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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 # Increased timeout for larger images
EXECUTOR = ThreadPoolExecutor(max_workers=2) # Handle concurrent requests
# ===== WATERMARK FUNCTION =====
def add_watermark(image_bytes):
"""Add professional watermark with fallback fonts"""
try:
image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
draw = ImageDraw.Draw(image)
# Try multiple font options
font = None
for font_path in [
"Roboto-Bold.ttf", # Our Dockerfile installs this
"DejaVuSans-Bold.ttf",
"FreeSansBold.ttf",
None # Final fallback to default
]:
try:
size = min(image.width // 15, 40) # Responsive sizing
font = ImageFont.truetype(font_path, size) if font_path else ImageFont.load_default(size)
break
except:
continue
# Calculate dynamic position
bbox = draw.textbbox((0, 0), WATERMARK_TEXT, font=font)
text_w, text_h = bbox[2] - bbox[0], bbox[3] - bbox[1]
margin = image.width // 50
position = (image.width - text_w - margin, image.height - text_h - margin)
# Draw with outline effect
for offset in [(-1,-1), (1,1)]: # Shadow positions
draw.text(
(position[0]+offset[0], position[1]+offset[1]),
WATERMARK_TEXT,
font=font,
fill=(0, 0, 0, 180)) # Semi-transparent black
draw.text(
position,
WATERMARK_TEXT,
font=font,
fill=(255, 255, 255, 200)) # Semi-transparent white
return image
except Exception as e:
print(f"Watermark error: {str(e)}")
return Image.open(io.BytesIO(image_bytes)) # Fallback to original
# ===== IMAGE GENERATION =====
def generate_image(prompt):
"""Generate image with robust error handling"""
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": 768,
"width": 768,
"num_inference_steps": 25
},
"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) * 10
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, "⚠️ Timeout: Model took too long to respond"
except Exception as e:
return None, f"⚠️ Unexpected error: {str(e)[:200]}"
return None, "⚠️ Failed after multiple attempts. Please try again later."
# ===== GRADIO INTERFACE =====
theme = gr.themes.Default(
primary_hue="emerald",
secondary_hue="amber",
font=[gr.themes.GoogleFont("Poppins"), "Arial", "sans-serif"]
)
with gr.Blocks(theme=theme, title="SelamGPT Image Generator") as demo:
gr.Markdown("""
# 🎨 SelamGPT Image Generator
*Generate watermarked images with Stable Diffusion XL*
""")
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,
elem_id="prompt-box"
)
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 in space"],
["Hyper-realistic portrait of a Habesha woman with neon tribal markings"]
],
inputs=prompt_input,
label="Example Prompts"
)
with gr.Column(scale=2):
output_image = gr.Image(
label="Generated Image",
height=512,
elem_id="output-image"
)
status_output = gr.Textbox(
label="Status",
interactive=False,
elem_id="status-box"
)
# Event handlers
generate_btn.click(
fn=generate_image,
inputs=prompt_input,
outputs=[output_image, status_output],
queue=True,
show_progress="minimal"
)
clear_btn.click(
fn=lambda: [None, ""],
outputs=[output_image, status_output]
)
# ===== DEPLOYMENT CONFIG =====
if __name__ == "__main__":
demo.queue(concurrency_count=2, api_open=False)
demo.launch(
server_name="0.0.0.0",
server_port=7860,
favicon_path="./favicon.ico" # Optional
) |