Add app.py

app.py

@@ -0,0 +1,29 @@
+# app.py
+
+import gradio as gr
+import jax
+import jax.numpy as jnp
+import numpy as np
+from PIL import Image
+
+# Dummy generator function — Replace this with your real model inference!
+def generate_image(seed):
+    key = jax.random.PRNGKey(seed)
+    # Generate a fake "image" of size 64x64x3 (RGB)
+    img = jax.random.uniform(key, (64, 64, 3), minval=0, maxval=1.0)
+    img_np = np.array(img * 255, dtype=np.uint8)
+    return Image.fromarray(img_np)
+
+# Define Gradio Interface
+iface = gr.Interface(
+    fn=generate_image,
+    inputs=gr.Slider(0, 10000, value=42, step=1, label="Random Seed"),
+    outputs=gr.Image(type="pil", label="Generated Image"),
+    title="JAX Diffusion Demo",
+    description="🎨 Generate random diffusion samples using JAX! \n\n(Replace dummy function with your trained model.)",
+    theme="default",
+    live=False
+)
+
+if __name__ == "__main__":
+    iface.launch()