Update app.py

app.py

@@ -12,9 +12,19 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 # AutoTokenizer helps in Text input -> Sentences -> Words -> Even subwords like ['un', 'break', 'able'] -> Integer IDs that model expects.
 # And whats awesome is Tokens will be generated following the configurations and requirements of model which we will be using.
 
+# AutoModelForCausalLM is a powerful and convenient class serves as a high-level interface for loading pre-trained transformer models specifically designed for causal language modeling.
+# The cooler part of these Auto* classes are you don't need to know exact class name of model(like GPT2LMHeadModel, CTRLLMHeadModel, ReformerLMHeadModel, etc.).
+# Meaning The AutoModelForCausalLM automatically determine the correct model architecture based on the pretrained_model_name_or_path you provide. AutoModelForCausalLM infers this from the configuration files associated with the pre-trained model.
 
 from wordcloud import WordCloud
+# This will help us in knowing which words have large frequency. It creates a visual representation of words used, know as Word Cloud.
+# More the frequency + More the importance -> Word will appear larger in Word Cloud.
+# Mostly it avoids our stop words like it, is, are etc
+
+
 import matplotlib.pyplot as plt
+# This guy helps us to plot. So wait till you see it.
+
 import torch
 
 # Cache the model and tokenizer to avoid reloading on every run