test model loading

app.py

@@ -8,62 +8,51 @@ 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'))
-option = st.selectbox('Which pre-trained model would you like for your sentiment analysis?',('MILESTONE 3', 'Pipeline'))
+option = st.selectbox('Which pre-trained model would you like for your sentiment analysis?',('MILESTONE 3', 'Pipeline', 'TextBlob'))
 
 st.write('You selected:', option)
 
 if option == 'MILESTONE 3':
-    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)
+    st.write('test1')
+
+    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_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_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_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_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)
+    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]
+    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')
+    st.write('test2')
 
 if option == 'Pipeline':
 
@@ -75,17 +64,17 @@ if option == 'Pipeline':
     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)
+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)
 
 
 #------------------------------------------------------------------------