fixes

backend/services.py

@@ -202,16 +202,16 @@ class SentimentAnalyzer:
         }
 
         self.pipelines = {
-            "sa_trial5_1": [pipeline("sentiment-analysis", model="{}/train_{}/best_model".format("sa_trial5_1",i), device=-1,return_all_scores =True) for i in range(0,5)],
-            "sa_no_aoa_in_neutral": [pipeline("sentiment-analysis", model="{}/train_{}/best_model".format("sa_no_aoa_in_neutral",i), device=-1,return_all_scores =True) for i in range(0,5)],
-            "sa_cnnbert": [CNNTextClassificationPipeline("{}/train_{}/best_model".format("sa_cnnbert",i), device=-1, return_all_scores =True) for i in range(0,5)],
-            "sa_sarcasm": [pipeline("sentiment-analysis", model="{}/train_{}/best_model".format("sa_sarcasm",i), device=-1,return_all_scores =True) for i in range(0,5)],
-            "sar_trial10": [pipeline("sentiment-analysis", model="{}/train_{}/best_model".format("sar_trial10",i), device=-1,return_all_scores =True) for i in range(0,5)],
-            "sa_no_AOA": [pipeline("sentiment-analysis", model="{}/train_{}/best_model".format("sa_no_AOA",i), device=-1,return_all_scores =True) for i in range(0,5)],
+            "sa_trial5_1": [pipeline("sentiment-analysis", model="{}/train_{}/best_model".format("sa_trial5_1",i), device=-1,return_all_scores =True) for i in tqdm(range(0,5), desc=f"Loading pipeline for model: sa_trial5_1}"],
+            "sa_no_aoa_in_neutral": [pipeline("sentiment-analysis", model="{}/train_{}/best_model".format("sa_no_aoa_in_neutral",i), device=-1,return_all_scores =True) for i in tqdm(range(0,5), desc=f"Loading pipeline for model: sa_no_aoa_in_neutral}"],
+            "sa_cnnbert": [CNNTextClassificationPipeline("{}/train_{}/best_model".format("sa_cnnbert",i), device=-1, return_all_scores =True) for i in tqdm(range(0,5), desc=f"Loading pipeline for model: sa_cnnbert}"],
+            "sa_sarcasm": [pipeline("sentiment-analysis", model="{}/train_{}/best_model".format("sa_sarcasm",i), device=-1,return_all_scores =True) for i in tqdm(range(0,5), desc=f"Loading pipeline for model: sa_sarcasm}"],
+            "sar_trial10": [pipeline("sentiment-analysis", model="{}/train_{}/best_model".format("sar_trial10",i), device=-1,return_all_scores =True) for i in tqdm(range(0,5), desc=f"Loading pipeline for model: sar_trial10}"],
+            "sa_no_AOA": [pipeline("sentiment-analysis", model="{}/train_{}/best_model".format("sa_no_AOA",i), device=-1,return_all_scores =True) for i in tqdm(range(0,5), desc=f"Loading pipeline for model: sa_no_AOA}"],
         }
         # fmt: on
 
-    def get_sarcasm_label(self, texts):
+    def get_preds_from_sarcasm(self, texts):
         prep = self.processors["sar_trial10"]
         prep_texts = [prep.preprocess(x) for x in texts]
 

backend/utils.py

@@ -1,6 +1,9 @@
 import psutil
 import os
 from tqdm.auto import tqdm
+import logging
+
+logger = logging.getLogger(__name__)
 
 
 def get_current_ram_usage():
@@ -10,6 +13,7 @@ def get_current_ram_usage():
 
 def download_models(models):
     for model in tqdm(models, desc="Downloading models"):
+        logger.info(f"Downloading {model}")
         for i in range(0, 5):
             curr_dir = f"{model}/train_{i}/best_model/"
             os.makedirs(curr_dir)