test def

app.py

@@ -8,84 +8,84 @@ import torch.nn.functional as F
 
 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'))
-
-while True:
-    st.write('You selected:', option)
-
-    if option == 'MILESTONE 3: FINE-TUNED':
-        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)
-
-        # 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]
-
-        # X_train = [textIn]
-        # batch = tokenizer(X_train, padding=True, truncation=True, max_length=512, return_tensors="pt")
-
-        
-        # st.write('TESTING2')
+# option = st.selectbox('Which pre-trained model would you like for your sentiment analysis?',('Pipeline', 'TextBlob', 'MILESTONE 3: FINE-TUNED'))
+option = st.selectbox('Which pre-trained model would you like for your sentiment analysis?',('MILESTONE 3: FINE-TUNED', 'Pipeline'))
+
+st.write('You selected:', option)
+
+if option == 'MILESTONE 3: FINE-TUNED':
+    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)
+
+    # 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]
+
+    # X_train = [textIn]
+    # batch = tokenizer(X_train, padding=True, truncation=True, max_length=512, return_tensors="pt")
+
     
-    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)
+    # 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)
 
 
 #------------------------------------------------------------------------