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 import random
import os
import uuid
from datetime import datetime
import gradio as gr
import numpy as np
import spaces
import torch
from diffusers import DiffusionPipeline
from PIL import Image
import re
import tempfile
import io
import logging
# -----------------------------
# Google Gemini API κ΄€λ ¨
# -----------------------------
import google.generativeai as genai
import google.generativeai.types as genai_types
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
###############################################################################
# 1. ν…μŠ€νŠΈ(ν•œκΈ€ β†’ μ˜μ–΄) λ³€ν™˜ 보쑰 ν•¨μˆ˜
###############################################################################
def maybe_translate_to_english(text: str) -> str:
"""
ν…μŠ€νŠΈμ— ν•œκ΅­μ–΄κ°€ 있으면 κ°„λ‹¨ν•œ μΉ˜ν™˜ κ·œμΉ™μ— 따라 μ˜μ–΄λ‘œ λ³€ν™˜.
"""
translations = {
"μ•ˆλ…•ν•˜μ„Έμš”": "Hello",
"ν™˜μ˜ν•©λ‹ˆλ‹€": "Welcome",
"μ•ˆλ…•": "Hello",
"λ°°λ„ˆ": "Banner",
# ν•„μš”μ— 따라 μΆ”κ°€
}
for kr, en in translations.items():
if kr in text:
text = text.replace(kr, en)
return text
###############################################################################
# 2. Gemini API ν˜ΈμΆœμ„ μœ„ν•œ μ€€λΉ„
###############################################################################
def save_binary_file(file_name, data):
""" 이진 νŒŒμΌμ„ μ €μž₯ν•˜λŠ” 헬퍼 ν•¨μˆ˜ """
with open(file_name, "wb") as f:
f.write(data)
def generate_by_google_genai(text, file_name, model="gemini-2.0-flash-exp"):
"""
Google Gemini APIλ₯Ό ν˜ΈμΆœν•΄ ν…μŠ€νŠΈ 기반 이미지 νŽΈμ§‘/생성을 μˆ˜ν–‰.
file_name: 원본 이미지λ₯Ό μž„μ‹œ μ—…λ‘œλ“œν•˜μ—¬ API둜 전달
text: μ μš©ν•  ν…μŠ€νŠΈ μ§€μ‹œμ‚¬ν•­
"""
api_key = os.getenv("GAPI_TOKEN")
if not api_key:
raise ValueError("GAPI_TOKEN is missing. Please set an API key.")
# Gemini API 인증 μ„€μ •
genai.configure(api_key=api_key)
# 이미지 파일 μ—…λ‘œλ“œ
uploaded_file = genai.upload_file(path=file_name)
# API에 전달할 content ꡬ성
contents = [
genai_types.Content(
role="user",
parts=[
# λ¨Όμ € μ—…λ‘œλ“œλœ 파일 URIλ₯Ό 포함
genai_types.Part.from_uri(
file_uri=uploaded_file.uri,
mime_type=uploaded_file.mime_type,
),
# μ΄μ–΄μ„œ text μ§€μ‹œμ‚¬ν•­μ„ 포함
genai_types.Part.from_text(text=text),
],
),
]
# 생성(νŽΈμ§‘) μ„€μ •
generation_config = genai_types.GenerationConfig(
temperature=1,
top_p=0.95,
top_k=40,
max_output_tokens=8192, # 좜λ ₯ 토큰 μ œν•œ
response_mime_type="text/plain",
)
text_response = "" # APIκ°€ λ°˜ν™˜ν•œ ν…μŠ€νŠΈ λˆ„μ 
image_path = None # APIκ°€ λ°˜ν™˜ν•œ 이미지 파일의 둜컬 경둜
# μž„μ‹œ νŒŒμΌμ— νŽΈμ§‘λœ 이미지 μ €μž₯
with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
temp_path = tmp.name
# 슀트리밍 ν˜•νƒœλ‘œ 응닡을 λ°›μŒ
response = genai.generate_content(
model=model,
contents=contents,
generation_config=generation_config,
stream=True
)
# 슀트리밍된 chunkλ“€μ—μ„œ 이미지와 ν…μŠ€νŠΈλ₯Ό μΆ”μΆœ
for chunk in response:
for candidate in chunk.candidates:
for part in candidate.content.parts:
# 이미지인 경우
if hasattr(part, 'inline_data') and part.inline_data:
save_binary_file(temp_path, part.inline_data.data)
image_path = temp_path
break
# ν…μŠ€νŠΈμΈ 경우
elif hasattr(part, 'text'):
text_response += part.text + "\n"
if image_path:
break
if image_path:
break
# μ—…λ‘œλ“œλœ μž„μ‹œ 파일 μ‚­μ œ
genai.delete_file(uploaded_file.name)
return image_path, text_response
###############################################################################
# 3. 이미지에 ν…μŠ€νŠΈλ₯Ό μ‚½μž…/μˆ˜μ •ν•˜λŠ” ν•¨μˆ˜ (Gemini API 2회 호좜)
###############################################################################
def change_text_in_image_two_times(original_image, instruction):
"""
Gemini APIλ₯Ό 두 번 ν˜ΈμΆœν•˜μ—¬ 두 개의 버전을 μƒμ„±ν•œλ‹€.
"""
import numpy as np
# λ§Œμ•½ 이미지가 numpy.ndarray νƒ€μž…μ΄λ©΄ PIL둜 λ³€ν™˜
if isinstance(original_image, np.ndarray):
original_image = Image.fromarray(original_image)
results = []
for version_tag in ["(A)", "(B)"]:
mod_instruction = f"{instruction} {version_tag}"
try:
with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
original_path = tmp.name
if isinstance(original_image, Image.Image):
original_image.save(original_path, format="PNG")
logging.debug(f"[DEBUG] Saved image to temporary file: {original_path}")
else:
raise gr.Error(f"μ˜ˆμƒλœ PIL Imageκ°€ μ•„λ‹Œ {type(original_image)} νƒ€μž…μ΄ μ œκ³΅λ˜μ—ˆμŠ΅λ‹ˆλ‹€.")
# Gemini API 호좜
image_path, text_response = generate_by_google_genai(
text=mod_instruction,
file_name=original_path
)
if image_path:
# λ°˜ν™˜λœ 이미지 λ‘œλ“œ
try:
with open(image_path, "rb") as f:
image_data = f.read()
new_img = Image.open(io.BytesIO(image_data))
results.append(new_img)
except Exception as img_err:
logging.error(f"[ERROR] Failed to process Gemini image: {img_err}")
results.append(original_image)
else:
logging.warning(f"[WARNING] 이미지가 λ°˜ν™˜λ˜μ§€ μ•Šμ•˜μŠ΅λ‹ˆλ‹€. ν…μŠ€νŠΈ 응닡: {text_response}")
results.append(original_image)
except Exception as e:
logging.exception(f"Text modification error: {e}")
results.append(original_image)
return results
###############################################################################
# 4. ν…μŠ€νŠΈ λ Œλ”λ§(문자 μ‚½μž…)용 ν•¨μˆ˜
###############################################################################
def gemini_text_rendering(image, rendering_text):
"""
주어진 image에 λŒ€ν•΄ Gemini API둜 text_rendering을 적용
"""
rendering_text_en = maybe_translate_to_english(rendering_text)
instruction = (
f"Render the following text on the image in a clear, visually appealing manner: "
f"{rendering_text_en}."
)
# 이미지에 ν…μŠ€νŠΈ μ‚½μž…(A/B 버전 2회 생성) β†’ μ—¬κΈ°μ„œλŠ” 2회 쀑 첫 번째만 λ°˜ν™˜
rendered_images = change_text_in_image_two_times(image, instruction)
if rendered_images and len(rendered_images) > 0:
return rendered_images[0]
return image
def apply_text_rendering(image, rendering_text):
"""
rendering_textκ°€ μ‘΄μž¬ν•˜λ©΄ Gemini API둜 ν…μŠ€νŠΈ μ‚½μž…μ„ 적용.
μ—†μœΌλ©΄ 원본 이미지λ₯Ό κ·ΈλŒ€λ‘œ λ°˜ν™˜.
"""
if rendering_text and rendering_text.strip():
return gemini_text_rendering(image, rendering_text)
return image
###############################################################################
# 5. Diffusion Pipeline λ‘œλ“œ 및 κΈ°λ³Έ μ„ΈνŒ…
###############################################################################
SAVE_DIR = "saved_images"
if not os.path.exists(SAVE_DIR):
os.makedirs(SAVE_DIR, exist_ok=True)
device = "cuda" if torch.cuda.is_available() else "cpu"
repo_id = "black-forest-labs/FLUX.1-dev"
adapter_id = "openfree/flux-chatgpt-ghibli-lora"
def load_model_with_retry(max_retries=5):
"""
둜컬 λ˜λŠ” Hugging Faceλ‘œλΆ€ν„° λͺ¨λΈ(FLUX.1-dev) + LoRA μ–΄λŒ‘ν„°(weights)λ₯Ό λΆˆλŸ¬μ˜¨λ‹€.
"""
for attempt in range(max_retries):
try:
logging.info(f"Loading model attempt {attempt+1}/{max_retries}...")
pipeline = DiffusionPipeline.from_pretrained(
repo_id,
torch_dtype=torch.bfloat16,
use_safetensors=True,
resume_download=True
)
logging.info("Model loaded successfully, loading LoRA weights...")
pipeline.load_lora_weights(adapter_id)
pipeline = pipeline.to(device)
logging.info("Pipeline ready!")
return pipeline
except Exception as e:
if attempt < max_retries - 1:
wait_time = 10 * (attempt + 1)
logging.error(f"Error loading model: {e}. Retrying in {wait_time} seconds...")
import time
time.sleep(wait_time)
else:
raise Exception(f"Failed to load model after {max_retries} attempts: {e}")
pipeline = load_model_with_retry()
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
def save_generated_image(image, prompt):
"""
μƒμ„±λœ 이미지λ₯Ό μ €μž₯ν•˜λ©΄μ„œ 메타 정보λ₯Ό κΈ°λ‘ν•œλ‹€.
"""
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
unique_id = str(uuid.uuid4())[:8]
filename = f"{timestamp}_{unique_id}.png"
filepath = os.path.join(SAVE_DIR, filename)
image.save(filepath)
metadata_file = os.path.join(SAVE_DIR, "metadata.txt")
with open(metadata_file, "a", encoding="utf-8") as f:
f.write(f"{filename}|{prompt}|{timestamp}\n")
return filepath
def load_generated_images():
"""
μ €μž₯된 이미지λ₯Ό μ΅œμ‹ μˆœμœΌλ‘œ λΆˆλŸ¬μ˜¨λ‹€.
"""
if not os.path.exists(SAVE_DIR):
return []
image_files = [
os.path.join(SAVE_DIR, f)
for f in os.listdir(SAVE_DIR)
if f.endswith(('.png', '.jpg', '.jpeg', '.webp'))
]
image_files.sort(key=lambda x: os.path.getctime(x), reverse=True)
return image_files
@spaces.GPU(duration=120)
def inference(
prompt: str,
seed: int,
randomize_seed: bool,
width: int,
height: int,
guidance_scale: float,
num_inference_steps: int,
lora_scale: float,
progress: gr.Progress = gr.Progress(track_tqdm=True),
):
"""
Diffusion Pipeline을 μ‚¬μš©ν•΄ 이미지λ₯Ό 생성. (LoRA μŠ€μΌ€μΌ, Steps λ“± μ„€μ • κ°€λŠ₯)
"""
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator(device=device).manual_seed(seed)
try:
image = pipeline(
prompt=prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
joint_attention_kwargs={"scale": lora_scale},
).images[0]
filepath = save_generated_image(image, prompt)
return image, seed, load_generated_images()
except Exception as e:
logging.error(f"Error during inference: {e}")
error_img = Image.new('RGB', (width, height), color='red')
return error_img, seed, load_generated_images()
###############################################################################
# 6. Gradio UI
###############################################################################
examples = [
"Ghibli style futuristic stormtrooper with glossy white armor and a sleek helmet, standing heroically on a lush alien planet, vibrant flowers blooming around, soft sunlight illuminating the scene, a gentle breeze rustling the leaves. The armor reflects the pink and purple hues of the alien sunset, creating an ethereal glow around the figure. [trigger]",
"Ghibli style young mechanic girl in a floating workshop, surrounded by hovering tools and glowing mechanical parts, her blue overalls covered in oil stains, tinkering with a semi-transparent robot companion. Magical sparks fly as she works, while floating islands with waterfalls drift past her open workshop window. [trigger]",
"Ghibli style ancient forest guardian robot, covered in moss and flowering vines, sitting peacefully in a crystal-clear lake. Its gentle eyes glow with soft blue light, while bioluminescent dragonflies dance around its weathered metal frame. Ancient tech symbols on its surface pulse with a gentle rhythm. [trigger]",
"Ghibli style sky whale transport ship, its metallic skin adorned with traditional Japanese patterns, gliding through cotton candy clouds at sunrise. Small floating gardens hang from its sides, where workers in futuristic kimonos tend to glowing plants. Rainbow auroras shimmer in the background. [trigger]",
"Ghibli style cyber-shrine maiden with flowing holographic robes, performing a ritual dance among floating lanterns and digital cherry blossoms. Her traditional headdress emits soft light patterns, while spirit-like AI constructs swirl around her in elegant patterns. The scene is set in a modern shrine with both ancient wood and sleek chrome elements. [trigger]",
"Ghibli style robot farmer tending to floating rice paddies in the sky, wearing a traditional straw hat with advanced sensors. Its gentle movements create ripples in the water as it plants glowing rice seedlings. Flying fish leap between the terraced fields, leaving trails of sparkles in their wake, while future Tokyo's spires gleam in the distance. [trigger]"
]
css = """
:root {
--primary-color: #6a92cc;
--primary-hover: #557ab8;
--secondary-color: #f4c062;
--background-color: #f7f9fc;
--panel-background: #ffffff;
--text-color: #333333;
--border-radius: 12px;
--shadow: 0 4px 12px rgba(0,0,0,0.08);
--font-main: 'Poppins', -apple-system, BlinkMacSystemFont, sans-serif;
}
body {
background-color: var(--background-color);
font-family: var(--font-main);
}
.gradio-container {
margin: 0 auto;
max-width: 1200px !important;
}
.main-header {
text-align: center;
padding: 2rem 1rem 1rem;
background: linear-gradient(90deg, #6a92cc 0%, #8f7fc8 100%);
color: white;
margin-bottom: 2rem;
border-radius: var(--border-radius);
box-shadow: var(--shadow);
}
.main-header h1 {
font-size: 2.5rem;
margin-bottom: 0.5rem;
font-weight: 700;
text-shadow: 0 2px 4px rgba(0,0,0,0.2);
}
.main-header p {
font-size: 1rem;
margin-bottom: 0.5rem;
opacity: 0.9;
}
.main-header a {
color: var(--secondary-color);
text-decoration: none;
font-weight: 600;
transition: all 0.2s ease;
}
.main-header a:hover {
text-decoration: underline;
opacity: 0.9;
}
.container {
background-color: var(--panel-background);
padding: 1.5rem;
border-radius: var(--border-radius);
box-shadow: var(--shadow);
margin-bottom: 1.5rem;
}
button.primary {
background: var(--primary-color) !important;
border: none !important;
color: white !important;
padding: 10px 20px !important;
border-radius: 8px !important;
font-weight: 600 !important;
box-shadow: 0 2px 5px rgba(0,0,0,0.1) !important;
transition: all 0.2s ease !important;
}
button.primary:hover {
background: var(--primary-hover) !important;
transform: translateY(-2px) !important;
box-shadow: 0 4px 8px rgba(0,0,0,0.15) !important;
}
button.secondary {
background: white !important;
border: 1px solid #ddd !important;
color: var(--text-color) !important;
padding: 10px 20px !important;
border-radius: 8px !important;
font-weight: 500 !important;
box-shadow: 0 2px 5px rgba(0,0,0,0.05) !important;
transition: all 0.2s ease !important;
}
button.secondary:hover {
background: #f5f5f5 !important;
transform: translateY(-2px) !important;
}
.gr-box {
border-radius: var(--border-radius) !important;
border: 1px solid #e0e0e0 !important;
}
.gr-panel {
border-radius: var(--border-radius) !important;
}
.gr-input {
border-radius: 8px !important;
border: 1px solid #ddd !important;
padding: 12px !important;
}
.gr-form {
border-radius: var(--border-radius) !important;
background-color: var(--panel-background) !important;
}
.gr-accordion {
border-radius: var(--border-radius) !important;
overflow: hidden !important;
}
.gr-button {
border-radius: 8px !important;
}
.gallery-item {
border-radius: var(--border-radius) !important;
transition: all 0.3s ease !important;
}
.gallery-item:hover {
transform: scale(1.02) !important;
box-shadow: 0 6px 15px rgba(0,0,0,0.1) !important;
}
.tabs {
border-radius: var(--border-radius) !important;
overflow: hidden !important;
}
footer {
display: none !important;
}
.settings-accordion legend span {
font-weight: 600 !important;
}
.example-prompt {
font-size: 0.9rem;
color: #555;
padding: 8px;
background: #f5f7fa;
border-radius: 6px;
border-left: 3px solid var(--primary-color);
margin-bottom: 8px;
cursor: pointer;
transition: all 0.2s;
}
.example-prompt:hover {
background: #eef2f8;
}
.status-generating {
color: #ffa200;
font-weight: 500;
display: flex;
align-items: center;
gap: 8px;
}
.status-generating::before {
content: "";
display: inline-block;
width: 12px;
height: 12px;
border-radius: 50%;
background-color: #ffa200;
animation: pulse 1.5s infinite;
}
.status-complete {
color: #00c853;
font-weight: 500;
display: flex;
align-items: center;
gap: 8px;
}
.status-complete::before {
content: "";
display: inline-block;
width: 12px;
height: 12px;
border-radius: 50%;
background-color: #00c853;
}
@keyframes pulse {
0% { opacity: 0.6; }
50% { opacity: 1; }
100% { opacity: 0.6; }
}
.gr-accordion-title {
font-weight: 600 !important;
color: var(--text-color) !important;
}
.tabs button {
font-weight: 500 !important;
padding: 10px 16px !important;
}
.tabs button.selected {
font-weight: 600 !important;
color: var(--primary-color) !important;
background: rgba(106, 146, 204, 0.1) !important;
}
.gr-slider-container {
padding: 10px 0 !important;
}
.gr-prose h3 {
font-weight: 600 !important;
color: var(--primary-color) !important;
margin-bottom: 1rem !important;
}
"""
with gr.Blocks(css=css, analytics_enabled=False, theme="soft") as demo:
with gr.Column():
gr.HTML('''
<div class="main-header">
<h1>✨ FLUX Ghibli LoRA Generator ✨</h1>
<p>Community: <a href="https://discord.gg/openfreeai" target="_blank">https://discord.gg/openfreeai</a></p>
</div>
''')
with gr.Row():
with gr.Column(scale=3):
with gr.Group(elem_classes="container"):
prompt = gr.Textbox(
label="Enter your imagination",
placeholder="Describe your Ghibli-style image here...",
lines=3
)
# Text Rendering μž…λ ₯λž€
text_rendering = gr.Textbox(
label="Text Rendering (Multilingual: English, Korean...)",
placeholder="Man saying 'μ•ˆλ…•' in 'speech bubble'",
lines=1
)
with gr.Row():
run_button = gr.Button("✨ Generate Image", elem_classes="primary")
clear_button = gr.Button("Clear", elem_classes="secondary")
with gr.Accordion("Advanced Settings", open=False, elem_classes="settings-accordion"):
with gr.Row():
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=42,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row():
width = gr.Slider(
label="Width",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=768,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=10.0,
step=0.1,
value=3.5,
)
with gr.Row():
num_inference_steps = gr.Slider(
label="Steps",
minimum=1,
maximum=50,
step=1,
value=30,
)
lora_scale = gr.Slider(
label="LoRA scale",
minimum=0.0,
maximum=1.0,
step=0.1,
value=1.0,
)
with gr.Group(elem_classes="container"):
gr.Markdown("### ✨ Example Prompts")
examples_html = '\n'.join([f'<div class="example-prompt">{ex}</div>' for ex in examples])
example_container = gr.HTML(examples_html)
with gr.Column(scale=4):
with gr.Group(elem_classes="container"):
generation_status = gr.HTML('<div class="status-complete">Ready to generate</div>')
result = gr.Image(label="Generated Image", elem_id="result-image")
seed_text = gr.Number(label="Used Seed", value=42)
with gr.Tabs(elem_classes="tabs") as tabs:
with gr.TabItem("Gallery"):
with gr.Group(elem_classes="container"):
gallery_header = gr.Markdown("### πŸ–ΌοΈ Your Generated Masterpieces")
with gr.Row():
refresh_btn = gr.Button("πŸ”„ Refresh Gallery", elem_classes="secondary")
generated_gallery = gr.Gallery(
label="Generated Images",
columns=3,
value=load_generated_images(),
height="500px",
elem_classes="gallery-item"
)
###########################################################################
# Gradio Helper Functions
###########################################################################
def refresh_gallery():
return load_generated_images()
def clear_output():
return "", gr.update(value=None), seed, '<div class="status-complete">Ready to generate</div>'
def before_generate():
return '<div class="status-generating">Generating image...</div>'
def after_generate(image, seed_num, gallery):
return image, seed_num, gallery, '<div class="status-complete">Generation complete!</div>'
###########################################################################
# Gradio Event Wiring
###########################################################################
refresh_btn.click(
fn=refresh_gallery,
inputs=None,
outputs=generated_gallery,
)
clear_button.click(
fn=clear_output,
inputs=None,
outputs=[prompt, result, seed_text, generation_status]
)
# 1) μƒνƒœ ν‘œμ‹œ
# 2) 이미지 생성
# 3) μƒνƒœ μ—…λ°μ΄νŠΈ
# 4) ν…μŠ€νŠΈ λ Œλ”λ§(μžˆλ‹€λ©΄)
run_button.click(
fn=before_generate,
inputs=None,
outputs=generation_status,
).then(
fn=inference,
inputs=[
prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
lora_scale,
],
outputs=[result, seed_text, generated_gallery],
).then(
fn=after_generate,
inputs=[result, seed_text, generated_gallery],
outputs=[result, seed_text, generated_gallery, generation_status],
).then(
fn=apply_text_rendering,
inputs=[result, text_rendering],
outputs=result
)
# prompt submit μ‹œμ—λ„ λ™μΌν•œ 체인 μ‹€ν–‰
prompt.submit(
fn=before_generate,
inputs=None,
outputs=generation_status,
).then(
fn=inference,
inputs=[
prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
lora_scale,
],
outputs=[result, seed_text, generated_gallery],
).then(
fn=after_generate,
inputs=[result, seed_text, generated_gallery],
outputs=[result, seed_text, generated_gallery, generation_status],
).then(
fn=apply_text_rendering,
inputs=[result, text_rendering],
outputs=result
)
# JS둜 μ˜ˆμ‹œ prompt 클릭 μ‹œ μžλ™ μ±„μš°κΈ°
gr.HTML("""
<script>
document.addEventListener('DOMContentLoaded', function() {
setTimeout(() => {
const examples = document.querySelectorAll('.example-prompt');
const promptInput = document.querySelector('textarea');
examples.forEach(example => {
example.addEventListener('click', function() {
promptInput.value = this.textContent.trim();
const event = new Event('input', { bubbles: true });
promptInput.dispatchEvent(event);
});
});
}, 1000);
});
</script>
""")
###############################################################################
# 7. μ‹€ν–‰
###############################################################################
try:
demo.queue(concurrency_count=1, max_size=20)
demo.launch(debug=True, show_api=False)
except Exception as e:
logging.error(f"Error during launch: {e}")
logging.info("Trying alternative launch configuration...")
demo.launch(debug=True, show_api=False, share=False)