Spaces:
Runtime error
Runtime error
| import tensorflow as tf | |
| from tensorflow.keras.preprocessing import image | |
| from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2 as keras_model | |
| from tensorflow.keras.applications.mobilenet_v2 import ( | |
| preprocess_input, | |
| decode_predictions, | |
| ) | |
| import matplotlib.pyplot as plt | |
| from alibi.explainers import IntegratedGradients | |
| from alibi.datasets import load_cats | |
| from alibi.utils.visualization import visualize_image_attr | |
| import numpy as np | |
| from PIL import Image, ImageFilter | |
| import io | |
| import time | |
| import os | |
| import copy | |
| import pickle | |
| import datetime | |
| import urllib.request | |
| import gradio as gr | |
| url = ( | |
| "https://upload.wikimedia.org/wikipedia/commons/3/38/Adorable-animal-cat-20787.jpg" | |
| ) | |
| path_input = "./cat.jpg" | |
| urllib.request.urlretrieve(url, filename=path_input) | |
| url = "https://upload.wikimedia.org/wikipedia/commons/4/43/Cute_dog.jpg" | |
| path_input = "./dog.jpg" | |
| urllib.request.urlretrieve(url, filename=path_input) | |
| model = keras_model(weights="imagenet") | |
| n_steps = 50 | |
| method = "gausslegendre" | |
| internal_batch_size = 50 | |
| ig = IntegratedGradients( | |
| model, n_steps=n_steps, method=method, internal_batch_size=internal_batch_size | |
| ) | |
| def do_process(img, baseline): | |
| instance = image.img_to_array(img) | |
| instance = np.expand_dims(instance, axis=0) | |
| instance = preprocess_input(instance) | |
| preds = model.predict(instance) | |
| lstPreds = decode_predictions(preds, top=3)[0] | |
| dctPreds = { | |
| lstPreds[i][1]: round(float(lstPreds[i][2]), 2) for i in range(len(lstPreds)) | |
| } | |
| predictions = preds.argmax(axis=1) | |
| if baseline == "white": | |
| baselines = bls = np.ones(instance.shape).astype(instance.dtype) | |
| img_flt = Image.fromarray(np.uint8(np.squeeze(baselines) * 255)) | |
| elif baseline == "black": | |
| baselines = bls = np.zeros(instance.shape).astype(instance.dtype) | |
| img_flt = Image.fromarray(np.uint8(np.squeeze(baselines) * 255)) | |
| elif baseline == "blur": | |
| img_flt = img.filter(ImageFilter.GaussianBlur(5)) | |
| baselines = image.img_to_array(img_flt) | |
| baselines = np.expand_dims(baselines, axis=0) | |
| baselines = preprocess_input(baselines) | |
| else: | |
| baselines = np.random.random_sample(instance.shape).astype(instance.dtype) | |
| img_flt = Image.fromarray(np.uint8(np.squeeze(baselines) * 255)) | |
| explanation = ig.explain(instance, baselines=baselines, target=predictions) | |
| attrs = explanation.attributions[0] | |
| fig, ax = visualize_image_attr( | |
| attr=attrs.squeeze(), | |
| original_image=img, | |
| method="blended_heat_map", | |
| sign="all", | |
| show_colorbar=True, | |
| title=baseline, | |
| plt_fig_axis=None, | |
| use_pyplot=False, | |
| ) | |
| fig.tight_layout() | |
| buf = io.BytesIO() | |
| fig.savefig(buf) | |
| buf.seek(0) | |
| img_res = Image.open(buf) | |
| return img_res, img_flt, dctPreds | |
| input_im = gr.inputs.Image( | |
| shape=(224, 224), image_mode="RGB", invert_colors=False, source="upload", type="pil" | |
| ) | |
| input_drop = gr.inputs.Dropdown( | |
| label="Baseline (default: random)", | |
| choices=["random", "black", "white", "blur"], | |
| default="random", | |
| type="value", | |
| ) | |
| output_img = gr.outputs.Image(label="Output of Integrated Gradients", type="pil") | |
| output_base = gr.outputs.Image(label="Baseline image", type="pil") | |
| output_label = gr.outputs.Label(label="Classification results", num_top_classes=3) | |
| title = "XAI - Integrated gradients" | |
| description = "Playground: Integrated gradients for a ResNet model trained on Imagenet dataset. Tools: Alibi, TF, Gradio." | |
| examples = [["./cat.jpg", "blur"], ["./dog.jpg", "random"]] | |
| article = "<p style='text-align: center'><a href='https://github.com/mawady' target='_blank'>By Dr. Mohamed Elawady</a></p>" | |
| iface = gr.Interface( | |
| fn=do_process, | |
| inputs=[input_im, input_drop], | |
| outputs=[output_img, output_base, output_label], | |
| live=False, | |
| interpretation=None, | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples, | |
| ) | |
| iface.launch(debug=True) | |