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 api_key = 'https://api.nasa.gov/planetary/apod?api_key=0eyGPKWmJmE5Z0Ijx25oG56ydbTKWE2H75xuEefx' date = '&date=2022-12-20' def getRequest(date): r = requests.get(api_key + date) result = r.json() receive = requests.get(result['url']) img = Image.open(BytesIO(receive.content)).convert('RGB') return img # model = from_pretrained_keras("GIanlucaRub/doubleResFinal") 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) to_predict = [] 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 to_predict.append(temp_slice) # removing inner borders for i in range(height): for j in range(width): if i != 0 and j != 0 and i != height-1 and j != width-1: right_slice = expanded_input_image[i * 128:(i+1)*128, (j+1)*128-64:(j+1)*128+64]/255 to_predict.append(right_slice) left_slice = expanded_input_image[i * 128:(i+1)*128, j*128-64:(j)*128+64]/255 to_predict.append(left_slice) upper_slice = expanded_input_image[( i+1)*128-64:(i+1)*128+64, j*128:(j+1)*128]/255 to_predict.append(upper_slice) lower_slice = expanded_input_image[i * 128-64:i*128+64, j*128:(j+1)*128]/255 to_predict.append(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 to_predict.append(lower_right_slice) lower_left_slice = expanded_input_image[i * 128-64:i*128+64, j*128-64:j*128+64]/255 to_predict.append(lower_left_slice) # predicting all images at once completed = False # n = 16 n = 2 while not completed: try: print("attempting with "+ str(n)) predicted = model.predict(np.array(to_predict),batch_size = n) completed = True print("completed with "+ str(n)) except: print("attempt with " + str(n) + " failed") n += -1 if n <= 0: n = 1 counter = 0 for i in range(height): for j in range(width): np.copyto(output_image[i*256:(i+1)*256, j * 256:(j+1)*256], predicted[counter]) counter+=1 for i in range(height): for j in range(width): if i != 0 and j != 0 and i != height-1 and j != width-1: right_upsampled_slice = predicted[counter] counter+=1 resized_right_slice = right_upsampled_slice[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_upsampled_slice = predicted[counter] counter+=1 resized_left_slice = left_upsampled_slice[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_upsampled_slice = predicted[counter] counter+=1 resized_upper_slice = upper_upsampled_slice[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_upsampled_slice = predicted[counter] counter+=1 resized_lower_slice = lower_upsampled_slice[64:192, 64:192] np.copyto(output_image[i*256-64:i*256+64, j*256+64:(j+1)*256-64], resized_lower_slice) lower_right_upsampled_slice = predicted[counter] counter+=1 resized_lower_right_slice = lower_right_upsampled_slice[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_upsampled_slice = predicted[counter] counter+=1 resized_lower_left_slice = lower_left_upsampled_slice[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 def get_new_img(): # sometimes the new image is a video try: original_img = getRequest('') except: original_img = getRequest(date) numpydata = asarray(original_img) doubled_img = double_res(numpydata) # numpy.ndarray return original_img,doubled_img original_img, doubled_img = get_new_img() with gr.Blocks() as demo: with gr.Row(): with gr.Column(): gr.Label("Original image") original = gr.Image(original_img) with gr.Column(): gr.Label("Image with doubled resolution") doubled = gr.Image(doubled_img) with gr.Row(): btn_get = gr.Button("Get the new daily image") # Event btn_get.click(get_new_img, inputs=None, outputs = [original,doubled]) demo.launch()