Create app.py

app.py

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+ìimport gradio as gr
+import numpy as np
+from math import ceil
+import tensorflow.keras as tfk
+
+model = tfk.models.load_model("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
+
+demo = gr.Interface(
+    fn=double_res,
+    title="Double picture resolution",
+    description="Upload a picture and get the horizontal and vertical resolution doubled (4x pixels)",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Image(type="numpy")
+        ],
+    outputs=gr.Image(type="numpy"))
+
+demo.launch()
+