta-base-zeroshot-v2"))
     topicmodel_dberta = AutoModelForSequenceClassification.from_pretrained(os.path.join(application_path,"models/restricted-dberta-base-zeroshot-v2")).to(device)
     nlp = pipeline('zero-shot-classification', model=topicmodel_dberta, tokenizer=topictokenizer_dberta,device=gpu)
+    log.info("Loaded restricted topic model")
     # classifier = pipeline("zero-shot-classification",model="../../models/facebook",device=device)
     # classifier2 = pipeline("zero-shot-classification",model="../../models/restricted-dberta-large-zeroshot",device=device)
-    encoder = SentenceTransformer(os.path.join(application_path, "models/multi-qa-mpnet-base-dot-v1")).to(device)
 
+    log.info("Loading similarity model")
+    encoder = SentenceTransformer(os.path.join(application_path, "models/multi-qa-mpnet-base-dot-v1")).to(device)
+    log.info("Loaded similarity model")
     jailbreakModel = encoder
     similarity_model =encoder
     request_id_var.set("Startup")