Update app.py

app.py

@@ -1,36 +1,34 @@
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
+# from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-download = False
-save_model_locally= False
-if download:
-    tokenizer = AutoTokenizer.from_pretrained("MilaNLProc/feel-it-italian-sentiment", cache_dir="data/")
-    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_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_sent = AutoModelForSequenceClassification.from_pretrained("./local_models/sentiment_ITA/", num_labels=2)
-    model_sent.eval()
+# download = False
+# save_model_locally= False
+# if download:
+#     tokenizer = AutoTokenizer.from_pretrained("MilaNLProc/feel-it-italian-sentiment", cache_dir="data/")
+#     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_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_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()
+#     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
+# #%%generator_sent
 
 from transformers import pipeline
-import re
-
-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)
+generator_sent = pipeline(task="text-classification", model='./local_models/sentiment_ITA/', top_k=None)
+generator_emo = pipeline(task="text-classification", model='./local_models/emotion_ITA/', top_k=None)
 
 def sentiment_emoji(input_abs):
 
@@ -38,6 +36,7 @@ def sentiment_emoji(input_abs):
         return "🤷‍♂️"
         
     res = generator_sent(input_abs)[0]
+    print("res: ", res)
     res = {res[x]["label"]: res[x]["score"] for x in range(len(res))}
     res["🙂 positive"] = res.pop("positive")
     res["🙁 negative"] = res.pop("negative")