Update app.py

app.py

@@ -1,546 +1,333 @@
 import gradio as gr
-import numpy as np
-import random
-import torch
-import spaces
-from PIL import Image
-from diffusers import QwenImageEditPipeline
-from diffusers.utils import is_xformers_available
+import replicate
 import os
+from PIL import Image
+import requests
+from io import BytesIO
+import time
+import tempfile
 import base64
-import json
-from huggingface_hub import InferenceClient
-import logging
 
-#############################
-os.environ.setdefault('GRADIO_ANALYTICS_ENABLED', 'False')
-os.environ.setdefault('HF_HUB_DISABLE_TELEMETRY', '1')
-logging.basicConfig(level=logging.DEBUG)
-logger = logging.getLogger(__name__)
-#############################
+# Set up Replicate API key from environment variable
+os.environ['REPLICATE_API_TOKEN'] = os.getenv('REPLICATE_API_TOKEN')
 
-def get_caption_language(prompt):
-    """Detects if the prompt contains Chinese characters."""
-    ranges = [
-        ('\u4e00', '\u9fff'),  # CJK Unified Ideographs
-    ]
-    for char in prompt:
-        if any(start <= char <= end for start, end in ranges):
-            return 'zh'
-    return 'en'
-
-def polish_prompt(original_prompt, system_prompt, hf_token):
+def upload_image_to_hosting(image):
     """
-    Rewrites the prompt using a Hugging Face InferenceClient.
-    Requires user-provided HF token for API access.
+    Upload image to multiple hosting services with fallback
     """
-    if not hf_token or not hf_token.strip():
-        gr.Warning("HF Token is required for prompt rewriting but was not provided!")
-        return original_prompt
-    client = InferenceClient(
-        provider="cerebras",
-        api_key=hf_token,
-    )
-    messages = [
-        {"role": "system", "content": system_prompt},
-        {"role": "user", "content": original_prompt}
-    ]
+    # Method 1: Try imgbb.com (most reliable)
     try:
-        completion = client.chat.completions.create(
-            model="Qwen/Qwen3-235B-A22B-Instruct-2507",
-            messages=messages,
-            max_tokens=512,
+        buffered = BytesIO()
+        image.save(buffered, format="PNG")
+        buffered.seek(0)
+        img_base64 = base64.b64encode(buffered.getvalue()).decode()
+        
+        response = requests.post(
+            "https://api.imgbb.com/1/upload",
+            data={
+                'key': '6d207e02198a847aa98d0a2a901485a5',
+                'image': img_base64,
+            }
         )
-        polished_prompt = completion.choices[0].message.content
-        polished_prompt = polished_prompt.strip().replace("\n", " ")
-        return polished_prompt
-    except Exception as e:
-        print(f"Error during Hugging Face API call: {e}")
-        gr.Warning("Failed to rewrite prompt. Using original.")
-        return original_prompt
-
-SYSTEM_PROMPT_EDIT = '''
-# Edit Instruction Rewriter
-You are a professional edit instruction rewriter. Your task is to generate a precise, concise, and visually achievable instruction based on the user's intent and the input image.
-## 1. General Principles
-- Keep the rewritten instruction **concise** and clear.
-- Avoid contradictions, vagueness, or unachievable instructions.
-- Maintain the core logic of the original instruction; only enhance clarity and feasibility.
-- Ensure new added elements or modifications align with the image's original context and art style.
-## 2. Task Types
-### Add, Delete, Replace:
-- When the input is detailed, only refine grammar and clarity.
-- For vague instructions, infer minimal but sufficient details.
-- For replacement, use the format: `"Replace X with Y"`.
-### Text Editing (e.g., text replacement):
-- Enclose text content in quotes, e.g., `Replace "abc" with "xyz"`.
-- Preserving the original structure and language—**do not translate** or alter style.
-### Human Editing (e.g., change a person's face/hair):
-- Preserve core visual identity (gender, ethnic features).
-- Describe expressions in subtle and natural terms.
-- Maintain key clothing or styling details unless explicitly replaced.
-### Style Transformation:
-- If a style is specified, e.g., `Disco style`, rewrite it to encapsulate the essential visual traits.
-- Use a fixed template for **coloring/restoration**:  
-  `"Restore old photograph, remove scratches, reduce noise, enhance details, high resolution, realistic, natural skin tones, clear facial features, no distortion, vintage photo restoration"`  
-  if applicable.
-## 4. Output Format
-Please provide the rewritten instruction in a clean `json` format as:
-{
-  "Rewritten": "..."
-}
-'''
-
-dtype = torch.bfloat16
-device = "cuda" if torch.cuda.is_available() else "cpu"
-pipe = QwenImageEditPipeline.from_pretrained("Qwen/Qwen-Image-Edit", torch_dtype=dtype).to(device)
-
-# Load LoRA weights for acceleration
-pipe.load_lora_weights(
-    "lightx2v/Qwen-Image-Lightning", weight_name="Qwen-Image-Lightning-8steps-V1.1.safetensors"
-)
-pipe.fuse_lora()
-
-if is_xformers_available():
-    pipe.enable_xformers_memory_efficient_attention()
-else:
-    print("xformers not available or failed to load.")
-
-@spaces.GPU(duration=60)
-def infer(
-    image,
-    prompt,
-    seed=42,
-    randomize_seed=False,
-    true_guidance_scale=1.0,
-    num_inference_steps=8,
-    rewrite_prompt=False,
-    hf_token="",
-    num_images_per_prompt=1,
-    progress=gr.Progress(track_tqdm=True),
-):
-    """
-    Requires user-provided HF token for prompt rewriting.
-    """
-    original_prompt = prompt  # Save original prompt for display
-    negative_prompt = " "
-    prompt_info = ""  # Initialize info text
+        
+        if response.status_code == 200:
+            data = response.json()
+            if data.get('success'):
+                return data['data']['url']
+    except:
+        pass
     
-    # Handle prompt rewriting with status messages
-    if rewrite_prompt:
-        if not hf_token.strip():
-            gr.Warning("HF Token is required for prompt rewriting but was not provided!")
-            prompt_info = f"""<div class="prompt-info-box warning">
-<h3>⚠️ Prompt Rewriting Skipped</h3>
-<p><strong>Original:</strong> {original_prompt}</p>
-<p class="note">HF Token required for enhancement</p>
-</div>"""
-            rewritten_prompt = original_prompt
-        else:
-            try:
-                rewritten_prompt = polish_prompt(original_prompt, SYSTEM_PROMPT_EDIT, hf_token)
-                prompt_info = f"""<div class="prompt-info-box success">
-<h3>✨ Enhanced Successfully</h3>
-<p><strong>Original:</strong> {original_prompt}</p>
-<p><strong>Enhanced:</strong> {rewritten_prompt}</p>
-</div>"""
-            except Exception as e:
-                gr.Warning(f"Prompt rewriting failed: {str(e)}")
-                rewritten_prompt = original_prompt
-                prompt_info = f"""<div class="prompt-info-box error">
-<h3>❌ Enhancement Failed</h3>
-<p><strong>Original:</strong> {original_prompt}</p>
-<p class="note">Error: {str(e)}</p>
-</div>"""
-    else:
-        rewritten_prompt = original_prompt
-        prompt_info = f"""<div class="prompt-info-box default">
-<h3>📝 Original Prompt</h3>
-<p>{original_prompt}</p>
-</div>"""
+    # Method 2: Try 0x0.st (simple and reliable)
+    try:
+        buffered = BytesIO()
+        image.save(buffered, format="PNG")
+        buffered.seek(0)
+        
+        files = {'file': ('image.png', buffered, 'image/png')}
+        response = requests.post("https://0x0.st", files=files)
+        
+        if response.status_code == 200:
+            return response.text.strip()
+    except:
+        pass
     
-    # Generate images
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    generator = torch.Generator(device=device).manual_seed(seed)
+    # Method 3: Fallback to base64
+    buffered = BytesIO()
+    image.save(buffered, format="PNG")
+    buffered.seek(0)
+    img_base64 = base64.b64encode(buffered.getvalue()).decode()
+    return f"data:image/png;base64,{img_base64}"
+
+def process_images(prompt, image1, image2=None):
+    """
+    Process uploaded images with Replicate API
+    """
+    if not image1:
+        return None, "Please upload at least one image"
     
-    edited_images = pipe(
-        image,
-        prompt=rewritten_prompt,
-        negative_prompt=negative_prompt,
-        num_inference_steps=num_inference_steps,
-        generator=generator,
-        true_cfg_scale=true_guidance_scale,
-        num_images_per_prompt=num_images_per_prompt,
-    ).images
+    if not os.getenv('REPLICATE_API_TOKEN'):
+        return None, "Please set REPLICATE_API_TOKEN"
     
-    return edited_images, seed, prompt_info
-
-MAX_SEED = np.iinfo(np.int32).max
-
-examples = [
-    "Replace the cat with a friendly golden retriever. Make it look happier, and add more background details.",
-    "Add text 'Qwen - AI for image editing' in Chinese at the bottom center with a small shadow.",
-    "Change the style to 1970s vintage, add old photo effect, restore any scratches on the wall or window.",
-    "Remove the blue sky and replace it with a dark night cityscape.",
-    """Replace "Qwen" with "通义" in the Image. Ensure Chinese font is used for "通义" and position it to the top left with a light heading-style font."""
-]
+    try:
+        image_urls = []
+        
+        # Upload images
+        url1 = upload_image_to_hosting(image1)
+        image_urls.append(url1)
+        
+        if image2:
+            url2 = upload_image_to_hosting(image2)
+            image_urls.append(url2)
+        
+        # Run the model
+        output = replicate.run(
+            "google/nano-banana",
+            input={
+                "prompt": prompt,
+                "image_input": image_urls
+            }
+        )
+        
+        if output is None:
+            return None, "No output received"
+        
+        # Get the generated image
+        try:
+            if hasattr(output, 'read'):
+                img_data = output.read()
+                img = Image.open(BytesIO(img_data))
+                return img, "✨ Generated successfully!"
+        except:
+            pass
+        
+        try:
+            if hasattr(output, 'url'):
+                output_url = output.url()
+                response = requests.get(output_url, timeout=30)
+                if response.status_code == 200:
+                    img = Image.open(BytesIO(response.content))
+                    return img, "✨ Generated successfully!"
+        except:
+            pass
+        
+        output_url = None
+        if isinstance(output, str):
+            output_url = output
+        elif isinstance(output, list) and len(output) > 0:
+            output_url = output[0]
+        
+        if output_url:
+            response = requests.get(output_url, timeout=30)
+            if response.status_code == 200:
+                img = Image.open(BytesIO(response.content))
+                return img, "✨ Generated successfully!"
+        
+        return None, "Could not process output"
+        
+    except Exception as e:
+        return None, f"Error: {str(e)[:100]}"
 
-# Custom CSS for enhanced visual design
-custom_css = """
-/* Gradient background */
+# Enhanced CSS with modern, minimal design
+css = """
 .gradio-container {
-    background: linear-gradient(135deg, #667eea 0%, #764ba2 25%, #f093fb 50%, #fecfef 75%, #fecfef 100%);
+    background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
+    font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif;
     min-height: 100vh;
 }
-
-/* Main container styling */
-.container {
-    max-width: 1400px !important;
-    margin: 0 auto !important;
-    padding: 2rem !important;
+.header-container {
+    background: linear-gradient(135deg, #ffd93d 0%, #ffb347 100%);
+    padding: 2.5rem;
+    border-radius: 24px;
+    margin-bottom: 2.5rem;
+    box-shadow: 0 20px 60px rgba(255, 179, 71, 0.25);
 }
-
-/* Card-like sections */
-.gr-box {
-    background: rgba(255, 255, 255, 0.95) !important;
-    backdrop-filter: blur(10px) !important;
-    border-radius: 20px !important;
-    box-shadow: 0 20px 40px rgba(0, 0, 0, 0.1) !important;
-    border: 1px solid rgba(255, 255, 255, 0.5) !important;
-    padding: 1.5rem !important;
-    margin-bottom: 1.5rem !important;
+.logo-text {
+    font-size: 3.5rem;
+    font-weight: 900;
+    color: #2d3436;
+    text-align: center;
+    margin: 0;
+    letter-spacing: -2px;
 }
-
-/* Header styling */
-h1 {
-    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-    -webkit-background-clip: text;
-    -webkit-text-fill-color: transparent;
-    background-clip: text;
-    font-size: 3rem !important;
-    font-weight: 800 !important;
+.subtitle {
+    color: #2d3436;
     text-align: center;
-    margin-bottom: 0.5rem !important;
-    text-shadow: 2px 2px 4px rgba(0,0,0,0.1);
+    font-size: 1rem;
+    margin-top: 0.5rem;
+    opacity: 0.8;
 }
-
-h2 {
-    color: #4a5568 !important;
-    font-size: 1.5rem !important;
-    font-weight: 600 !important;
-    margin-bottom: 1rem !important;
+.main-content {
+    background: rgba(255, 255, 255, 0.95);
+    backdrop-filter: blur(20px);
+    border-radius: 24px;
+    padding: 2.5rem;
+    box-shadow: 0 10px 40px rgba(0, 0, 0, 0.08);
 }
-
-/* Button styling */
 .gr-button-primary {
-    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
+    background: linear-gradient(135deg, #ffd93d 0%, #ffb347 100%) !important;
     border: none !important;
-    color: white !important;
-    font-weight: 600 !important;
+    color: #2d3436 !important;
+    font-weight: 700 !important;
     font-size: 1.1rem !important;
-    padding: 0.8rem 2rem !important;
-    border-radius: 12px !important;
-    box-shadow: 0 4px 15px rgba(102, 126, 234, 0.4) !important;
-    transition: all 0.3s ease !important;
+    padding: 1.2rem 2rem !important;
+    border-radius: 14px !important;
+    transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1) !important;
+    text-transform: uppercase;
+    letter-spacing: 1px;
+    width: 100%;
+    margin-top: 1rem !important;
 }
-
 .gr-button-primary:hover {
-    transform: translateY(-2px) !important;
-    box-shadow: 0 6px 20px rgba(102, 126, 234, 0.5) !important;
-}
-
-/* Input fields styling */
-.gr-input, .gr-text-input, .gr-slider, .gr-dropdown {
-    border-radius: 10px !important;
-    border: 2px solid #e2e8f0 !important;
-    background: white !important;
-    transition: all 0.3s ease !important;
+    transform: translateY(-3px) !important;
+    box-shadow: 0 15px 40px rgba(255, 179, 71, 0.35) !important;
 }
-
-.gr-input:focus, .gr-text-input:focus {
-    border-color: #667eea !important;
-    box-shadow: 0 0 0 3px rgba(102, 126, 234, 0.1) !important;
+.gr-input, .gr-textarea {
+    background: #ffffff !important;
+    border: 2px solid #e1e8ed !important;
+    border-radius: 14px !important;
+    color: #2d3436 !important;
+    font-size: 1rem !important;
+    padding: 0.8rem 1rem !important;
 }
-
-/* Accordion styling */
-.gr-accordion {
-    background: rgba(255, 255, 255, 0.8) !important;
-    border-radius: 12px !important;
-    border: 1px solid rgba(102, 126, 234, 0.2) !important;
-    overflow: hidden !important;
+.gr-input:focus, .gr-textarea:focus {
+    border-color: #ffd93d !important;
+    box-shadow: 0 0 0 4px rgba(255, 217, 61, 0.15) !important;
 }
-
-/* Gallery styling */
-.gr-gallery {
-    border-radius: 12px !important;
-    overflow: hidden !important;
-}
-
-/* Prompt info boxes */
-.prompt-info-box {
-    padding: 1.5rem;
-    border-radius: 12px;
-    margin: 1rem 0;
-    animation: fadeIn 0.5s ease;
-}
-
-.prompt-info-box h3 {
-    margin: 0 0 0.75rem 0;
-    font-size: 1.2rem;
-    font-weight: 600;
-}
-
-.prompt-info-box p {
-    margin: 0.5rem 0;
-    line-height: 1.6;
-}
-
-.prompt-info-box.success {
-    background: linear-gradient(135deg, #d4f4dd 0%, #e3f9e5 100%);
-    border-left: 4px solid #48bb78;
+.gr-form {
+    background: transparent !important;
+    border: none !important;
 }
-
-.prompt-info-box.warning {
-    background: linear-gradient(135deg, #fef5e7 0%, #fff9ec 100%);
-    border-left: 4px solid #f6ad55;
+.gr-panel {
+    background: #ffffff !important;
+    border: 2px solid #e1e8ed !important;
+    border-radius: 16px !important;
+    padding: 1.5rem !important;
 }
-
-.prompt-info-box.error {
-    background: linear-gradient(135deg, #fed7d7 0%, #fee5e5 100%);
-    border-left: 4px solid #fc8181;
+.gr-box {
+    border-radius: 14px !important;
+    border-color: #e1e8ed !important;
 }
-
-.prompt-info-box.default {
-    background: linear-gradient(135deg, #e6f3ff 0%, #f0f7ff 100%);
-    border-left: 4px solid #667eea;
+label {
+    color: #636e72 !important;
+    font-weight: 600 !important;
+    font-size: 0.85rem !important;
+    text-transform: uppercase;
+    letter-spacing: 0.5px;
+    margin-bottom: 0.5rem !important;
 }
-
-.prompt-info-box .note {
+.status-text {
+    font-family: 'SF Mono', 'Monaco', monospace;
+    color: #00b894;
     font-size: 0.9rem;
-    color: #718096;
-    font-style: italic;
-}
-
-/* Checkbox styling */
-.gr-checkbox {
-    background: white !important;
-    border-radius: 8px !important;
-    padding: 0.5rem !important;
-}
-
-/* Token input field */
-input[type="password"] {
-    font-family: monospace !important;
-    letter-spacing: 0.05em !important;
-}
-
-/* Info badges */
-.gr-markdown p {
-    color: #4a5568;
-    line-height: 1.6;
-}
-
-.gr-markdown a {
-    color: #667eea !important;
-    text-decoration: none !important;
-    font-weight: 500 !important;
-    transition: color 0.3s ease !important;
-}
-
-.gr-markdown a:hover {
-    color: #764ba2 !important;
-    text-decoration: underline !important;
 }
-
-/* Animation */
-@keyframes fadeIn {
-    from {
-        opacity: 0;
-        transform: translateY(10px);
-    }
-    to {
-        opacity: 1;
-        transform: translateY(0);
-    }
+.image-container {
+    border-radius: 14px !important;
+    overflow: hidden;
+    border: 2px solid #e1e8ed !important;
+    background: #fafbfc !important;
 }
-
-/* Slider styling */
-.gr-slider input[type="range"] {
-    background: linear-gradient(90deg, #667eea 0%, #764ba2 100%) !important;
+footer {
+    display: none !important;
 }
-
-/* Group styling */
-.gr-group {
-    background: rgba(249, 250, 251, 0.8) !important;
-    border-radius: 12px !important;
-    padding: 1rem !important;
-    margin-top: 1rem !important;
+/* Equal sizing for all image containers */
+.image-upload {
+    min-height: 200px !important;
+    max-height: 200px !important;
 }
-
-/* Loading spinner customization */
-.gr-loading {
-    color: #667eea !important;
+.output-image {
+    min-height: 420px !important;
+    max-height: 420px !important;
 }
-
-/* Example buttons */
-.gr-examples button {
-    background: white !important;
-    border: 2px solid #e2e8f0 !important;
-    border-radius: 8px !important;
-    padding: 0.5rem 1rem !important;
-    transition: all 0.3s ease !important;
+/* Ensure consistent spacing */
+.gr-row {
+    gap: 1rem !important;
 }
-
-.gr-examples button:hover {
-    border-color: #667eea !important;
-    background: rgba(102, 126, 234, 0.05) !important;
+.gr-column {
+    gap: 1rem !important;
 }
 """
 
-
-with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🎨 Nano-Banana")
-    gr.Markdown("✨ **Ultra-fast 8-step image editing with AI-powered prompt enhancement**")
-    gr.Markdown("🔐 **Secure prompt rewriting with your [Hugging Face token](https://huggingface.co/settings/tokens)**")
-    
-    # 배지를 가운데 정렬하여 나란히 배치
-    gr.HTML("""
-        <div style="display: flex; justify-content: center; align-items: center; gap: 20px; margin: 20px 0;">
-            <a href="https://huggingface.co/spaces/Heartsync/Nano-Banana" target="_blank">
-                <img src="https://img.shields.io/static/v1?label=OPEN%20NANO-BANANA&message=Image%20EDITOR&color=%230000ff&labelColor=%23800080&logo=huggingface&logoColor=white&style=for-the-badge" alt="badge">
-            </a>
-
-        </div>
-    """)
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            with gr.Group():
-                input_image = gr.Image(
-                    label="📸 Input Image", 
-                    type="pil",
-                    elem_classes="gr-box"
-                )
-                prompt = gr.Text(
-                    label="✏️ Edit Instruction", 
-                    placeholder="e.g. Add a dog to the right side, change the sky to sunset...",
+with gr.Blocks(css=css, theme=gr.themes.Base()) as demo:
+    with gr.Column(elem_classes="header-container"):
+        gr.HTML("""
+            <h1 class="logo-text">🍌 Open Nano Banana</h1>
+            <p class="subtitle">AI-Powered Image Style Transfer</p>
+            <div style="display: flex; justify-content: center; align-items: center; gap: 10px; margin-top: 20px;">
+                <a href="https://huggingface.co/spaces/openfree/Nano-Banana-Upscale" target="_blank">
+                    <img src="https://img.shields.io/static/v1?label=NANO%20BANANA&message=UPSCALE&color=%230000ff&labelColor=%23800080&logo=GOOGLE&logoColor=white&style=for-the-badge" alt="Nano Banana Upscale">
+                </a>
+                <a href="https://huggingface.co/spaces/openfree/Free-Nano-Banana" target="_blank">
+                    <img src="https://img.shields.io/static/v1?label=NANO%20BANANA&message=FREE&color=%230000ff&labelColor=%23800080&logo=GOOGLE&logoColor=white&style=for-the-badge" alt="Free Nano Banana">
+                </a>
+                <a href="https://huggingface.co/spaces/aiqtech/Nano-Banana-API" target="_blank">
+                    <img src="https://img.shields.io/static/v1?label=NANO%20BANANA&message=API&color=%230000ff&labelColor=%23800080&logo=GOOGLE&logoColor=white&style=for-the-badge" alt="Nano Banana API">
+                </a>
+                <a href="https://discord.gg/openfreeai" target="_blank">
+                    <img src="https://img.shields.io/static/v1?label=Discord&message=Openfree%20AI&color=%230000ff&labelColor=%23800080&logo=discord&logoColor=white&style=for-the-badge" alt="Discord Openfree AI">
+                </a>
+            </div>
+        """)
+
+    with gr.Column(elem_classes="main-content"):
+        with gr.Row(equal_height=True):
+            # Left Column - Inputs
+            with gr.Column(scale=1):
+                prompt = gr.Textbox(
+                    label="Style Description",
+                    placeholder="Describe your style...",
                     lines=3,
-                    elem_classes="gr-box"
+                    value="Make the sheets in the style of the logo. Make the scene natural.",
+                    elem_classes="prompt-input"
                 )
                 
-                with gr.Accordion("⚙️ Advanced Settings", open=False):
-                    seed = gr.Slider(
-                        label="Seed",
-                        minimum=0,
-                        maximum=MAX_SEED,
-                        step=1,
-                        value=0
+                with gr.Row(equal_height=True):
+                    image1 = gr.Image(
+                        label="Primary Image",
+                        type="pil",
+                        height=200,
+                        elem_classes="image-container image-upload"
                     )
-                    randomize_seed = gr.Checkbox(label="🎲 Randomize Seed", value=True)
-                    
-                    with gr.Row():
-                        true_guidance_scale = gr.Slider(
-                            label="Guidance Scale",
-                            minimum=1.0,
-                            maximum=5.0,
-                            step=0.1,
-                            value=4.0
-                        )
-                        num_inference_steps = gr.Slider(
-                            label="Inference Steps",
-                            minimum=4,
-                            maximum=16,
-                            step=1,
-                            value=8
-                        )
-                    
-                    num_images_per_prompt = gr.Slider(
-                        label="Images per Prompt",
-                        minimum=1,
-                        maximum=4,
-                        step=1,
-                        value=1
+                    image2 = gr.Image(
+                        label="Secondary Image",
+                        type="pil",
+                        height=200,
+                        elem_classes="image-container image-upload"
                     )
                 
-                run_button = gr.Button("🚀 Generate Edit", variant="primary", size="lg")
-        
-        with gr.Column(scale=1):
-            result = gr.Gallery(
-                label="🖼️ Output Images", 
-                show_label=True, 
-                columns=2,
-                rows=2,
-                elem_classes="gr-box"
-            )
-            
-            # Prompt display component
-            prompt_info = gr.HTML(visible=False)
-            
-            with gr.Group():
-                rewrite_toggle = gr.Checkbox(
-                    label="🤖 Enable AI Prompt Enhancement", 
-                    value=False, 
-                    interactive=True
+                generate_btn = gr.Button(
+                    "Generate Magic ✨",
+                    variant="primary",
+                    size="lg"
                 )
-                hf_token_input = gr.Textbox(
-                    label="🔑 Hugging Face API Token",
-                    type="password",
-                    placeholder="hf_xxxxxxxxxxxxxxxx",
-                    visible=False,
-                    info="Your token is secure and only used for API calls. Get yours from HuggingFace settings.",
-                    elem_classes="gr-box"
+            
+            # Right Column - Output
+            with gr.Column(scale=1):
+                output_image = gr.Image(
+                    label="Generated Result",
+                    type="pil",
+                    height=420,
+                    elem_classes="image-container output-image"
                 )
                 
-                def toggle_token_visibility(checked):
-                    return gr.update(visible=checked)
-                
-                rewrite_toggle.change(
-                    toggle_token_visibility,
-                    inputs=[rewrite_toggle],
-                    outputs=[hf_token_input]
+                status = gr.Textbox(
+                    label="Status",
+                    interactive=False,
+                    lines=1,
+                    elem_classes="status-text",
+                    value="Ready to generate..."
                 )
     
-    # Examples section
-    gr.Examples(
-        examples=examples,
-        inputs=prompt,
-        label="💡 Example Prompts"
-    )
-    
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            input_image,
-            prompt,
-            seed,
-            randomize_seed,
-            true_guidance_scale,
-            num_inference_steps,
-            rewrite_toggle,
-            hf_token_input,
-            num_images_per_prompt
-        ],
-        outputs=[result, seed, prompt_info]
-    )
-    
-    # Show prompt info box after processing
-    def set_prompt_visible():
-        return gr.update(visible=True)
-    
-    run_button.click(
-        fn=set_prompt_visible,
-        inputs=None,
-        outputs=[prompt_info],
-        queue=False
-    )
-    prompt.submit(
-        fn=set_prompt_visible,
-        inputs=None,
-        outputs=[prompt_info],
-        queue=False
+    # Event handler
+    generate_btn.click(
+        fn=process_images,
+        inputs=[prompt, image1, image2],
+        outputs=[output_image, status]
     )
 
+# Launch
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(
+        share=True,
+        server_name="0.0.0.0",
+        server_port=7860
+    )