app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch

model_name = "ayyuce/SmolGRPO-135M"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

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):
    generate_kwargs = {
        "max_new_tokens": int(max_new_tokens),
        "temperature": float(temperature),
        "top_p": float(top_p),
        "do_sample": do_sample == "Yes",
    }
    generated_list = generator(prompt, **generate_kwargs)
    generated_text = generated_list[0]["generated_text"]
    return generated_text

with gr.Blocks() as demo:
    gr.Markdown("# SmolGRPO-135M Text Generator")
    with gr.Row():
        with gr.Column():
            prompt = gr.Textbox(label="Prompt", lines=5, placeholder="Enter your prompt here...")
            max_new_tokens = gr.Number(label="Max New Tokens", value=256)
            temperature = gr.Slider(label="Temperature", minimum=0.0, maximum=1.0, value=0.5)
            top_p = gr.Slider(label="Top-p (Nucleus Sampling)", minimum=0.0, maximum=1.0, value=0.9)
            do_sample = gr.Dropdown(label="Do Sample", choices=["Yes", "No"], value="Yes")
            generate_button = gr.Button("Generate Text")
        with gr.Column():
            output = gr.Textbox(label="Generated Text", lines=15)
    
    generate_button.click(
        fn=generate_text,
        inputs=[prompt, max_new_tokens, temperature, top_p, do_sample],
        outputs=output
    )

if __name__ == "__main__":
    demo.launch()