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 = 45  # Reduced timeout for faster failover
EXECUTOR = ThreadPoolExecutor(max_workers=3)  # Increased workers

# ===== OPTIMIZED WATERMARK FUNCTION (WebP) =====
def add_watermark(image_bytes):
    """Add watermark with optimized WebP output (85% quality)"""
    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 WebP (faster + smaller)
        webp_buffer = io.BytesIO()
        image.save(webp_buffer, format="WEBP", quality=85, method=6)  # 85% quality, best compression
        webp_buffer.seek(0)
        return Image.open(webp_buffer)
    except Exception as e:
        print(f"Watermark error: {str(e)}")
        return Image.open(io.BytesIO(image_bytes))

# ===== FASTER IMAGE GENERATION =====
def generate_image(prompt):
    if not prompt.strip():
        return None, "⚠️ Please enter a prompt"
    
    # Faster generation parameters
    params = {
        "height": 768,  # Slightly smaller = faster
        "width": 768,
        "num_inference_steps": 20,  # Reduced from 30
        "guidance_scale": 7.0,  # Slightly lower for speed
        "options": {"wait_for_model": False}  # Don't wait if model is loading
    }
    
    def api_call():
        return requests.post(
            API_URL,
            headers=headers,
            json={
                "inputs": prompt,
                "parameters": params
            },
            timeout=TIMEOUT
        )
    
    for attempt in range(MAX_RETRIES):
        try:
            start_time = time.time()
            response = EXECUTOR.submit(api_call).result()
            
            if response.status_code == 200:
                gen_time = time.time() - start_time
                return add_watermark(response.content), f"✔️ Generated in {gen_time:.1f}s"
            elif response.status_code == 503:
                if attempt < MAX_RETRIES - 1:
                    time.sleep(5 * (attempt + 1))  # Progressive backoff
                continue
            else:
                return None, f"⚠️ API Error: {response.text[:200]}"
        except requests.Timeout:
            return None, f"⚠️ Timeout: Model took >{TIMEOUT}s"
        except Exception as e:
            return None, f"⚠️ Error: {str(e)[:200]}"
    
    return None, "⚠️ Failed after retries. Try again."

# ===== GRADIO INTERFACE =====
with gr.Blocks(title="SelamGPT Image Generator") as demo:
    gr.Markdown("""
    # 🎨 SelamGPT Image Generator
    *Optimized for speed (WebP output @ 768px)*
    """)
    
    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
            )
            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"],
                    ["Traditional Ethiopian coffee ceremony"],
                    ["Habesha queen with jewelry"]
                ],
                inputs=prompt_input
            )
            
        with gr.Column(scale=2):
            output_image = gr.Image(
                label="Generated Image (WebP)",
                type="pil",
                format="webp",  # WebP output
                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(concurrency_count=3)  # Increased concurrency
    demo.launch(server_name="0.0.0.0", server_port=7860)