more information.

GEMINI.md

@@ -0,0 +1,40 @@
+# Project Overview
+
+This project is a web-based user interface for `rayt`, a Python library that implements the concepts from Peter Shirley's "Ray Tracing in One Weekend" book.
+
+The application is built using the Gradio framework, providing an interactive way to generate ray-traced images. It leverages Numba and CUDA for accelerated rendering.
+
+**Key Technologies:**
+
+*   **Python:** The core programming language.
+*   **Gradio:** Used to create the web-based UI.
+*   **rayt library:** The underlying ray tracing engine.
+*   **Numba/CUDA:** For performance-critical rendering calculations.
+*   **Pillow (PIL):** For image manipulation.
+
+# Building and Running
+
+## 1. Installation
+
+Install the required Python dependencies using pip:
+
+```bash
+pip install -r requirements.txt
+```
+
+## 2. Running the Application
+
+To start the Gradio web server, run the main application file:
+
+```bash
+python app.py
+```
+
+This will launch a local web server, and you can access the UI in your browser at the address provided in the console (usually `http://127.0.0.1:7860`).
+
+# Development Conventions
+
+*   The main application logic is contained in `app.py`.
+*   The core rendering functionality is handled by the `render_image` function, which interacts with the `rayt` library.
+*   The user interface is defined declaratively using Gradio's `Blocks` API.
+*   The application checks for CUDA availability and falls back to the Numba renderer if a compatible GPU is not found.

app.py

@@ -1,4 +1,5 @@
 import gradio as gr
+from gradio import themes as gr_themes
 import io
 import sys
 from PIL import Image
@@ -57,6 +58,15 @@ with gr.Blocks() as iface:
     gr.Markdown("# Ray Tracing in One Weekend")
     gr.Markdown("A Python implementation of the book by Peter Shirley, rendered with Numba.")
 
+    if cuda.is_available():
+        gr.Markdown("CUDA support: **<span style='color: green'>AVAILABLE</span>**. Rendering will be done on the GPU.")
+    else:
+        gr.Markdown(
+            "CUDA support: **<span style='color: red'>NOT AVAILABLE</span>**. Rendering will be done on the CPU. "
+            "If you have an NVIDIA GPU, you can clone the project and run it locally. "
+            "You can also deploy it to a server with CUDA support for faster rendering."
+        )
+
     with gr.Row():
         with gr.Column():
             image_width = gr.Slider(label="Image Width", minimum=100, maximum=1200, value=300, step=10)
@@ -66,6 +76,7 @@ with gr.Blocks() as iface:
             render_button = gr.Button("Render Image")
         with gr.Column():
             output_image = gr.Image(label="Rendered Image", type="pil")
+            gr.Image(label="Sample Output", value="image.webp")
 
     render_button.click(
         fn=render_image,