init space

.gitignore

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+.idea/
+__pycache__/
+get.py

app.py

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+import gradio as gr
+from style_transfer import StyleTransfer
+
+style = StyleTransfer()
+
+
+def predict(content_image, style_image):
+    return style.transfer(content_image, style_image)
+
+
+footer = r"""
+<center>
+<b>
+Demo for <a href='https://www.tensorflow.org/hub/tutorials/tf2_arbitrary_image_stylization'>Style Transfer</a>
+</b>
+</center>
+"""
+
+coffe = r"""
+<center>
+<a href="https://www.buymeacoffee.com/leonelhs"> <img 
+src="https://img.buymeacoffee.com/button-api/?text=Buy me a 
+coffee&emoji=&slug=leonelhs&button_colour=FFDD00&font_colour=000000&font_family=Cookie&outline_colour=000000
+&coffee_colour=ffffff" /></a>
+</center>
+"""
+
+with gr.Blocks(title="Style Transfer") as app:
+    gr.HTML("<center><h1>Style Transfer</h1></center>")
+    gr.HTML("<center><h3>Fast Style Transfer for Arbitrary Styles</h3></center>")
+    with gr.Row(equal_height=False):
+        with gr.Column():
+            content_img = gr.Image(type="filepath", label="Content image")
+            style_img = gr.Image(type="filepath", label="Style image")
+            run_btn = gr.Button(variant="primary")
+        with gr.Column():
+            output_img = gr.Image(type="pil", label="Output image")
+            gr.ClearButton(components=[content_img, style_img, output_img], variant="stop")
+
+    run_btn.click(predict, [content_img, style_img], [output_img])
+
+    with gr.Row():
+        blobs_c = [[f"examples/contents/{x:02d}.jpg"] for x in range(1, 4)]
+        examples_c = gr.Dataset(components=[content_img], samples=blobs_c)
+        examples_c.click(lambda x: x[0], [examples_c], [content_img])
+    with gr.Row():
+        blobs_s = [[f"examples/styles/{x:02d}.jpg"] for x in range(1, 12)]
+        examples_s = gr.Dataset(components=[style_img], samples=blobs_s)
+        examples_s.click(lambda x: x[0], [examples_s], [style_img])
+
+    with gr.Row():
+        gr.HTML(footer)
+    with gr.Row():
+        gr.HTML(coffe)
+
+app.launch(share=False, debug=True, show_error=True)
+app.queue()

requirements.txt

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+pillow>=10.0.1
+numpy>=1.23.5
+tensorflow>=2.15.0