Update app.py

app.py

@@ -1,126 +1,41 @@
 import gradio as gr
-import fal_client
-import os
-from typing import Optional
-
-# 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 generate_image(prompt: str, image: Optional[str] = None) -> str:
-    """
-    Generates or edits an image.
-    - If an image filepath is provided, it performs image-to-image editing.
-    - Otherwise, it performs text-to-image generation.
-    """
-    get_fal_key()
-
-    if image:
-        # Image-to-Image: Upload the local file to get a public URL
-        image_url = fal_client.upload_file(image)
-        result = fal_client.run(
-            "fal-ai/nano-banana/edit",
-            arguments={
-                "prompt": prompt,
-                "image_url": image_url,
-            },
-        )
-    else:
-        # Text-to-Image
-        result = fal_client.run(
-            "fal-ai/nano-banana",
-            arguments={
-                "prompt": prompt,
-            },
-        )
-    return result["images"][0]["url"]
-
-def multi_image_edit(prompt: str, images: list[str]) -> list[str]:
-    """Edits multiple images based on a text prompt."""
-    get_fal_key()
-
-    if not images:
-        raise gr.Error("Please upload at least one image.")
-
-    output_images = []
-    for image_path in images:
-        # Upload each image and get its URL
-        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 Image Generation")
-    gr.Markdown("Generate or edit images with FAL. **Sign in with Hugging Face to begin.**")
-    
-    login_button = gr.LoginButton()
-    
-    # This entire Column will be hidden until the user logs in
-    main_interface = gr.Column(visible=False)
-
-    with main_interface:
-        gr.Markdown("## Welcome! You are logged in.")
-        with gr.Tabs():
-            with gr.TabItem("Generate"):
-                with gr.Row():
-                    with gr.Column(scale=1):
-                        prompt_input = gr.Textbox(label="Prompt", placeholder="A delicious looking pizza")
-                        image_input = gr.Image(type="filepath", label="Input Image (Optional)")
-                        generate_button = gr.Button("Generate", variant="primary")
-                    with gr.Column(scale=1):
-                        generate_output = gr.Image(label="Output")
-                
-                generate_button.click(
-                    generate_image,
-                    inputs=[prompt_input, image_input],
-                    outputs=[generate_output],
-                )
-
-            with gr.TabItem("Multi-Image Edit"):
-                with gr.Row():
-                    with gr.Column(scale=1):
-                        multi_prompt_input = gr.Textbox(label="Prompt", placeholder="Make it black and white")
-                        multi_image_input = gr.Gallery(label="Input Images", file_types=["image"])
-                        multi_edit_button = gr.Button("Edit Images", variant="primary")
-                    with gr.Column(scale=1):
-                        multi_image_output = gr.Gallery(label="Output Images")
-                
-                multi_edit_button.click(
-                    multi_image_edit,
-                    inputs=[multi_prompt_input, multi_image_input],
-                    outputs=[multi_image_output],
-                )
-
-    def show_app_on_login(profile: Optional[gr.OAuthProfile] = None):
-        """
-        Controls the visibility of the main UI.
-        Gradio automatically injects `gr.OAuthProfile` if the user is logged in.
-        """
-        if profile:
-            return gr.update(visible=True)
-        return gr.update(visible=False)
-
-    # When the app loads or the user logs in/out, this function will be called.
-    # It receives the user's profile (or None) and updates the UI visibility.
-    demo.load(show_app_on_login, inputs=None, outputs=main_interface)
-
-if __name__ == "__main__":
-    demo.launch()
+from huggingface_hub import list_models
+
+
+def hello(profile: gr.OAuthProfile | None) -> str:
+    # ^ expect a gr.OAuthProfile object as input to get the user's profile
+    # if the user is not logged in, profile will be None
+    if profile is None:
+        return "I don't know you."
+    return f"Hello {profile.name}"
+
+
+def list_private_models(profile: gr.OAuthProfile | None, oauth_token: gr.OAuthToken | None) -> str:
+    # ^ expect a gr.OAuthToken object as input to get the user's token
+    # if the user is not logged in, oauth_token will be None
+    if oauth_token is None:
+        return "Please log in to list private models."
+    models = [
+        f"{model.id} ({'private' if model.private else 'public'})"
+        for model in list_models(author=profile.username, token=oauth_token.token)
+    ]
+    return "Models:\n\n" + "\n - ".join(models) + "."
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        "# Gradio OAuth Space"
+        "\n\nThis Space is a demo for the **Sign in with Hugging Face** feature. "
+        "Duplicate this Space to get started."
+        "\n\nFor more details, check out:"
+        "\n- https://www.gradio.app/guides/sharing-your-app#o-auth-login-via-hugging-face"
+        "\n- https://huggingface.co/docs/hub/spaces-oauth"
+    )
+    gr.LoginButton()
+    # ^ add a login button to the Space
+    m1 = gr.Markdown()
+    m2 = gr.Markdown()
+    demo.load(hello, inputs=None, outputs=m1)
+    demo.load(list_private_models, inputs=None, outputs=m2)
+
+demo.launch()