Create app.py

app.py

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+import numpy as np
+import tensorflow as tf
+import gradio as gr
+from huggingface_hub import from_pretrained_keras
+import cv2
+
+img_size = 28
+model = from_pretrained_keras("keras-io/keras-reptile")
+
+def read_image(image):
+    image = tf.convert_to_tensor(image)
+    image = cv2.resize()
+    image = image / 127.5 - 1
+    return image
+
+def infer(model, image_tensor):
+    predictions = model.predict(np.expand_dims((image_tensor), axis=0))
+    predictions = np.squeeze(predictions)
+    predictions = np.argmax(predictions, axis=0)
+    return predictions
+def display_result(input_image):
+    image_tensor = read_image(input_image)
+    prediction_label = infer(model=model, image_tensor=image_tensor)
+    return prediction_label
+
+input = gr.inputs.Image()
+examples = [["/content/drive/MyDrive/boot.jpg"], ["/content/drive/MyDrive/sneaker.jpg"]] 
+title = "Few shot learning"
+description = "Upload an image or select from examples to classify fashion items."
+
+
+gr.Interface(display_result, input, outputs="text", examples=examples, allow_flagging=False, analytics_enabled=False,
+ title=title, description=description).launch(enable_queue=True)