Update layout for embedded spaces

app.py

@@ -19,15 +19,18 @@ def predict(img):
     ort_inputs = {ort_session.get_inputs()[0].name: img}
     ort_outputs = ort_session.run(None, ort_inputs)
 
-    #probs = np.exp(ort_outputs) / np.sum(np.exp(ort_outputs))  # softmax
-
     label_index = np.argmax(ort_outputs[0], axis=1).item()
     predicted_label = weights.meta["categories"][label_index]
     return predicted_label
 
+with gr.Blocks() as demo:
+    gr.Markdown("# ResNet-50 Using ONNX Runtime")
+    gr.Markdown("Upload any image and see if ResNet-50 can classify it! (1000 possible image classes)")
+
+    with gr.Row():
+        image_input = gr.Image(type="pil", image_mode="RGB", label="Input Image")
+        label_output = gr.Label(label="Predicted Label")
+
+    gr.Markdown("Part of a tutorial on [how to deploy an ONNX mode to Hugging Face](https://liamgroen.nl/posts/day-6-deploying-model-to-huggingface-spaces-through-onnx/index.html)")
 
-demo = gr.Interface(predict, gr.Image(type="pil", image_mode="RGB"), gr.Label(),
-                    title="ResNet-50 Using onnxruntime",
-                    description="Upload any image and see if resnet-50 can classify it! (1000 possible image classes)",
-                    article="Part of a tutorial on [how to deploy an ONNX mode to Hugging Face](https://liamgroen.nl/posts/day-6-deploying-model-to-huggingface-spaces-through-onnx/index.html)")
-demo.launch()
+    demo.launch()