app.py

import os
import requests
import gradio as gr
from PIL import Image, ImageDraw, ImageFont
import io
import time
from concurrent.futures import ThreadPoolExecutor

# ===== CONFIGURATION =====
HF_API_TOKEN = os.environ.get("HF_API_TOKEN")
MODEL_NAME = "stabilityai/stable-diffusion-xl-base-1.0"
API_URL = f"https://api-inference.huggingface.co/models/{MODEL_NAME}"
headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
WATERMARK_TEXT = "SelamGPT"
MAX_RETRIES = 3
TIMEOUT = 60
EXECUTOR = ThreadPoolExecutor(max_workers=2)

# ===== WATERMARK FUNCTION =====
def add_watermark(image_bytes):
    """Add watermark with optimized PNG output"""
    try:
        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
        draw = ImageDraw.Draw(image)
        
        font_size = 24
        try:
            font = ImageFont.truetype("Roboto-Bold.ttf", font_size)
        except:
            font = ImageFont.load_default(font_size)
        
        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))
        
        # Convert to optimized PNG
        img_byte_arr = io.BytesIO()
        image.save(img_byte_arr, format='PNG', optimize=True, quality=85)
        img_byte_arr.seek(0)
        return Image.open(img_byte_arr)
    except Exception as e:
        print(f"Watermark error: {str(e)}")
        return Image.open(io.BytesIO(image_bytes))

# ===== IMAGE GENERATION =====
def generate_image(prompt):
    if not prompt.strip():
        return None, "⚠️ Please enter a prompt"
    
    def api_call():
        return requests.post(
            API_URL,
            headers=headers,
            json={
                "inputs": prompt,
                "parameters": {
                    "height": 1024,
                    "width": 1024,
                    "num_inference_steps": 30
                },
                "options": {"wait_for_model": True}
            },
            timeout=TIMEOUT
        )
    
    for attempt in range(MAX_RETRIES):
        try:
            future = EXECUTOR.submit(api_call)
            response = future.result()
            
            if response.status_code == 200:
                return add_watermark(response.content), "✔️ Generation successful"
            elif response.status_code == 503:
                wait_time = (attempt + 1) * 15
                print(f"Model loading, waiting {wait_time}s...")
                time.sleep(wait_time)
                continue
            else:
                return None, f"⚠️ API Error: {response.text[:200]}"
        except requests.Timeout:
            return None, f"⚠️ Timeout: Model took >{TIMEOUT}s to respond"
        except Exception as e:
            return None, f"⚠️ Unexpected error: {str(e)[:200]}"
    
    return None, "⚠️ Failed after multiple attempts. Please try later."

# ===== GRADIO THEME =====
theme = gr.themes.Default(
    primary_hue="emerald",
    secondary_hue="amber",
    font=[gr.themes.GoogleFont("Poppins"), "Arial", "sans-serif"]
)

# ===== GRADIO INTERFACE =====
with gr.Blocks(theme=theme, title="SelamGPT Image Generator") as demo:
    gr.Markdown("""
    # 🎨 SelamGPT Image Generator
    *Powered by Stable Diffusion XL (1024x1024 PNG output)*
    """)
    
    with gr.Row():
        with gr.Column(scale=3):
            prompt_input = gr.Textbox(
                label="Describe your image",
                placeholder="A futuristic Ethiopian city with flying cars...",
                lines=3,
                max_lines=5
            )
            with gr.Row():
                generate_btn = gr.Button("Generate Image", variant="primary")
                clear_btn = gr.Button("Clear")
            
            gr.Examples(
                examples=[
                    ["An ancient Aksumite warrior in cyberpunk armor, 4k detailed"],
                    ["Traditional Ethiopian coffee ceremony in zero gravity"],
                    ["Portrait of a Habesha queen with golden jewelry"]
                ],
                inputs=prompt_input
            )
            
        with gr.Column(scale=2):
            output_image = gr.Image(
                label="Generated Image",
                type="pil",
                format="png",
                height=512
            )
            status_output = gr.Textbox(
                label="Status",
                interactive=False
            )
    
    generate_btn.click(
        fn=generate_image,
        inputs=prompt_input,
        outputs=[output_image, status_output],
        queue=True
    )
    
    clear_btn.click(
        fn=lambda: [None, ""],
        outputs=[output_image, status_output]
    )

if __name__ == "__main__":
    demo.queue(max_size=2)
    demo.launch(server_name="0.0.0.0", server_port=7860)