app.py

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

# Load the fine-tuned model and tokenizer
model_name = "gpt2" 
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

def generate_pixel_art(prompt):
    input_ids = tokenizer.encode(prompt, return_tensors="pt")
    output = model.generate(input_ids, max_length=2304, num_return_sequences=1)
    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
    return generated_text

def hex_to_rgb(hex_string):
    return [int(hex_string[i:i+2], 16) for i in (0, 2, 4)]

def visualize_pixel_art(hex_string):
    pixels = [hex_to_rgb(hex_string[i:i+6]) for i in range(0, len(hex_string), 6)]
    img = Image.new('RGB', (16, 16))
    img.putdata(pixels)
    return img

demo = gr.Interface(
    fn=generate_pixel_art,
    inputs=gr.Textbox(lines=2, placeholder="Enter a prompt for pixel art generation..."),
    outputs=[gr.Textbox(label="Generated Hex String"), gr.Image(label="Visualized Pixel Art")],
    title="LlamaSquint Pixel Art Generator",
    description="Generate pixel art using a fine-tuned LLaMa model"
)

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