import gradio as gr
import torch
from diffusers import DiffusionPipeline
from PIL import Image

# Load the model
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
pipe = DiffusionPipeline.from_pretrained(
    model_id,
    torch_dtype=torch.float16,  # Use float32 for CPU
    use_safetensors=True
)
pipe.to("cuda")

def generate_image(prompt, negative_prompt, size):
    if not prompt:
        prompt = "a beautiful landscape"
    if not negative_prompt:
        negative_prompt = ""
        
    width, height = map(int, size.split('x'))
    generator = torch.Generator("cuda").manual_seed(42)

    try:
        result = pipe(prompt=prompt, height=height, width=width, negative_prompt=negative_prompt, generator=generator)
        if result and hasattr(result, 'images') and len(result.images) > 0:
            return result.images[0]
        else:
            print("Error: No images in the result or result is None")
            return None
    except Exception as e:
        print(f"Error occurred: {e}")
        return None

with gr.Blocks() as demo:
    gr.Markdown("## Text to Image SDXL")
    
    with gr.Row():
        with gr.Column():
            prompt = gr.Textbox(label="Prompt", placeholder="Enter the prompt here...")
            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter the negative prompt here...")
            size = gr.Dropdown(choices=["512x512", "768x768", "1024x1024"], value="1024x1024", label="Size")
            submit = gr.Button("Submit")
        with gr.Column():
            output = gr.Image(label="Output")

    submit.click(generate_image, inputs=[prompt, negative_prompt, size], outputs=output)

demo.launch()