Upload app.py

app.py

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+import requests
+from PIL import Image
+from io import BytesIO
+from numpy import asarray
+import gradio as gr
+import numpy as np
+from math import ceil
+from huggingface_hub import from_pretrained_keras
+
+
+def getRequest():
+    r = requests.get(
+        'https://api.nasa.gov/planetary/apod?api_key=0eyGPKWmJmE5Z0Ijx25oG56ydbTKWE2H75xuEefx')
+    result = r.json()
+    receive = requests.get(result['url'])
+    img = Image.open(BytesIO(receive.content)).convert('RGB')
+    return img
+
+
+model = from_pretrained_keras("GIanlucaRub/autoencoder_model_d_0")
+
+
+def double_res(input_image):
+    input_height = input_image.shape[0]
+    input_width = input_image.shape[1]
+    height = ceil(input_height/128)
+    width = ceil(input_width/128)
+    expanded_input_image = np.zeros((128*height, 128*width, 3), dtype=np.uint8)
+    np.copyto(expanded_input_image[0:input_height, 0:input_width], input_image)
+
+    output_image = np.zeros((128*height*2, 128*width*2, 3), dtype=np.float32)
+
+    for i in range(height):
+        for j in range(width):
+            temp_slice = expanded_input_image[i *
+                                              128:(i+1)*128, j*128:(j+1)*128]/255
+            upsampled_slice = model.predict(temp_slice[np.newaxis, ...])
+            np.copyto(output_image[i*256:(i+1)*256, j *
+                      256:(j+1)*256], upsampled_slice[0])
+            if i != 0 and j != 0 and i != height-1 and j != width-1:
+                # removing inner borders
+                right_slice = expanded_input_image[i *
+                                                   128:(i+1)*128, (j+1)*128-64:(j+1)*128+64]/255
+                right_upsampled_slice = model.predict(
+                    right_slice[np.newaxis, ...])
+                resized_right_slice = right_upsampled_slice[0][64:192, 64:192]
+                np.copyto(output_image[i*256+64:(i+1)*256-64,
+                          (j+1)*256-64:(j+1)*256+64], resized_right_slice)
+
+                left_slice = expanded_input_image[i *
+                                                  128:(i+1)*128, j*128-64:(j)*128+64]/255
+                left_upsampled_slice = model.predict(
+                    left_slice[np.newaxis, ...])
+                resized_left_slice = left_upsampled_slice[0][64:192, 64:192]
+                np.copyto(output_image[i*256+64:(i+1)*256-64,
+                          j*256-64:j*256+64], resized_left_slice)
+
+                upper_slice = expanded_input_image[(
+                    i+1)*128-64:(i+1)*128+64, j*128:(j+1)*128]/255
+                upper_upsampled_slice = model.predict(
+                    upper_slice[np.newaxis, ...])
+                resized_upper_slice = upper_upsampled_slice[0][64:192, 64:192]
+                np.copyto(output_image[(i+1)*256-64:(i+1)*256+64,
+                          j*256+64:(j+1)*256-64], resized_upper_slice)
+
+                lower_slice = expanded_input_image[i *
+                                                   128-64:i*128+64, j*128:(j+1)*128]/255
+                lower_upsampled_slice = model.predict(
+                    lower_slice[np.newaxis, ...])
+                resized_lower_slice = lower_upsampled_slice[0][64:192, 64:192]
+                np.copyto(output_image[i*256-64:i*256+64,
+                          j*256+64:(j+1)*256-64], resized_lower_slice)
+
+
+# removing angles
+                lower_right_slice = expanded_input_image[i *
+                                                         128-64:i*128+64, (j+1)*128-64:(j+1)*128+64]/255
+                lower_right_upsampled_slice = model.predict(
+                    lower_right_slice[np.newaxis, ...])
+                resized_lower_right_slice = lower_right_upsampled_slice[0][64:192, 64:192]
+                np.copyto(output_image[i*256-64:i*256+64,  (j+1)
+                          * 256-64:(j+1)*256+64], resized_lower_right_slice)
+
+                lower_left_slice = expanded_input_image[i *
+                                                        128-64:i*128+64, j*128-64:j*128+64]/255
+                lower_left_upsampled_slice = model.predict(
+                    lower_left_slice[np.newaxis, ...])
+                resized_lower_left_slice = lower_left_upsampled_slice[0][64:192, 64:192]
+                np.copyto(
+                    output_image[i*256-64:i*256+64,  j*256-64:j*256+64], resized_lower_left_slice)
+
+    resized_output_image = output_image[0:input_height*2, 0:input_width*2]
+    return resized_output_image
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            gr.Label("Original image")
+            input_img = gr.Image(getRequest())
+        with gr.Column():
+            gr.Label("Image with resolution doubled")
+            numpydata = asarray(getRequest())
+            output = double_res(numpydata)  # numpy.ndarray
+            input_img = gr.Image(output)
+    with gr.Row().style(mobile_collapse=False, equal_height=True):
+        btn_get = gr.Button("Get the new daily-image")
+    # Event
+    btn_get.click(demo.launch())
+demo.launch()