Update app.py

app.py

@@ -2,30 +2,23 @@ import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
-
-# Load the tokenizer and model
 model_name = "ayyuce/SmolGRPO-135M"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
 
-# Initialize the text-generation pipeline
 generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=-1)  # device=-1 ensures CPU usage
 
 def generate_text(prompt, max_new_tokens, temperature, top_p, do_sample):
-    # Define generation parameters
     generate_kwargs = {
         "max_new_tokens": int(max_new_tokens),
         "temperature": float(temperature),
         "top_p": float(top_p),
         "do_sample": do_sample == "Yes",
     }
-    # Generate text
     generated_list = generator(prompt, **generate_kwargs)
-    # Extract the generated text from the first item in the list
     generated_text = generated_list[0]["generated_text"]
     return generated_text
 
-# Create the Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("# SmolGRPO-135M Text Generator")
     with gr.Row():