Create app.py

app.py

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+from huggingface_hub import from_pretrained_keras
+import tensorflow as tf
+from tensorflow_addons.optimizers import AdamW
+import numpy as np
+import gradio as gr
+
+tf.keras.optimizers.AdamW = AdamW
+model = from_pretrained_keras("keras-io/vit-small-ds")
+
+IMAGE_SIZE = 72
+
+def softmax(x):
+    f_x = np.exp(x) / np.sum(np.exp(x))
+    return f_x
+    
+labels = ["apple", "aquarium_fish", "baby", "bear", "beaver", "bed", "bee", "beetle", "bicycle", "bottle", "bowl", "boy", "bridge", "bus", "butterfly", "camel", "can", "castle", "caterpillar", "cattle", "chair", "chimpanzee", "clock", "cloud", "cockroach", "couch", "cra", "crocodile", "cup", "dinosaur", "dolphin", "elephant", "flatfish", "forest", "fox", "girl", "hamster", "house", "kangaroo", "keyboard", "lamp", "lawn_mower", "leopard", "lion", "lizard", "lobster", "man", "maple_tree", "motorcycle", "mountain", "mouse", "mushroom", "oak_tree", "orange", "orchid", "otter", "palm_tree", "pear", "pickup_truck", "pine_tree", "plain", "plate", "poppy", "porcupine", "possum", "rabbit", "raccoon", "ray", "road", "rocket", "rose", "sea", "seal", "shark", "shrew", "skunk", "skyscraper", "snail", "snake", "spider", "squirrel", "streetcar", "sunflower", "sweet_pepper", "table", "tank", "telephone", "television", "tiger", "tractor", "train", "trout", "tulip", "turtle", "wardrobe", "whale", "willow_tree", "wolf", "woman", "worm"]
+
+def classify_image(image):
+  #inp = inp.reshape((-1, 299, 299, 3))
+  resized_image = tf.image.resize(
+    tf.convert_to_tensor([image]), size=(IMAGE_SIZE, IMAGE_SIZE))
+  pred = model.predict(resized_image)
+  prediction = softmax(pred)
+  return {labels[i]: float(prediction[i]) for i in range(100)} 
+  
+image = gr.inputs.Image()
+label = gr.outputs.Label(num_top_classes=3)
+
+iface = gr.Interface(classify_image,image,label,
+	#outputs=[
+	#        gr.outputs.Textbox(label="Engine issue"),
+       	#	gr.outputs.Textbox(label="Engine issue score")], 
+	examples=["sample.csv","sample2.csv"],
+	#, title="Classification of Ford Motor data",
+#	description = "Model for predicting issues in Ford engines.",
+        article = "Author: <a href=\"https://huggingface.co/joheras\">Jónathan Heras</a>"
+#	examples = ["sample.csv"],
+)
+
+
+iface.launch()
+
+
+
+