COMMENTING. HEHEHE

Adding comments for future ease.

app.py

@@ -3,18 +3,18 @@ from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipe
 
 
 class EmotionClassifier:
-    def __init__(self):
-        self.model = AutoModelForSequenceClassification.from_pretrained("barbieheimer/MND_TweetEvalBert_model")
-        self.tokenizer = AutoTokenizer.from_pretrained("barbieheimer/MND_TweetEvalBert_model")
+    def __init__(self): # since we have defined the models below, this class will call itself.
+        self.model = AutoModelForSequenceClassification.from_pretrained("barbieheimer/MND_TweetEvalBert_model") # specify the model from repo.
+        self.tokenizer = AutoTokenizer.from_pretrained("barbieheimer/MND_TweetEvalBert_model") #need to spicify the tokeniser from repo too
         self.pipeline = pipeline(
-            "text-classification",
+            "text-classification", # specify pipeline task here.
             model=self.model,
             tokenizer=self.tokenizer,
-            return_all_scores=True,
+            return_all_scores=True, # so that all emotional scores are displayed.
         )
-
-    def predict(self, input_text: str):
-        pred = self.pipeline(input_text)[0]
+    # creating a prediction definition.
+    def predict(self, input_text: str): # defining what the output will look like.
+        pred = self.pipeline(input_text)[0] # processing text input.
         result = {
             "Anger 😠": pred[0]["score"],
            "Joy 😂": pred[1]["score"],
@@ -25,9 +25,10 @@ class EmotionClassifier:
 
 
 def main():
+    # call the emotionclassifier class to use our model, and now we can use the gradio UI.
     model = EmotionClassifier()
     iface = gr.Interface(
-        fn=model.predict,
+        fn=model.predict, # using the model to predict
         inputs=gr.inputs.Textbox(
             lines=3,
             placeholder="Type a phrase that has some emotion",