import gradio as gr from gradio_image_annotation import image_annotator import fal_client from PIL import Image import io import base64 import numpy as np import os def process_images(annotated_image, second_image, user_api_key=None, progress=gr.Progress()): """ Process the annotated image and second image using fal API """ # Check if annotated_image is provided if annotated_image is None: return None, "Please provide the first image and draw an annotation box" # Check if second_image is provided (could be None or numpy array) if second_image is None or (isinstance(second_image, np.ndarray) and second_image.size == 0): return None, "Please provide the second image" # Check if annotation box exists if not annotated_image.get("boxes") or len(annotated_image["boxes"]) == 0: return None, "Please draw an annotation box on the first image" # Extract bounding box coordinates box = annotated_image["boxes"][0] # Get the first (and only) box xmin = box.get("xmin") ymin = box.get("ymin") xmax = box.get("xmax") ymax = box.get("ymax") # Construct the dynamic prompt with the actual box coordinates prompt = f"""add the in the first image only inside an imaginary box defined by pixels values "xmin": {xmin}, "ymin": {ymin}, "xmax": {xmax}, "ymax": {ymax}. Take care of shadows, lighting, style, and general concept of objects as per the first image.""" print(f"prompt - {prompt}") progress(0.2, desc="Gradio is preparing your images...") try: # Set API key - prioritize user input, then environment variable original_key = os.environ.get("FAL_KEY", "") if user_api_key and user_api_key.strip(): # Use user-provided key os.environ["FAL_KEY"] = user_api_key.strip() api_key_source = "user-provided" elif original_key: # Use environment variable (secret) api_key_source = "environment" else: # No API key available return None, "⚠️ No FAL API key found. Please either:\n1. Duplicate this app and set your FAL_KEY as a secret, or\n2. Enter your FAL API key in the field provided above." # Convert first image to file for upload first_img = annotated_image["image"] if isinstance(first_img, np.ndarray): # Convert numpy array to PIL Image first_img_pil = Image.fromarray(first_img.astype('uint8')) # Save to bytes img1_bytes = io.BytesIO() first_img_pil.save(img1_bytes, format='PNG') img1_bytes.seek(0) uploaded_file1 = fal_client.upload(img1_bytes.getvalue(), "image/png") elif isinstance(first_img, str): # If it's a file path uploaded_file1 = fal_client.upload_file(first_img) else: # If it's already a PIL Image img1_bytes = io.BytesIO() first_img.save(img1_bytes, format='PNG') img1_bytes.seek(0) uploaded_file1 = fal_client.upload(img1_bytes.getvalue(), "image/png") # Convert second image to file for upload if isinstance(second_image, np.ndarray): second_img_pil = Image.fromarray(second_image.astype('uint8')) img2_bytes = io.BytesIO() second_img_pil.save(img2_bytes, format='PNG') img2_bytes.seek(0) uploaded_file2 = fal_client.upload(img2_bytes.getvalue(), "image/png") elif isinstance(second_image, str): uploaded_file2 = fal_client.upload_file(second_image) else: img2_bytes = io.BytesIO() second_image.save(img2_bytes, format='PNG') img2_bytes.seek(0) uploaded_file2 = fal_client.upload(img2_bytes.getvalue(), "image/png") progress(0.4, desc="Processing with nano-banana...") # Setup progress callback def on_queue_update(update): if isinstance(update, fal_client.InProgress): # InProgress updates don't have a progress attribute, just show we're processing progress(0.6, desc="nano-banana is working on your image...") # Optionally log any messages if they exist if hasattr(update, 'logs') and update.logs: for log in update.logs: print(log.get("message", "")) # Call fal API with the dynamic prompt including box coordinates result = fal_client.subscribe( "fal-ai/nano-banana/edit", arguments={ "prompt": prompt, "image_urls": [f"{uploaded_file1}", f"{uploaded_file2}"] }, with_logs=True, on_queue_update=on_queue_update, ) progress(0.95, desc="Finalizing...") # Extract the result image URL if result and "images" in result and len(result["images"]) > 0: output_url = result["images"][0]["url"] description = result.get("description", "Image processed successfully!") progress(1.0, desc="Complete!") return output_url, description else: return None, "Failed to generate image. Please check your API key or try again." except Exception as e: error_message = str(e).lower() # Check for authentication errors if "401" in error_message or "unauthorized" in error_message or "api key" in error_message: return None, f"⚠️ API Authentication Error: Invalid or missing FAL API key.\n\nPlease either:\n1. Duplicate this app and set your FAL_KEY as a secret, or\n2. Enter your valid FAL API key in the field provided above.\n\nGet your API key at: https://fal.ai" # Check for rate limit errors elif "429" in error_message or "rate limit" in error_message: return None, "⚠️ Rate limit exceeded. Please wait a moment and try again, or use your own API key for higher limits." # Check for server errors elif "500" in error_message or "502" in error_message or "503" in error_message: return None, f"⚠️ FAL API server error. The service might be temporarily unavailable.\n\nPlease either:\n1. Try again in a few moments, or\n2. Use your own API key by entering it in the field above.\n\nError details: {str(e)}" # Generic error with fallback message else: return None, f"⚠️ Error occurred: {str(e)}\n\nIf the error persists, please either:\n1. Duplicate this app and set your FAL_KEY as a secret, or\n2. Enter your FAL API key in the field provided above.\n\nGet your API key at: https://fal.ai" finally: # Restore original API key if we temporarily changed it if user_api_key and user_api_key.strip(): if original_key: os.environ["FAL_KEY"] = original_key else: os.environ.pop("FAL_KEY", None) examples_image_banner=gr.HTML( """
Nano Banana Magic ✨
Set1 Left
Set1 Right
Set1 Result
Set2 Left
Set2 Right
Set2 Result
Set3 Left
Set3 Right
Set3 Result
""" ) # Create the Gradio interface with gr.Blocks(theme='ocean') as demo: # Add navigation bar navbar = gr.Navbar( value=[ ("FAL.AI nano-banana", "https://fal.ai/models/fal-ai/nano-banana/edit/api"), ("Learn more about Gradio Navbar", "https://www.gradio.app/guides/multipage-apps#customizing-the-navbar") ], visible=True, main_page_name="🎨 guided nano banana" ) with gr.Row(): # Add the animated banner examples_image_banner.render() with gr.Column(): gr.HTML( """

Guide Your Nano Banana👉🍌

How to use:
1. Upload or capture the first image and draw a box where you want to place an object
2. Upload the second image containing the object you want to insert
3. Click "Generate Composite Image" and wait for the Gradio and Nano-Banana to blend the images

The Gradio app will intelligently place the object from the second image into the boxed area of the first image, taking care of lighting, shadows, and proper integration.
Kindly note that this app is experimental, so image edits might not always create the desired results. You can create a duplicate of the app and experiment with the prompt available here to achieve better results. """ ) # API Key input section with gr.Accordion("🔑 API Configuration (Optional)", open=False): gr.Markdown( """ **Note:** If you're experiencing API errors or want to use your own FAL account: - Enter your FAL API key below, or - [Duplicate this Space](https://huggingface.co/spaces) and set FAL_KEY as a secret - Get your API key at [fal.ai](https://fal.ai) """ ) api_key_input = gr.Textbox( label="FAL API Key", placeholder="Enter your FAL key (optional)", type="password", interactive=True, info="Your key will be used only for this session and won't be stored" ) with gr.Row(): with gr.Column(scale=1): with gr.Row(): with gr.Column(scale=1): gr.Markdown("### Step 1: Annotate First Image _(click on the upload image button if the app is stuck)_") # Image annotator for first image from gradio_image_annotation import image_annotator #first_image = ImageAnnotator( first_image = image_annotator( value=None, label="Draw a box where you want to place the object", image_type="pil", single_box=True, # Only allow one box disable_edit_boxes=True, show_download_button=False, show_share_button=False, box_thickness=3, box_selected_thickness=4, show_label=True, #image_mode="RGB", #box_min_size=20, ) with gr.Column(scale=1): gr.Markdown("### Step 2: Upload Second Image") # Regular image input for second image second_image = gr.Image( label="Image containing the object to insert", type="numpy", height=400, ) # Generate button generate_btn = gr.Button("Step 3: 🚀 Generate Composite Image", variant="primary", size="lg") # Output section with gr.Column(): output_image = gr.Image( label="Generated Composite Image", type="filepath", height=500, ) status_text = gr.Textbox( label="Status", placeholder="Results will appear here...", lines=3, ) # Connect the button to the processing function generate_btn.click( fn=process_images, inputs=[first_image, second_image, api_key_input], outputs=[output_image, status_text], show_progress=True, ) examples = [ [ { "image": "examples/example1-1.png", "boxes": [{"xmin": 61, "ymin": 298, "xmax": 228, "ymax": 462}], }, "examples/example1-2.png", ], [ { "image": "examples/example2-1.png", "boxes": [{"xmin": 205, "ymin": 791, "xmax": 813, "ymax": 1161}], }, "examples/example2-2.jpg", ], [ { "image": "examples/example3-1.png", "boxes": [{"xmin": 24, "ymin": 465, "xmax": 146, "ymax": 607}], }, "examples/example3-2.png", ], ] ex = gr.Examples( examples=examples, inputs=[first_image, second_image], ) with demo.route("ℹ️Tips for Best Results", "/tips"): gr.Markdown( """ # ℹ️ Tips for Best Results - **Box Placement**: Draw the box exactly where you want the object to appear - **Image Quality**: Use high-resolution images for better results - **Object Selection**: The second image should clearly show the object you want to insert - **Lighting**: Images with similar lighting conditions work best - **Processing Time**: Generation typically takes 10-30 seconds - **API Key**: If you encounter errors, try using your own FAL API key """ ) # Different navbar for the Settings page navbar = gr.Navbar( visible=True, main_page_name="Home", ) if __name__ == "__main__": demo.launch(ssr_mode=False, allowed_paths=["."], debug=False)