test while loop

app.py

@@ -10,7 +10,71 @@ textIn = st.text_input("Input Text Here:", "I really like the color of your car!
 
 option = st.selectbox('Which pre-trained model would you like for your sentiment analysis?',('Pipeline', 'TextBlob', 'MILESTONE 3: FINE-TUNED'))
 
-st.write('You selected:', option)
+while True:
+    st.write('You selected:', option)
+
+    if option == 'MILESTONE 3: FINE-TUNED':
+
+        st.write('TESTING1')
+
+        # 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('TESTING2')
+    
+    if option == 'Pipeline':
+
+        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)
+        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'])
+
+    if option == 'TextBlob':
+        polarity = TextBlob(textIn).sentiment.polarity
+        subjectivity = TextBlob(textIn).sentiment.subjectivity
+        sentiment = ''
+        if polarity < 0:
+            sentiment = 'Negative'
+        elif polarity == 0:
+            sentiment = 'Neutral'
+        else:
+            sentiment = 'Positive'
+        st.write('According to TextBlob, input text is ', sentiment, ' and a subjectivity score (from 0 being objective to 1 being subjective) of ', subjectivity)
 
 
 #------------------------------------------------------------------------
@@ -38,72 +102,4 @@ st.write('You selected:', option)
 # tokenizer = AutoTokenizer.from_pretrained(save_directory)
 # model = AutoModelForSequenceClassification.from_pretrained(save_directory)
 
-#------------------------------------------------------------------------
-
-if option == 'Pipeline':
-
-    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'])
-
-if option == 'TextBlob':
-    # textblob
-    polarity = TextBlob(textIn).sentiment.polarity
-    subjectivity = TextBlob(textIn).sentiment.subjectivity
-    sentiment = ''
-    if polarity < 0:
-        sentiment = 'Negative'
-    elif polarity == 0:
-        sentiment = 'Neutral'
-    else:
-        sentiment = 'Positive'
-
-    st.write('According to TextBlob, input text is ', sentiment, ' and a subjectivity score (from 0 being objective to 1 being subjective) of ', subjectivity)
-
-
-if option == 'MILESTONE 3: FINE-TUNED':
-
-    st.write('TESTING1')
-
-    # 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('TESTING2')
+#------------------------------------------------------------------------