Added the final comments to Complete the Tutorial keeping in mind Previous beginner me is following the code to learn.

app.py

@@ -57,6 +57,7 @@ def load_model_and_tokenizer():
 
 
 # Function to generate text with Gemma
+# This calls the function we just made to get the tokenizer and model ready to work.
 def generate_text(prompt, tone, max_length, temperature=0.7, top_p=0.9, repetition_penalty=1.0):
     tokenizer, model = load_model_and_tokenizer()
     # Adjust prompt based on tone
@@ -66,20 +67,29 @@ def generate_text(prompt, tone, max_length, temperature=0.7, top_p=0.9, repetiti
         "Serious": f"Provide a detailed, thoughtful, and professional response to: {prompt}. Offer logical reasoning, depth, and a formal tone, as if explaining to an expert audience. For example, if the prompt is 'The future of AI,' discuss ethical implications and technical challenges in 2-3 sentences.",
         "Poetic": f"Write a vivid, poetic response to: {prompt}. Use metaphor, rhythm, and imagery to create a lyrical flow, as if crafting a short verse. For instance, if the prompt is 'The river flows,' respond with 'The river flows, a silver thread, / Through emerald dreams where silence tread.'"
     }
+    # This creates a dictionary that holds different prompts based on the tone we pick, making sure the model knows how to respond.
     input_text = tone_prompts.get(tone, prompt)
-    
+    # This picks the right instruction from the dictionary based on the tone.
     inputs = tokenizer(input_text, return_tensors="pt")
+    # This turns our input text (with the tone instruction) into a format (tensors) that the model can process using the tokenizer.
     outputs = model.generate(
         inputs["input_ids"],
         max_length=max_length + len(input_text.split()),
+        # This sets how long the generated text can be. We add the number of words in our input text (len(input_text.split())) to the max_length the user picked, so the model knows how many total words to create.
         temperature=temperature,
+        # This controls how creative the model gets. A lower temperature (e.g., 0.7) keeps things more predictable, while a higher one makes it wilder and more random—think of it like adjusting the spice level!
         top_p=top_p,
+        # This is like a filter for word choices. It picks from the top percentage of likely words (e.g., 0.9 means 90% of the best options), making the output diverse but not too crazy.
         repetition_penalty=repetition_penalty,
+        # This stops the model from repeating the same words too much. A higher value (e.g., 1.5) pushes it to try new words, like telling it to mix up its vocabulary!
         num_return_sequences=1,
+        # This tells the model to give us just one version of the text. If we wanted more options, we could change
         do_sample=True
     )
+    # This tells the model to generate text: it uses the input IDs, sets a max length, and adjusts creativity with temperature, top_p, and repetition_penalty.
     return tokenizer.decode(outputs[0], skip_special_tokens=True)
-
+    # This turns again the model's output back into readable form, skipping any extra tokens we don’t need.
+    
 # Clean and Solid UI for our Project, keeping the blue theme of gemini.
 # We will continue tutorial after this st.markdown()
 st.markdown("""
@@ -335,8 +345,12 @@ if "trigger_example" not in st.session_state:
 with col1:
     if st.button("✨ Funny Cat Story"):
         st.session_state.prompt = "The cat hacked my WiFi"
+        # This sets the prompt to a fun example when the button is clicked
         st.session_state.tone = "Funny"
+        # This sets the tone to Funny for the example.
         st.session_state.trigger_example = True
+        # This turns on the trigger to automatically generate the example.
+        # And then same things with below Poetic and Serious tones of ours, Phewww, don't be sleepy, little more.
 with col2:
     if st.button("🌅 Poetic Goodbye"):
         st.session_state.prompt = "As the sun set on our final day"
@@ -350,15 +364,19 @@ with col3:
 
 # Clean form with better spacing
 with st.form(key="input_form"):
+    # This starts a form where users can input their own prompt and settings
     st.markdown('<div style="margin-bottom: 15px;"><h3 style="color: #4A5BEA; margin-bottom: 10px;">Generate Your Text</h3></div>', unsafe_allow_html=True)
-    
+    # This adds a styled heading to label the form section.
     prompt = st.text_input("Enter a prompt", placeholder="e.g., 'The future of AI is'", value=st.session_state.get("prompt", ""))
-    
+    # This creates a text box where users can type their prompt, with a placeholder hint and a default value from our example if set.
     col1, col2 = st.columns(2)
+    # This splits the next part into two columns for better layout. Yep Saiyans like orgainised layouts.
     with col1:
         tone = st.selectbox("Tone", ["Funny", "Serious", "Poetic"], index=["Funny", "Serious", "Poetic"].index(st.session_state.get("tone", "Funny")))
     with col2:
         max_length = st.slider("Word count", 20, 100, 50)
+        # This adds a slider for users to set how many words they want in the output, ranging from 20 to 100 with a default of 50.
+        # And similarly every slider here works
     
     st.markdown('<div class="parameter-card"><h4>Advanced Parameters</h4>', unsafe_allow_html=True)
     
@@ -394,11 +412,15 @@ with st.form(key="input_form"):
 
 # Generate and display output
 if submit_button or st.session_state.trigger_example:
+# This checks if the Generate button was clicked or our predefined one click example was triggered.
     st.session_state.trigger_example = False
+    # This resets the example trigger so it doesn’t keep running.
     if not prompt:
         st.error("Please enter a prompt!")
+        # If user thought clicking generate is fun without entering a prompt. Naah buddy, i stopped you :)
     else:
         with st.spinner("Generating text..."):
+            # This shows a spinning icon while the text is being created.
             output = generate_text(prompt, tone, max_length, temperature, top_p, repetition_penalty)
             
             # Display metadata about the generation with improved value styling
@@ -419,6 +441,7 @@ if submit_button or st.session_state.trigger_example:
             # WordCloud visualization
             st.markdown('<div class="wordcloud-container">', unsafe_allow_html=True)
             st.markdown('<h4 style="color: #4A5BEA; margin-top: 0;">Word Cloud Visualization</h4>', unsafe_allow_html=True)
+            # This starts a container for the word cloud visualization.
             
             # Generate a clean wordcloud
             wordcloud = WordCloud(
@@ -445,4 +468,7 @@ st.markdown("""
     </p>
     <p style="margin-top: 10px;">Wish me luck! 🤞</p>         
 </div>
-""", unsafe_allow_html=True)
+""", unsafe_allow_html=True)
+
+# Yep here we done. Hope you guys like my attempt. I really enjoyed this project as Google Gen AI + Kaggle Workshop also helped. Thanks for your time
+# And please let me know the improvements i need, Will appreciate any reviews.