load all models

app.py

@@ -16,43 +16,46 @@ option = st.selectbox('Which pre-trained model would you like for your sentiment
 st.write('You selected:', option)
 
 if option == 'MILESTONE 3':
-
     st.write('test1')
-
-    model_name_0 = "Rathgeberj/milestone3_1"
+    model_name_0 = "Rathgeberj/milestone3_0"
     # model_0 = AutoModelForSequenceClassification.from_pretrained(model_name_0)
-    model_0 = BertForMaskedLM.from_pretrained("Rathgeberj/milestone3_0")
+    model_0 = BertForMaskedLM.from_pretrained(model_name_0)
     tokenizer_0 = AutoTokenizer.from_pretrained(model_name_0)
     classifier_0 = pipeline(task="sentiment-analysis", model=model_0, tokenizer=tokenizer_0)
 
-    # model_name_1 = "Rathgeberj/milestone3_1"
+    model_name_1 = "Rathgeberj/milestone3_1"
     # model_1 = AutoModelForSequenceClassification.from_pretrained(model_name_1)
-    # tokenizer_1 = AutoTokenizer.from_pretrained(model_name_1)
-    # classifier_1 = pipeline(task="sentiment-analysis", model=model_1, tokenizer=tokenizer_1)
+    model_1 = BertForMaskedLM.from_pretrained(model_name_1)
+    tokenizer_1 = AutoTokenizer.from_pretrained(model_name_1)
+    classifier_1 = pipeline(task="sentiment-analysis", model=model_1, tokenizer=tokenizer_1)
 
-    # model_name_2 = "Rathgeberj/milestone3_2"
+    model_name_2 = "Rathgeberj/milestone3_2"
     # model_2 = AutoModelForSequenceClassification.from_pretrained(model_name_2)
-    # tokenizer_2 = AutoTokenizer.from_pretrained(model_name_2)
-    # classifier_2 = pipeline(task="sentiment-analysis", model=model_2, tokenizer=tokenizer_2)
+    model_2 = BertForMaskedLM.from_pretrained(model_name_2)
+    tokenizer_2 = AutoTokenizer.from_pretrained(model_name_2)
+    classifier_2 = pipeline(task="sentiment-analysis", model=model_2, tokenizer=tokenizer_2)
 
-    # model_name_3 = "Rathgeberj/milestone3_3"
+    model_name_3 = "Rathgeberj/milestone3_3"
     # model_3 = AutoModelForSequenceClassification.from_pretrained(model_name_3)
-    # tokenizer_3 = AutoTokenizer.from_pretrained(model_name_3)
-    # classifier_3 = pipeline(task="sentiment-analysis", model=model_3, tokenizer=tokenizer_3)
+    model_3 = BertForMaskedLM.from_pretrained(model_name_3)
+    tokenizer_3 = AutoTokenizer.from_pretrained(model_name_3)
+    classifier_3 = pipeline(task="sentiment-analysis", model=model_3, tokenizer=tokenizer_3)
 
-    # model_name_4 = "Rathgeberj/milestone3_4"
+    model_name_4 = "Rathgeberj/milestone3_4"
     # model_4 = AutoModelForSequenceClassification.from_pretrained(model_name_4)
-    # tokenizer_4 = AutoTokenizer.from_pretrained(model_name_4)
-    # classifier_4 = pipeline(task="sentiment-analysis", model=model_4, tokenizer=tokenizer_4)
+    model_4 = BertForMaskedLM.from_pretrained(model_name_4)
+    tokenizer_4 = AutoTokenizer.from_pretrained(model_name_4)
+    classifier_4 = pipeline(task="sentiment-analysis", model=model_4, tokenizer=tokenizer_4)
 
-    # model_name_5 = "Rathgeberj/milestone3_5"
+    model_name_5 = "Rathgeberj/milestone3_5"
     # model_5 = AutoModelForSequenceClassification.from_pretrained(model_name_5)
-    # tokenizer_5 = AutoTokenizer.from_pretrained(model_name_5)
-    # classifier_5 = pipeline(task="sentiment-analysis", model=model_5, tokenizer=tokenizer_5)
+    model_5 = BertForMaskedLM.from_pretrained(model_name_5)
+    tokenizer_5 = AutoTokenizer.from_pretrained(model_name_5)
+    classifier_5 = pipeline(task="sentiment-analysis", model=model_5, tokenizer=tokenizer_5)
 
-    # models = [model_0, model_1, model_2, model_3, model_4, model_5]
-    # tokenizers = [tokenizer_0, tokenizer_1, tokenizer_2, tokenizer_3, tokenizer_4, tokenizer_5]
-    # classifiers = [classifier_0, classifier_1, classifier_2, classifier_3, classifier_4, classifier_5]
+    models = [model_0, model_1, model_2, model_3, model_4, model_5]
+    tokenizers = [tokenizer_0, tokenizer_1, tokenizer_2, tokenizer_3, tokenizer_4, tokenizer_5]
+    classifiers = [classifier_0, classifier_1, classifier_2, classifier_3, classifier_4, classifier_5]
 
     # X_train = [textIn]
     # batch = tokenizer(X_train, padding=True, truncation=True, max_length=512, return_tensors="pt")