Update app.py

app.py

@@ -1,23 +1,36 @@
-import gradio as gr
 import spaces
-import torch
-from diffusers import AutoencoderKL, TCDScheduler
-from diffusers.models.model_loading_utils import load_state_dict
+import gradio as gr
 from gradio_imageslider import ImageSlider
-from huggingface_hub import hf_hub_download
+# from PIL import Image
+import numpy as np
+import torch
+
+title = """<h1 align="center">AuraSR</h1>
+<p><center>Upscales your images to x4</center></p>
+<p><center>
+<a href="https://huggingface.co/fal/AuraSR-v2" target="_blank">[AuraSR-v2]</a>
+<a href="https://blog.fal.ai/introducing-aurasr-an-open-reproduction-of-the-gigagan-upscaler-2/" target="_blank">[Blog Post]</a>
+<a href="https://huggingface.co/fal-ai/AuraSR" target="_blank">[v1 Model Page]</a>
+</center></p>
+<br/>
+<p>This is an open reproduction of the GigaGAN Upscaler from fal.ai</p>
+"""
 
 with gr.Blocks() as demo:
-    with gr.Column():
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(
-                    type='pil',
-                    label="Input Image"
-                )
-            with gr.Column():
-                result = ImageSlider(
-                    interactive=False,
-                    label="Generated Image",
-                )
+    
+    gr.HTML(title)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            input_image = gr.Image(label="Input Image", type="numpy")
+            process_btn = gr.Button(value="Upscale Image", variant = "primary")
+        with gr.Column(scale=1):
+            output_slider = ImageSlider(label="Before / After", type="numpy")
+
+    process_btn.click(
+        fn=process_image,
+        inputs=[input_image],
+        outputs=output_slider
+    )
 
-demo.queue(max_size=12).launch(share=False)
+demo.launch(debug=True)