Delete app.py

app.py

@@ -1,102 +0,0 @@
-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
-
-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')
-
-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(img)
-        with gr.Column():
-            gr.Label("Image with resolution doubled")
-            numpydata = asarray(img)
-            output = double_res(numpydata)  # numpy.ndarray
-            input_img = gr.Image(output)
-demo.launch()