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| import gradio as gr | |
| import fal_client | |
| import os | |
| from typing import Optional, List | |
| from huggingface_hub import whoami | |
| # It is recommended to create this as a Secret on your Hugging Face Space | |
| # For example: FAL_KEY = "fal_key_..." | |
| FAL_KEY = os.getenv("FAL_KEY", "") | |
| # Set the key for the fal_client | |
| if FAL_KEY: | |
| fal_client.api_key = FAL_KEY | |
| def get_fal_key(): | |
| """Checks for the FAL_KEY and raises a Gradio error if it's not set.""" | |
| if not FAL_KEY: | |
| raise gr.Error("FAL_KEY is not set. Please add it to your Hugging Face Space secrets.") | |
| def verify_pro_status(token: Optional[gr.OAuthToken]) -> bool: | |
| """Verifies if the user is a Hugging Face PRO user using their token.""" | |
| if not token: | |
| return False | |
| try: | |
| user_info = whoami(token=token.token) | |
| return user_info.get("isPro", False) | |
| except Exception as e: | |
| print(f"Could not verify user's PRO status: {e}") | |
| return False | |
| # --- Backend Generation Functions --- | |
| def run_single_image_logic(prompt: str, image: Optional[str] = None) -> List[str]: | |
| """Handles text-to-image or single image-to-image and returns a list.""" | |
| get_fal_key() | |
| if image: | |
| image_url = fal_client.upload_file(image) | |
| result = fal_client.run( | |
| "fal-ai/nano-banana/edit", | |
| arguments={"prompt": prompt, "image_url": image_url}, | |
| ) | |
| else: | |
| result = fal_client.run( | |
| "fal-ai/nano-banana", arguments={"prompt": prompt} | |
| ) | |
| return [result["images"][0]["url"]] | |
| def run_multi_image_logic(prompt: str, images: List[str]) -> List[str]: | |
| """Handles multi-image editing.""" | |
| get_fal_key() | |
| if not images: | |
| raise gr.Error("Please upload at least one image in the 'Multiple Images' tab.") | |
| output_images = [] | |
| for image_path in images: | |
| image_url = fal_client.upload_file(image_path) | |
| result = fal_client.run( | |
| "fal-ai/nano-banana/edit", | |
| arguments={"prompt": prompt, "image_url": image_url}, | |
| ) | |
| output_images.append(result["images"][0]["url"]) | |
| return output_images | |
| # --- Gradio App UI --- | |
| with gr.Blocks(theme=gr.themes.Soft()) as demo: | |
| gr.Markdown("# Nano Banana for PROs") | |
| gr.Markdown("Hugging Face PRO users can use Google's Nano Banana (Gemini 2.5 Flash Image Preview) on this Space. [Subscribe to PRO](https://huggingface.co/pro)") | |
| login_button = gr.LoginButton() | |
| pro_message = gr.Markdown(visible=False) | |
| main_interface = gr.Column(visible=False) | |
| with main_interface: | |
| gr.Markdown("## Welcome, PRO User!") | |
| with gr.Row(): | |
| # LEFT COLUMN: Inputs | |
| with gr.Column(scale=1): | |
| prompt_input = gr.Textbox( | |
| label="Prompt", | |
| placeholder="A delicious looking pizza" | |
| ) | |
| active_tab_state = gr.State(value="single") | |
| with gr.Tabs() as tabs: | |
| with gr.TabItem("Single Image", id="single") as single_tab: | |
| image_input = gr.Image( | |
| type="filepath", | |
| label="Input Image (Optional for text-to-image)" | |
| ) | |
| with gr.TabItem("Multiple Images", id="multiple") as multi_tab: | |
| gallery_input = gr.Gallery( | |
| label="Input Images", file_types=["image"] | |
| ) | |
| generate_button = gr.Button("Generate", variant="primary") | |
| # RIGHT COLUMN: Outputs | |
| with gr.Column(scale=1): | |
| output_gallery = gr.Gallery(label="Output") | |
| selected_output_image_state = gr.State() | |
| use_image_button = gr.Button("♻️ Use Generated Image for Next Edit") | |
| # --- 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, | |
| ) -> List[str]: | |
| if not verify_pro_status(oauth_token): | |
| raise gr.Error("Access Denied. This service is for PRO users only.") | |
| if active_tab == "multiple" and multi_images: | |
| return run_multi_image_logic(prompt, multi_images) | |
| else: | |
| return run_single_image_logic(prompt, single_image) | |
| 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, login_button], | |
| outputs=[output_gallery], | |
| ) | |
| # New handlers for the continuous editing loop | |
| def store_selected_image(evt: gr.SelectData): | |
| """When an image is selected in the output gallery, store its path in state.""" | |
| return evt.value['image'] | |
| def reuse_output_image(selected_image_path): | |
| """ | |
| Takes the path from state and sends it to the single image input. | |
| Also forces the UI to switch to the "Single Image" tab. | |
| """ | |
| if not selected_image_path: | |
| gr.Warning("Please select an image from the output gallery first!") | |
| return None, gr.update() | |
| # Output 1: The image path for the gr.Image component | |
| # Output 2: The ID of the tab to select for the gr.Tabs component | |
| return selected_image_path, "single" | |
| output_gallery.select(store_selected_image, None, selected_output_image_state) | |
| use_image_button.click( | |
| reuse_output_image, | |
| inputs=[selected_output_image_state], | |
| outputs=[image_input, tabs] | |
| ) | |
| # --- 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_pro_status(oauth_token): | |
| return gr.update(visible=True), gr.update(visible=False) | |
| else: | |
| message = ( | |
| "## ✨ Exclusive Access for PRO Users\n\n" | |
| "Thank you for your interest! This feature is available exclusively for our Hugging Face **PRO** members.\n\n" | |
| "To unlock this and many other benefits, please consider upgrading your account.\n\n" | |
| "### [**Become a PRO Member Today!**](https://huggingface.co/pro)" | |
| ) | |
| return gr.update(visible=False), gr.update(visible=True, value=message) | |
| demo.load(control_access, inputs=None, outputs=[main_interface, pro_message]) | |
| if __name__ == "__main__": | |
| demo.launch() |