add: app and examples

app.py

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+from huggingface_hub import from_pretrained_keras
+import tensorflow as tf
+import gradio as gr
+
+# download the model in the global context
+vis_model = from_pretrained_keras("ariG23498/involution")
+
+def infer(test_image):
+	# convert the image to a tensorflow tensor and resize the image
+	# to a constant 32x32
+	image = tf.constant(test_image)
+	image = tf.image.resize(image, (32, 32))
+	
+	# Use the model and get the activation maps
+	(inv1_out, inv2_out, inv3_out) = vis_model.predict(image[None, ...])
+	_, inv1_kernel = inv1_out
+	_, inv2_kernel = inv2_out
+	_, inv3_kernel = inv3_out
+
+	inv1_kernel = tf.reduce_sum(inv1_kernel, axis=[-1, -2, -3])
+	inv2_kernel = tf.reduce_sum(inv2_kernel, axis=[-1, -2, -3])
+	inv3_kernel = tf.reduce_sum(inv3_kernel, axis=[-1, -2, -3])
+
+	return (
+		inv1_kernel[0, ..., None],
+		inv2_kernel[0, ..., None],
+		inv3_kernel[0, ..., None]
+	)
+
+iface = gr.Interface(
+	fn=infer,
+	title = "Involutional Neural Networks",
+	description = 
+		"""Authors: [Aritra Roy Gosthipaty](https://twitter.com/ariG23498) and [Ritwik Raha](https://twitter.com/ritwik_raha)
+		Paper: [Involution: Inverting the Inherence of Convolution for Visual Recognition](https://arxiv.org/abs/2103.06255)
+		""",
+	inputs=gr.inputs.Image(label="Input Image"),
+	outputs=[
+		gr.outputs.Image(label="Activation from Kernel 1"),
+		gr.outputs.Image(label="Activation from Kernel 2"),
+		gr.outputs.Image(label="Activation from Kernel 3"),
+	],
+	examples=[["examples/llama.jpeg"], ["examples/dalai-lamao.jpeg"]],
+).launch()

requirements.txt

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+tensorflow>2.6