msg

app.py

@@ -1,6 +1,7 @@
 import tensorflow as tf
 import numpy as np
 from PIL import Image
+from glob import glob 
 
 from tensorflow.keras.preprocessing.image import img_to_array
 from huggingface_hub import from_pretrained_keras
@@ -11,7 +12,8 @@ model.summary()
 
 def infer(image):
 	img = Image.fromarray(image)
-	img = img.resize((100,100))
+	# img = img.resize((100,100))
+   img = img.crop((0,100,0,100))
 	ycbcr = img.convert("YCbCr")
 	y, cb, cr = ycbcr.split()
 	y = img_to_array(y)
@@ -39,14 +41,16 @@ article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1609.051
 examples = [['examples/2000-04-28-18-21-24_L5_rgb.jpg'],['examples/2000-08-02-18-23-18_L5_rgb.jpg'],
             ['examples/2000-08-18-18-23-46_L5_rgb.jpg'],['examples/2000-09-19-18-24-18_L5_rgb.jpg'],['examples/2000-10-21-18-24-43_L5_rgb.jpg']]
 
+examples= [[l] for l in glob('examples/tiles/*.jpg')]
+
 iface = gr.Interface(
 	fn=infer,
 	title = " Satellite Super-resolution",
 	description = "This space is a demo of the keras tutorial 'Image Super-Resolution using an Efficient Sub-Pixel CNN' based on the paper 'Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network' 👀",
 	article = article,
 	inputs=gr.inputs.Image(label="Input Image"),
-	outputs=[gr.outputs.Image(label="Resized 100x100 image"),
-	gr.outputs.Image(label="Super-resolution 300x300 image")
+	outputs=[gr.outputs.Image(label="Cropped input image"),
+	gr.outputs.Image(label="Super-resolution x3 image")
 	],
 	examples=examples,
 ).launch()