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-import gradio as gr
-import replicate
-import os
-from typing import Optional, List
-from huggingface_hub import whoami
-from PIL import Image
-import requests
-from io import BytesIO
-import tempfile
-import base64
-# --- Replicate API Configuration ---
-REPLICATE_API_TOKEN = os.getenv("REPLICATE_API_TOKEN")
-if not REPLICATE_API_TOKEN:
-    raise ValueError("REPLICATE_API_TOKEN environment variable is not set.")
-# Initialize Replicate client
-os.environ["REPLICATE_API_TOKEN"] = REPLICATE_API_TOKEN
-def verify_login_status(token: Optional[gr.OAuthToken]) -> bool:
-    """Verifies if the user is logged in to Hugging Face."""
-    if not token:
-        return False
-    try:
-        user_info = whoami(token=token.token)
-        return True if user_info else False
-    except Exception as e:
-        print(f"Could not verify user's login status: {e}")
-        return False
-def upload_image_to_hosting(image_path: str) -> str:
-    """
-    Upload image to hosting service and return URL.
-    Using multiple fallback methods for reliability.
-    """
-    # Open the image
-    img = Image.open(image_path)
-    # Method 1: Try imgbb.com (most reliable)
-    try:
-        buffered = BytesIO()
-        img.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',  # Free API key
-                'image': img_base64,
-            }
-        )
-        if response.status_code == 200:
-            data = response.json()
-            if data.get('success'):
-                return data['data']['url']
-    except Exception as e:
-        print(f"imgbb upload failed: {e}")
-    # Method 2: Try 0x0.st (simple and reliable)
-    try:
-        buffered = BytesIO()
-        img.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:
-            url = response.text.strip()
-            if url.startswith('http'):
-                return url
-    except Exception as e:
-        print(f"0x0.st upload failed: {e}")
-    # Method 3: Fallback to data URI (last resort)
-    buffered = BytesIO()
-    img.save(buffered, format="PNG")
-    buffered.seek(0)
-    img_base64 = base64.b64encode(buffered.getvalue()).decode()
-    return f"data:image/png;base64,{img_base64}"
-def image_to_data_uri(image_path: str) -> str:
-    """Convert local image file to data URI format (kept for backwards compatibility)."""
-    with open(image_path, "rb") as img_file:
-        img_data = img_file.read()
-        img_base64 = base64.b64encode(img_data).decode('utf-8')
-    # Get the image format
-    img = Image.open(image_path)
-    img_format = img.format.lower() if img.format else 'png'
-    # Create data URI
-    data_uri = f"data:image/{img_format};base64,{img_base64}"
-    return data_uri
-def process_output(output, progress=gr.Progress()) -> str:
-    """Process the output from Replicate API and return a local file path."""
-    try:
-        # Check if output has a url attribute (FileObject)
-        if hasattr(output, 'url'):
-            # If url is a method, call it; if it's a property, just access it
-            image_url = output.url() if callable(output.url) else output.url
-        # If output is already a string URL
-        elif isinstance(output, str):
-            image_url = output
-        # If output is a list of URLs
-        elif isinstance(output, list) and len(output) > 0:
-            # Check first item in list
-            first_item = output[0]
-            if hasattr(first_item, 'url'):
-                image_url = first_item.url() if callable(first_item.url) else first_item.url
-            else:
-                image_url = first_item
-        else:
-            raise ValueError(f"Unexpected output format from Replicate: {type(output)}")
-        # Download the image from URL
-        response = requests.get(image_url)
-        response.raise_for_status()
-        # Save to temporary file
-        img = Image.open(BytesIO(response.content))
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as tmpfile:
-            img.save(tmpfile.name)
-            progress(1.0, desc="✅ Complete!")
-            return tmpfile.name
-    except Exception as e:
-        print(f"Error processing output: {e}")
-        raise ValueError(f"Failed to process output: {str(e)}")
-def run_single_image_logic(prompt: str, image_path: Optional[str] = None, progress=gr.Progress()) -> str:
-    """Handles text-to-image or single image-to-image using Replicate's Nano Banana."""
-    try:
-        progress(0.2, desc="🎨 Preparing...")
-        # Prepare input for Replicate API
-        input_data = {
-            "prompt": prompt
-        }
-        # If there's an input image, upload it to get a proper URL
-        if image_path:
-            progress(0.3, desc="📤 Uploading image...")
-            # Upload to hosting service for proper URL
-            image_url = upload_image_to_hosting(image_path)
-            if image_url.startswith('http'):
-                print(f"Image uploaded successfully: {image_url[:50]}...")
-            else:
-                print("Using data URI fallback")
-            input_data["image_input"] = [image_url]
-        progress(0.5, desc="✨ Generating...")
-        # Run the model on Replicate
-        output = replicate.run(
-            "google/nano-banana",
-            input=input_data
-        )
-        progress(0.8, desc="🖼️ Finalizing...")
-        # Handle the output - output is already a URL string or FileObject
-        if output:
-            return process_output(output, progress)
-        else:
-            raise ValueError("No output received from Replicate API")
-    except Exception as e:
-        print(f"Error details: {e}")
-        print(f"Error type: {type(e)}")
-        if 'output' in locals():
-            print(f"Output value: {output}")
-            print(f"Output type: {type(output)}")
-        raise gr.Error(f"Image generation failed: {str(e)[:200]}")
-def run_multi_image_logic(prompt: str, images: List[str], progress=gr.Progress()) -> str:
-    """
-    Handles multi-image editing by sending a list of images and a prompt.
-    """
-    if not images:
-        raise gr.Error("Please upload at least one image in the 'Multiple Images' tab.")
-    try:
-        progress(0.2, desc="🎨 Preparing images...")
-        # Upload all images to get proper URLs
-        image_urls = []
-        for idx, image_path in enumerate(images):
-            if isinstance(image_path, (list, tuple)):
-                image_path = image_path[0]
-            progress(0.2 + (0.2 * idx / len(images)), desc=f"📤 Uploading image {idx+1}/{len(images)}...")
-            image_url = upload_image_to_hosting(image_path)
-            image_urls.append(image_url)
-        # Prepare input for Replicate API with multiple images
-        input_data = {
-            "prompt": prompt,
-            "image_input": image_urls
-        }
-        progress(0.5, desc="✨ Generating...")
-        # Run the model on Replicate
-        output = replicate.run(
-            "google/nano-banana",
-            input=input_data
-        )
-        progress(0.8, desc="🖼️ Finalizing...")
-        # Handle the output using the process_output function
-        if output:
-            return process_output(output, progress)
-        else:
-            raise ValueError("No output received from Replicate API")
-    except Exception as e:
-        print(f"Multi-image error details: {e}")
-        print(f"Output value: {output if 'output' in locals() else 'Not set'}")
-        print(f"Output type: {type(output) if 'output' in locals() else 'Not set'}")
-        raise gr.Error(f"Image generation failed: {e}")
-# --- Gradio App UI ---
-css = '''
-/* Header Styling */
-.main-header {
-    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-    padding: 2rem;
-    border-radius: 1rem;
-    margin-bottom: 2rem;
-    box-shadow: 0 10px 30px rgba(0,0,0,0.1);
-}
-.header-title {
-    font-size: 2.5rem !important;
-    font-weight: bold;
-    color: white;
-    text-align: center;
-    margin: 0 !important;
-    text-shadow: 2px 2px 4px rgba(0,0,0,0.2);
-}
-.header-subtitle {
-    color: rgba(255,255,255,0.9);
-    text-align: center;
-    margin-top: 0.5rem !important;
-    font-size: 1.1rem;
-}
-/* Card Styling */
-.card {
-    background: white;
-    border-radius: 1rem;
-    padding: 1.5rem;
-    box-shadow: 0 4px 6px rgba(0,0,0,0.1);
-    border: 1px solid rgba(0,0,0,0.05);
-}
-.dark .card {
-    background: #1f2937;
-    border: 1px solid #374151;
-}
-/* Tab Styling */
-.tabs {
-    border-radius: 0.5rem;
-    overflow: hidden;
-    margin-bottom: 1rem;
-}
-.tabitem {
-    padding: 1rem !important;
-}
-button.selected {
-    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
-    color: white !important;
-}
-/* Button Styling */
-.generate-btn {
-    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
-    border: none !important;
-    color: white !important;
-    font-size: 1.1rem !important;
-    font-weight: 600 !important;
-    padding: 0.8rem 2rem !important;
-    border-radius: 0.5rem !important;
-    cursor: pointer !important;
-    transition: all 0.3s ease !important;
-    width: 100% !important;
-    margin-top: 1rem !important;
-}
-.generate-btn:hover {
-    transform: translateY(-2px) !important;
-    box-shadow: 0 10px 20px rgba(102, 126, 234, 0.4) !important;
-}
-.use-btn {
-    background: linear-gradient(135deg, #10b981 0%, #059669 100%) !important;
-    border: none !important;
-    color: white !important;
-    font-weight: 600 !important;
-    padding: 0.6rem 1.5rem !important;
-    border-radius: 0.5rem !important;
-    cursor: pointer !important;
-    transition: all 0.3s ease !important;
-    width: 100% !important;
-}
-.use-btn:hover {
-    transform: translateY(-1px) !important;
-    box-shadow: 0 5px 15px rgba(16, 185, 129, 0.4) !important;
-}
-/* Input Styling */
-.prompt-input textarea {
-    border-radius: 0.5rem !important;
-    border: 2px solid #e5e7eb !important;
-    padding: 0.8rem !important;
-    font-size: 1rem !important;
-    transition: border-color 0.3s ease !important;
-}
-.prompt-input textarea:focus {
-    border-color: #667eea !important;
-    outline: none !important;
-}
-.dark .prompt-input textarea {
-    border-color: #374151 !important;
-    background: #1f2937 !important;
-}
-/* Image Output Styling */
-#output {
-    border-radius: 0.5rem !important;
-    overflow: hidden !important;
-    box-shadow: 0 4px 6px rgba(0,0,0,0.1) !important;
-}
-/* Progress Bar Styling */
-.progress-bar {
-    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
-}
-/* Examples Styling */
-.examples {
-    background: #f9fafb;
-    border-radius: 0.5rem;
-    padding: 1rem;
-    margin-top: 1rem;
-}
-.dark .examples {
-    background: #1f2937;
-}
-/* Login Message Styling */
-.login-message {
-    background: linear-gradient(135deg, #fef3c7 0%, #fde68a 100%);
-    border-radius: 1rem;
-    padding: 2rem;
-    text-align: center;
-    border: 2px solid #f59e0b;
-}
-.dark .login-message {
-    background: linear-gradient(135deg, #7c2d12 0%, #92400e 100%);
-    border-color: #f59e0b;
-}
-/* Emoji Animations */
-@keyframes bounce {
-    0%, 100% { transform: translateY(0); }
-    50% { transform: translateY(-10px); }
-}
-.emoji-icon {
-    display: inline-block;
-    animation: bounce 2s infinite;
-}
-/* Responsive Design */
-@media (max-width: 768px) {
-    .header-title {
-        font-size: 2rem !important;
-    }
-    .main-container {
-        padding: 1rem !important;
-    }
-}
-'''
-with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
-    # Header
-    gr.HTML('''
-    <div class="main-header">
-        <h1 class="header-title">
-            🍌 Real Nano Banana
-        </h1>
-        <p class="header-subtitle">
-            AI Image Generator powered by Google Nano Banana
-        </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>
-    </div>
-    ''')
-    # Login Notice
-    gr.HTML('''
-    <div style="background: linear-gradient(135deg, #e0f2fe 0%, #bae6fd 100%);
-                border-radius: 0.5rem; padding: 1rem; margin-bottom: 1.5rem;
-                border-left: 4px solid #0284c7;">
-        <p style="margin: 0; color: #075985; font-weight: 600;">
-            🔐 Please sign in with your Hugging Face account to use this service.
-        </p>
-    </div>
-    ''')
-    login_message = gr.Markdown(visible=False)
-    main_interface = gr.Column(visible=False, elem_classes="main-container")
-    with main_interface:
-        with gr.Row():
-            with gr.Column(scale=1):
-                gr.HTML('<div class="card">')
-                # Mode Selection
-                gr.HTML('<h3 style="margin-top: 0;">📸 Select Mode</h3>')
-                active_tab_state = gr.State(value="single")
-                with gr.Tabs(elem_classes="tabs") as tabs:
-                    with gr.TabItem("🖼️ Single Image", id="single") as single_tab:
-                        image_input = gr.Image(
-                            type="filepath",
-                            label="Input Image (Optional)",
-                            elem_classes="image-input"
-                        )
-                        gr.HTML('''
-                        <p style="text-align: center; color: #6b7280; font-size: 0.9rem; margin-top: 0.5rem;">
-                            💡 Leave empty for text-to-image generation
-                        </p>
-                        ''')
-                    with gr.TabItem("🎨 Multiple Images", id="multiple") as multi_tab:
-                        gallery_input = gr.Gallery(
-                            label="Input Images (Max 2 images)",
-                            file_types=["image"],
-                            elem_classes="gallery-input"
-                        )
-                        gr.HTML('''
-                        <p style="text-align: center; color: #6b7280; font-size: 0.9rem; margin-top: 0.5rem;">
-                            💡 Upload up to 2 images for combination/editing
-                        </p>
-                        ''')
-                # Prompt Input
-                gr.HTML('<h3>✍️ Prompt</h3>')
-                prompt_input = gr.Textbox(
-                    label="",
-                    info="Describe what you want the AI to generate",
-                    placeholder="e.g., A delicious pizza, a cat in space, futuristic cityscape...",
-                    lines=3,
-                    elem_classes="prompt-input"
-                )
-                # Generate Button
-                generate_button = gr.Button(
-                    "🚀 Generate",
-                    variant="primary",
-                    elem_classes="generate-btn"
-                )
-                # Examples
-                with gr.Accordion("💡 Example Prompts", open=False):
-                    gr.Examples(
-                        examples=[
-                            ["A delicious looking pizza with melting cheese"],
-                            ["A cat in a spacesuit walking on the moon surface"],
-                            ["Cyberpunk city at night with neon lights"],
-                            ["Japanese garden with cherry blossoms in spring"],
-                            ["Fantasy wizard tower in a magical world"],
-                            ["Make the scene more dramatic and cinematic"],
-                            ["Transform this into a watercolor painting style"],
-                        ],
-                        inputs=prompt_input
-                    )
-                gr.HTML('</div>')
-            with gr.Column(scale=1):
-                gr.HTML('<div class="card">')
-                gr.HTML('<h3 style="margin-top: 0;">🎨 Generated Result</h3>')
-                output_image = gr.Image(
-                    label="",
-                    interactive=False,
-                    elem_id="output"
-                )
-                use_image_button = gr.Button(
-                    "♻️ Use this image for next edit",
-                    elem_classes="use-btn",
-                    visible=False
-                )
-                # Tips
-                gr.HTML('''
-                <div style="background: #f0f9ff; border-radius: 0.5rem; padding: 1rem; margin-top: 1rem;">
-                    <h4 style="margin-top: 0; color: #0369a1;">💡 Tips</h4>
-                    <ul style="margin: 0; padding-left: 1.5rem; color: #0c4a6e;">
-                        <li>Use specific and detailed prompts for better results</li>
-                        <li>You can reuse generated images for iterative improvements</li>
-                        <li>Multiple image mode supports up to 2 images for combination</li>
-                        <li>English prompts tend to produce better results</li>
-                    </ul>
-                </div>
-                ''')
-                gr.HTML('</div>')
-        # Footer
-        gr.HTML('''
-        <div style="text-align: center; margin-top: 2rem; padding: 1rem;
-                    border-top: 1px solid #e5e7eb;">
-            <p style="color: #6b7280;">
-                Made with 💜 using Replicate API | Powered by Google Nano Banana
-            </p>
-        </div>
-        ''')
-    login_button = gr.LoginButton()
-    # --- Event Handlers ---
-    def unified_generator(
-        prompt: str,
-        single_image: Optional[str],
-        multi_images: Optional[List[str]],
-        active_tab: str,
-        oauth_token: Optional[gr.OAuthToken] = None,
-    ):
-        if not verify_login_status(oauth_token):
-            raise gr.Error("Login required. Please click the 'Sign in with Hugging Face' button at the top.")
-        if not prompt:
-            raise gr.Error("Please enter a prompt.")
-        if active_tab == "multiple" and multi_images:
-            result = run_multi_image_logic(prompt, multi_images)
-        else:
-            result = run_single_image_logic(prompt, single_image)
-        return result, gr.update(visible=True)
-    single_tab.select(lambda: "single", None, active_tab_state)
-    multi_tab.select(lambda: "multiple", None, active_tab_state)
-    generate_button.click(
-        unified_generator,
-        inputs=[prompt_input, image_input, gallery_input, active_tab_state],
-        outputs=[output_image, use_image_button],
-    )
-    use_image_button.click(
-        lambda img: (img, gr.update(visible=False)),
-        inputs=[output_image],
-        outputs=[image_input, use_image_button]
-    )
-    # --- Access Control Logic ---
-    def control_access(
-        profile: Optional[gr.OAuthProfile] = None,
-        oauth_token: Optional[gr.OAuthToken] = None
-    ):
-        if not profile:
-            return gr.update(visible=False), gr.update(visible=False)
-        if verify_login_status(oauth_token):
-            return gr.update(visible=True), gr.update(visible=False)
-        else:
-            message = '''
-            <div class="login-message">
-                <h2>🔐 Login Required</h2>
-                <p style="font-size: 1.1rem; margin: 1rem 0;">
-                    Please sign in with your Hugging Face account to use this AI image generation tool.
-                </p>
-                <p style="margin: 1rem 0;">
-                    After logging in, you can access:
-                </p>
-                <ul style="text-align: left; display: inline-block; margin: 1rem 0;">
-                    <li>🚀 High-quality image generation via Google Nano Banana</li>
-                    <li>⚡ Fast image generation and editing</li>
-                    <li>🎨 Text-to-image conversion</li>
-                    <li>🔧 Multiple image editing and combining</li>
-                </ul>
-                <p style="margin-top: 1.5rem; font-weight: bold;">
-                    Click the "Sign in with Hugging Face" button at the top to get started!
-                </p>
-            </div>
-            '''
-            return gr.update(visible=False), gr.update(visible=True, value=message)
-    demo.load(control_access, inputs=None, outputs=[main_interface, login_message])
-if __name__ == "__main__":
-    demo.queue(max_size=None, default_concurrency_limit=None)
-    demo.launch()