testing getting models

app.py

@@ -68,12 +68,40 @@ if option == 'TextBlob':
 
 
 if option == 'MILESTONE 3: FINE-TUNED':
-    model_name = "distilbert-base-uncased-finetuned-sst-2-english"
-    model = AutoModelForSequenceClassification.from_pretrained(model_name)
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    classifier = pipeline(task="sentiment-analysis", model=model, tokenizer=tokenizer)
 
-    # pipeline
-    preds = classifier(textIn)
-    preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
-    st.write('According to Pipeline, input text is ', preds[0]['label'], ' with a confidence of ', preds[0]['score'])
+    model_name_0 = "Rathgeberj/milestone3_0"
+    model_0 = AutoModelForSequenceClassification.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_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_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_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_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_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)
+
+    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]
+
+    
+    st.write('TESTING')