Add application file

app.py

@@ -0,0 +1,88 @@
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+from matplotlib import pyplot as plt
+
+import numpy as np
+import tensorflow as tf
+import gradio as gr
+from huggingface_hub import from_pretrained_keras
+
+
+model = from_pretrained_keras('geninhu/attention_mil')
+
+# functions for inference
+IMG_SIZE = 28
+
+# resize the image and it to a float between 0,1
+def plot(input_images=None, predictions=None, attention_weights=None):
+    bag_class = np.argmax(predictions)
+    bag_class = 'This set of image does not contain number 8' if bag_class == 0 else 'This set of image contains number 8'
+
+    # attention_weights = [round(i, 2) for i in attention_weights]
+    prob_str = f"Each image probability: {attention_weights[0]:.2f}, {attention_weights[1]:.2f}, {attention_weights[2]:.2f}"
+
+    if input_images is not None:
+        figure = plt.figure(figsize=(8, 8))
+        for j in range(len(input_images)):
+            image = input_images[j]
+            figure.add_subplot(1, len(input_images), j + 1)
+            plt.grid(False)
+            if attention_weights is not None:
+                plt.title(f"prob={attention_weights[j]:.2f}")
+            plt.imshow(np.squeeze(input_images[j]))
+        return [bag_class, plt.gcf()]
+
+    return [bag_class, prob_str]
+
+
+def preprocess_image(image):
+    # image = image[:, :, 0]
+    image = image / 255.0
+    image = np.expand_dims(image, axis = 0)
+    return image
+
+def infer(input_images_1, input_images_2, input_images_3):
+    if (input_images_1 is not None) & (input_images_2 is not None) & (input_images_3 is not None):
+        # Normalize input data
+        input_images_1 = preprocess_image(input_images_1)
+        input_images_2 = preprocess_image(input_images_2)
+        input_images_3 = preprocess_image(input_images_3)
+
+        # Collect info per model.
+        prediction = model.predict([input_images_1, input_images_2, input_images_3])
+        prediction = np.squeeze(np.swapaxes(prediction, 1, 0))
+        intermediate_model = keras.Model(model.input, model.get_layer("alpha").output)
+        intermediate_predictions = intermediate_model.predict([input_images_1, input_images_2, input_images_3])
+        attention_weights = np.squeeze(np.swapaxes(intermediate_predictions, 1, 0))
+
+        return plot(
+            [input_images_1, input_images_2, input_images_3],
+            predictions=prediction,
+            attention_weights=attention_weights
+        )
+
+# get the inputs
+input1 = gr.Image(shape=(28, 28), type='numpy', image_mode='L', label='First image', show_label=True, visible=True)
+input2 = gr.Image(shape=(28, 28), type='numpy', image_mode='L', label='Second image', show_label=True, visible=True)
+input3 = gr.Image(shape=(28, 28), type='numpy', image_mode='L', label='Third image', show_label=True, visible=True)
+# the app outputs two segmented images
+output = [gr.Label(), gr.Plot()]
+# output = [gr.Plot()]
+# it's good practice to pass examples, description and a title to guide users
+title = 'Image classification'
+description = 'Upload an image'
+
+gr_interface = gr.Interface(
+    infer, inputs=[input1, input2, input3], outputs=output, allow_flagging='never',
+    analytics_enabled=False, title=title, description=description, live=True,
+    # examples = [[f'{i}.png' for i in range(0,3)], [f'{i}.png' for i in range(3,6)], [f'{i}.png' for i in range(6,9)], '9.png']
+    examples = [['samples/0.png', 'samples/6.png', 'samples/2.png'], ['samples/1.png','samples/2.png', 'samples/3.png'],
+                ['samples/4.png', 'samples/8.png', 'samples/7.png'], ['samples/8.png', 'samples/0.png', 'samples/9.png'],
+                ['samples/5.png', 'samples/6.png', 'samples/3.png'], ['samples/7.png', 'samples/8.png', 'samples/9.png']]
+)
+gr_interface.launch(enable_queue=True, debug=True, inbrowser=True)
+
+# gr_interface = gr.Interface(infer, input, output, examples=examples, allow_flagging=False, analytics_enabled=False, title=title, description=description).launch(enable_queue=True, debug=False)
+# gr_interface.launch()