code refactoring

app.py

@@ -1,49 +1,43 @@
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-# import torch
-# device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
-# device
-
-
-
 download = False
 save_model_locally= False
 if download:
     tokenizer = AutoTokenizer.from_pretrained("MilaNLProc/feel-it-italian-sentiment", cache_dir="data/")
-    model = AutoModelForSequenceClassification.from_pretrained("MilaNLProc/feel-it-italian-sentiment", cache_dir="data/")
-    model.eval()
+    model_sent = AutoModelForSequenceClassification.from_pretrained("MilaNLProc/feel-it-italian-sentiment", cache_dir="data/")
+    model_sent.eval()
     tokenizer_emo = AutoTokenizer.from_pretrained("MilaNLProc/feel-it-italian-emotion", cache_dir="data/")
     model_emo = AutoModelForSequenceClassification.from_pretrained("MilaNLProc/feel-it-italian-emotion", cache_dir="data/")
     model_emo.eval()
     if save_model_locally:
-        model.save_pretrained('./local_models/sentiment_ITA')
+        model_sent.save_pretrained('./local_models/sentiment_ITA')
         tokenizer.save_pretrained('./local_models/sentiment_ITA')
         model_emo.save_pretrained('./local_models/emotion_ITA')
         tokenizer_emo.save_pretrained('./local_models/emotion_ITA')
 else:
     tokenizer = AutoTokenizer.from_pretrained("./local_models/sentiment_ITA/")
-    model = AutoModelForSequenceClassification.from_pretrained("./local_models/sentiment_ITA/", num_labels=2)
-    model.eval()
+    model_sent = AutoModelForSequenceClassification.from_pretrained("./local_models/sentiment_ITA/", num_labels=2)
+    model_sent.eval()
 
     tokenizer_emo = AutoTokenizer.from_pretrained("./local_models/emotion_ITA/")
     model_emo = AutoModelForSequenceClassification.from_pretrained("./local_models/emotion_ITA/", num_labels=4)
     model_emo.eval()
 
 
-#%%
+#%%generator_sent
 
 from transformers import pipeline
 import re
 
-generator = pipeline(task="text-classification", model=model, tokenizer=tokenizer, return_all_scores =True)
-generator_emo = pipeline(task="text-classification", model=model_emo, tokenizer=tokenizer_emo, return_all_scores =True)
+generator_sent = pipeline(task="text-classification", model_sent=model_sent, tokenizer=tokenizer, return_all_scores =True)
+generator_emo = pipeline(task="text-classification", model_sent=model_emo, tokenizer=tokenizer_emo, return_all_scores =True)
 
 def sentiment_emoji(input_abs):
 
     if(input_abs ==""):
         return "🤷‍♂️"
         
-    res = generator(input_abs)[0]
+    res = generator_sent(input_abs)[0]
     res = {res[x]["label"]: res[x]["score"] for x in range(len(res))}
     res["🙂 positive"] = res.pop("positive")
     res["🙁 negative"] = res.pop("negative")