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image-classification-interpretation-blocks / app.py

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	import json
	import numpy as np
	import tensorflow as tf
	from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
	import shap
	import gradio as gr

	import matplotlib
	matplotlib.use('Agg')

	model = ResNet50(weights='imagenet')



	url = "https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json"
	with open(shap.datasets.cache(url)) as file:
	class_names = [v[1] for v in json.load(file).values()]

	def predict(x):
	"""prediction function for shap."""
	tmp = np.expand_dims(x, axis=0)
	preprocess_input(tmp)
	pred = model(tmp)
	labels = list(zip(class_names, pred.numpy()[0].tolist()))
	return dict(sorted(labels, key=lambda t: t[1])[-10:])

	def f(x):
	"""prediction function for shap because explainer does not expect batch dim."""
	tmp = x.copy()
	preprocess_input(tmp)
	return model(tmp)

	def interpretation_function(img):
	# shap set up - see https://shap.readthedocs.io/en/latest/example_notebooks/image_examples/image_classification/Image%20Multi%20Class.html
	masker = shap.maskers.Image("inpaint_telea", [224, 224, 3])
	explainer = shap.PartitionExplainer(f, masker)

	# prediction to know which slice of shap value to get
	# shap interprets all classes
	pred = f(np.expand_dims(img, 0)).numpy().argmax()
	shap_values = explainer(np.expand_dims(img, 0), max_evals=10)

	# get shap values and return (224, 224)
	scores = shap_values.values[0][:, :, :, pred]
	scores = scores.mean(axis=-1)
	max_val, min_val = np.max(scores), np.min(scores)
	scores = (scores - min_val) / (max_val - min_val)
	# compute image to binary format so front-end can display
	return {"original": gr.processing_utils.encode_array_to_base64(img),
	"interpretation": scores.tolist()}


	with gr.Blocks() as demo:
	with gr.Row():
	with gr.Column():
	input_img = gr.Image(label="Input Image", shape=(224, 224))
	with gr.Row():
	classify = gr.Button("Classify")
	interpret = gr.Button("Interpret")
	with gr.Column():
	label = gr.Label(label="Predicted Class")
	with gr.Column():
	interpretation = gr.components.Interpretation(input_img)
	classify.click(predict, input_img, label)
	interpret.click(interpretation_function, input_img, interpretation)

	demo.launch()