Create app.py

app.py

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+import tensorflow as tf
+from tensorflow.keras.applications.resnet_v2 import ResNet50V2
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.resnet_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
+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 = "/content/cat.jpg"
+urllib.request.urlretrieve(url, filename=path_input)
+
+url = "https://upload.wikimedia.org/wikipedia/commons/4/43/Cute_dog.jpg"
+path_input = "/content/dog.jpg"
+urllib.request.urlretrieve(url, filename=path_input)
+
+model = ResNet50V2(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)
+
+# refs: 
+# - fig2pil: https://stackoverflow.com/questions/57316491/how-to-convert-matplotlib-figure-to-pil-image-object-without-saving-image
+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 is 'white':
+    baselines = bls = np.ones(instance.shape).astype(instance.dtype)
+  elif baseline is 'black':
+    baselines = bls = np.zeros(instance.shape).astype(instance.dtype)
+  else:
+    baselines = np.random.random_sample(instance.shape).astype(instance.dtype)
+  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='Overlaid Attributions',
+                      plt_fig_axis=None, use_pyplot=False)
+  buf = io.BytesIO()
+  fig.savefig(buf)
+  buf.seek(0)
+  img_res = Image.open(buf)
+  return img_res, 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=sorted(list(['black', 'white', 'random'])), default='random', type='value')
+
+output_img = gr.outputs.Image(label='Output image', type='pil')
+output_label = gr.outputs.Label(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'],['./dog.jpg']]
+article="<p style='text-align: center'><a href='https://github.com/mawady/colab-recipes-cv' target='_blank'>Colab recipes for computer vision - Dr. Mohamed Elawady</a></p>"
+iface = gr.Interface(
+    fn=do_process, 
+    inputs=[input_im, input_drop], 
+    outputs=[output_img,output_label],
+    live=False,
+    interpretation=None,
+    title=title,
+    description=description,
+    article=article,
+    examples=examples
+)
+
+iface.test_launch()
+
+iface.launch(share=True, debug=True)
+