format code

app.py

@@ -1,7 +1,10 @@
 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
+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
@@ -18,7 +21,9 @@ import urllib.request
 import gradio as gr
 
 
-url = "https://upload.wikimedia.org/wikipedia/commons/3/38/Adorable-animal-cat-20787.jpg"
+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)
 
@@ -26,77 +31,88 @@ 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')
+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)
+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')
+    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)
+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/colab-recipes-cv' target='_blank'>Colab recipes for computer vision - Dr. Mohamed Elawady</a></p>"
+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],
+    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
+    examples=examples,
 )
 
 iface.launch(debug=True)
-