app.py

import gradio as gr
import numpy as np
import random
from PIL import Image, ImageDraw, ImageFont
import io

from diffusers import DiffusionPipeline
import torch

# ===== CONFIG =====
device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if device == "cuda" else torch.float32
model_repo_id = "stabilityai/sdxl-turbo"
pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype, variant="fp16" if device == "cuda" else None)
pipe.to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
WATERMARK_TEXT = "SelamGPT"

# ===== WATERMARK FUNCTION =====
def add_watermark(image):
    draw = ImageDraw.Draw(image)
    font_size = 24
    try:
        font = ImageFont.truetype("Roboto-Bold.ttf", font_size)
    except:
        font = ImageFont.load_default()
    text_width = draw.textlength(WATERMARK_TEXT, font=font)
    x = image.width - text_width - 10
    y = image.height - 34
    draw.text((x+1, y+1), WATERMARK_TEXT, font=font, fill=(0, 0, 0, 128))
    draw.text((x, y), WATERMARK_TEXT, font=font, fill=(255, 255, 255))
    return image

# ===== IMAGE GENERATION FUNCTION =====
def generate(
    prompt,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    progress=gr.Progress(track_tqdm=True),
):
    if not prompt.strip():
        return None, "⚠️ Please enter a prompt"

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.manual_seed(seed)
    result = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        width=width,
        height=height,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        generator=generator,
    ).images[0]

    image = add_watermark(result)
    return image, seed

# ===== EXAMPLES =====
examples = [
    "A futuristic Ethiopian city with flying cars",
    "An ancient Aksumite queen in a high-tech palace, digital painting",
    "A cyberpunk Habesha coffee ceremony on Mars",
]

# ===== INTERFACE =====
theme = gr.themes.Default(
    primary_hue="cyan",
    secondary_hue="amber",
    font=[gr.themes.GoogleFont("Poppins"), "sans-serif"]
)

css = "#container { max-width: 800px; margin: 0 auto; }"

with gr.Blocks(css=css, theme=theme, title="SelamGPT Turbo Image Generator") as demo:
    with gr.Column(elem_id="container"):
        gr.Markdown("# 🎨 SelamGPT Turbo Image Generator\n*Powered by SDXL-Turbo (Fast & Creative)*")

        with gr.Row():
            prompt = gr.Textbox(
                label="Prompt",
                show_label=False,
                placeholder="Describe the image...",
                lines=2,
                scale=3
            )
            generate_btn = gr.Button("Generate", variant="primary")

        image_output = gr.Image(label="Generated Image", type="pil", format="png", height=512)
        seed_output = gr.Textbox(label="Seed Used", interactive=False)

        with gr.Accordion("⚙️ Advanced Settings", open=False):
            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Things to avoid (optional)", max_lines=1)
            randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
            seed = gr.Slider(0, MAX_SEED, label="Seed", step=1, value=0)

            with gr.Row():
                width = gr.Slider(256, MAX_IMAGE_SIZE, step=32, label="Width", value=1024)
                height = gr.Slider(256, MAX_IMAGE_SIZE, step=32, label="Height", value=1024)

            with gr.Row():
                guidance_scale = gr.Slider(0.0, 10.0, step=0.1, label="Guidance Scale", value=0.0)
                num_inference_steps = gr.Slider(1, 50, step=1, label="Inference Steps", value=2)

        gr.Examples(examples=examples, inputs=[prompt])

        generate_btn.click(
            fn=generate,
            inputs=[
                prompt,
                negative_prompt,
                seed,
                randomize_seed,
                width,
                height,
                guidance_scale,
                num_inference_steps
            ],
            outputs=[image_output, seed_output]
        )

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)