chore: display the image from the url

app.py

@@ -1,9 +1,22 @@
+from . import utils
 import streamlit as st
 import tensorflow as tf
 
-image_url = st.text_input(
-	label="URL of image",
-	value="",
-	placeholder="https://your-favourite-image.png"
-)
-st.write("Image URL:", image_url)
+def main():
+	image_url = st.text_input(
+		label="URL of image",
+		value="",
+		placeholder="https://your-favourite-image.png"
+	)
+
+	# Preprocess the same image but with normlization.
+	img_url = "https://dl.fbaipublicfiles.com/dino/img.png"
+	image, preprocessed_image = utils.load_image_from_url(
+		image_url,
+		model_type="dino"
+	)
+	st.image(image, caption="Original Image")
+
+
+if __name__ == "__main__":
+	main()

utils.py

@@ -0,0 +1,53 @@
+# import the necessary packages
+import tensorflow as tf
+from tensorflow.keras import layers
+
+from PIL import Image
+from io import BytesIO
+import requests
+import numpy as np
+
+
+RESOLUTION = 224
+
+crop_layer = layers.CenterCrop(RESOLUTION, RESOLUTION)
+norm_layer = layers.Normalization(
+    mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
+    variance=[(0.229 * 255) ** 2, (0.224 * 255) ** 2, (0.225 * 255) ** 2],
+)
+rescale_layer = layers.Rescaling(scale=1./127.5, offset=-1)
+
+
+def preprocess_image(image, model_type, size=RESOLUTION):
+    # Turn the image into a numpy array and add batch dim.
+    image = np.array(image)
+    image = tf.expand_dims(image, 0)
+    
+    # If model type is vit rescale the image to [-1, 1].
+    if model_type == "original_vit":
+        image = rescale_layer(image)
+
+    # Resize the image using bicubic interpolation.
+    resize_size = int((256 / 224) * size)
+    image = tf.image.resize(
+        image,
+        (resize_size, resize_size),
+        method="bicubic"
+    )
+
+    # Crop the image.
+    image = crop_layer(image)
+
+    # If model type is DeiT or DINO normalize the image.
+    if model_type != "original_vit":
+        image = norm_layer(image)
+    
+    return image.numpy()
+    
+
+def load_image_from_url(url, model_type):
+    # Credit: Willi Gierke
+    response = requests.get(url)
+    image = Image.open(BytesIO(response.content))
+    preprocessed_image = preprocess_image(image, model_type)
+    return image, preprocessed_image