edited code documentation

app.py

@@ -5,6 +5,7 @@ from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassific
 from PIL import Image
 import torch
 
+#Bertweet obtain label and score
 def bertweet(data):
     specific_model = pipeline(model="finiteautomata/bertweet-base-sentiment-analysis")
     result = specific_model(data)
@@ -13,12 +14,13 @@ def bertweet(data):
 
     return label, score 
 
+#Roberta obtain labels and score
 def roberta(data):
     specific_model = pipeline(model="cardiffnlp/twitter-roberta-base-sentiment")
     result = specific_model(data)
     label = result[0]['label']
     score = result[0]['score']
-
+    #Change name of labels
     if(label == 'LABEL_0'):
         label = 'Negative'
     elif(label == 'LABEL_1'):
@@ -28,6 +30,7 @@ def roberta(data):
 
     return label, score 
 
+#Siebert obtain labels and score
 def siebert(data):
     specific_model = pipeline(model='siebert/sentiment-roberta-large-english')
     result = specific_model(data)
@@ -36,18 +39,24 @@ def siebert(data):
 
     return label, score
 
+#Finetuned model obtain max and second highest labels and scores
 def finetuned(data):
+    #Access finetune model
     model_name = "dahongj/finetuned_toxictweets"
     tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)
     model = DistilBertForSequenceClassification.from_pretrained(model_name)
     tokenized_text = tokenizer(data, return_tensors="pt")
     res = model(**tokenized_text)
+
+    #Obtain score values
     mes = torch.sigmoid(res.logits)
 
+    #Labels corresponding to the array index
     Dict = {0: "toxic", 1: "severe_toxic", 2: "obscene", 3: "threat", 4: "insult", 5: "identity_hate"}
 
     maxres, maxscore, sec, secscore = Dict[0], mes[0][0].item(), 0, 0
 
+    #Search for second highest label
     for i in range(1,6):
         if mes[0][i].item() > secscore:
             sec = i
@@ -55,24 +64,29 @@ def finetuned(data):
 
     return maxres, maxscore, Dict[sec], secscore
 
+#Run model based on selection box
 def getSent(data, model):
     if(model == 'Bertweet'):
         label,score = bertweet(data)
+        #Create visual columns
         col1, col2 = st.columns(2)
         col1.metric("Feeling",label,None)
         col2.metric("Score",score,None)
     elif(model == 'Roberta'):
         label,score = roberta(data)
+        #Create visual columns
         col1, col2 = st.columns(2)
         col1.metric("Feeling",label,None)
         col2.metric("Score",score,None)
     elif(model == 'Siebert'):
         label,score = siebert(data)
+        #Create visual columns
         col1, col2 = st.columns(2)
         col1.metric("Feeling",label,None)
         col2.metric("Score",score,None)
     elif(model == 'Finetuned'):
         label, score, sec, secsc = finetuned(data)
+        #Create visual columns
         col1, col2 = st.columns(2)
         col3, col4 = st.columns(2)
         col1.metric("Highest",label,None)
@@ -84,16 +98,19 @@ def rendPage():
     st.title("Sentiment Analysis")
     userText = st.text_area('User Input', "Hope you are having a great day!")
     st.text("")
+    #Selection box
     type = st.selectbox(
         'Choose your model',
         ('Bertweet','Roberta','Siebert','Finetuned'))
     st.text("")
 
+    #Create button
     if st.button('Calculate'):
         if(userText!="" and type != None):
             st.text("")
             getSent(userText,type)
     
+    #Image for sample 10 texts
     image = Image.open("milestone3.jpg")
     st.image(image, caption="10 Example Texts")
 

finetune.py

@@ -23,9 +23,11 @@ class TextDataset(Dataset):
     self.labels = labels
 
   def __getitem__(self,idx):
+    #Create tokenizer
     encodings = tokenizer(self.texts[idx], truncation=True, padding="max_length")
     item = {key: torch.tensor(val) for key, val in encodings.items()}
     item['labels'] = torch.tensor(self.labels[idx],dtype=torch.float32)
+    #Remove encoding to prevent memory leak
     del encodings
     return item