{
 "cells": [
  {
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   "id": "58b61d4d",
   "metadata": {},
   "outputs": [
    {
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      "1/1 [==============================] - 0s 181ms/step\n",
      "1/1 [==============================] - 0s 147ms/step\n",
      "Running on local URL:  http://127.0.0.1:7867\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7867/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<gradio.routes.App at 0x7f9953260d60>, 'http://127.0.0.1:7867/', None)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "from numpy import asarray\n",
    "import numpy\n",
    "\n",
    "r =requests.get('https://api.nasa.gov/planetary/apod?api_key=0eyGPKWmJmE5Z0Ijx25oG56ydbTKWE2H75xuEefx')\n",
    "result = r.json()\n",
    "receive = requests.get(result['url'])\n",
    "img = Image.open(BytesIO(receive.content)).convert('RGB')\n",
    "\n",
    "with gr.Blocks() as demo:\n",
    "    with gr.Row():\n",
    "      with gr.Column():\n",
    "          gr.Label(\"Original image\")\n",
    "          input_img = gr.Image(img)\n",
    "      with gr.Column():\n",
    "          gr.Label(\"Image with resolution doubled\")\n",
    "          numpydata = asarray(img)\n",
    "          output = double_res(numpydata)#numpy.ndarray\n",
    "          input_img = gr.Image(output)\n",
    "demo.launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8d410d74",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-24 12:18:34.670116: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "config.json not found in /Users/fabio/Downloads/Project/GIanlucaRub/autoencoder_model_d_0\n",
      "2022-12-24 12:18:40.167772: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fabio/opt/anaconda3/lib/python3.9/site-packages/gradio/inputs.py:256: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
      "  warnings.warn(\n",
      "/Users/fabio/opt/anaconda3/lib/python3.9/site-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
      "  warnings.warn(value)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IMPORTANT: You are using gradio version 3.9.1, however version 3.14.0 is available, please upgrade.\n",
      "--------\n",
      "Running on local URL:  http://127.0.0.1:7861\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7861/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<gradio.routes.App at 0x7f996ccff940>, 'http://127.0.0.1:7861/', None)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gradio as gr\n",
    "import numpy as np\n",
    "from math import ceil\n",
    "from huggingface_hub import from_pretrained_keras\n",
    "\n",
    "model = from_pretrained_keras(\"GIanlucaRub/autoencoder_model_d_0\")\n",
    "def double_res(input_image):\n",
    "    input_height = input_image.shape[0] \n",
    "    input_width = input_image.shape[1] \n",
    "    height = ceil(input_height/128)\n",
    "    width = ceil(input_width/128)\n",
    "    expanded_input_image = np.zeros((128*height, 128*width,3), dtype=np.uint8)\n",
    "    np.copyto(expanded_input_image[0:input_height, 0:input_width], input_image)\n",
    "    \n",
    "    output_image = np.zeros((128*height*2, 128*width*2,3), dtype=np.float32)\n",
    "\n",
    "    for i in range(height):\n",
    "        for j in range(width):\n",
    "            temp_slice = expanded_input_image[i*128:(i+1)*128, j*128:(j+1)*128]/255\n",
    "            upsampled_slice = model.predict(temp_slice[np.newaxis, ...]) \n",
    "            np.copyto(output_image[i*256:(i+1)*256, j*256:(j+1)*256], upsampled_slice[0])\n",
    "            if i!= 0 and j!= 0 and i != height-1 and j!=width-1:\n",
    "# removing inner borders\n",
    "                right_slice = expanded_input_image[i*128:(i+1)*128, (j+1)*128-64:(j+1)*128+64]/255\n",
    "                right_upsampled_slice = model.predict(right_slice[np.newaxis, ...]) \n",
    "                resized_right_slice = right_upsampled_slice[0][64:192,64:192]\n",
    "                np.copyto(output_image[i*256+64:(i+1)*256-64, (j+1)*256-64:(j+1)*256+64], resized_right_slice)\n",
    "                \n",
    "                left_slice = expanded_input_image[i*128:(i+1)*128, j*128-64:(j)*128+64]/255\n",
    "                left_upsampled_slice = model.predict(left_slice[np.newaxis, ...]) \n",
    "                resized_left_slice = left_upsampled_slice[0][64:192,64:192]\n",
    "                np.copyto(output_image[i*256+64:(i+1)*256-64, j*256-64:j*256+64], resized_left_slice)\n",
    "                \n",
    "                upper_slice = expanded_input_image[(i+1)*128-64:(i+1)*128+64, j*128:(j+1)*128]/255\n",
    "                upper_upsampled_slice = model.predict(upper_slice[np.newaxis, ...]) \n",
    "                resized_upper_slice = upper_upsampled_slice[0][64:192,64:192]\n",
    "                np.copyto(output_image[(i+1)*256-64:(i+1)*256+64, j*256+64:(j+1)*256-64], resized_upper_slice)\n",
    "                \n",
    "                lower_slice = expanded_input_image[i*128-64:i*128+64, j*128:(j+1)*128]/255\n",
    "                lower_upsampled_slice = model.predict(lower_slice[np.newaxis, ...]) \n",
    "                resized_lower_slice = lower_upsampled_slice[0][64:192,64:192]\n",
    "                np.copyto(output_image[i*256-64:i*256+64, j*256+64:(j+1)*256-64], resized_lower_slice)\n",
    "\n",
    "\n",
    "# removing angles\n",
    "                lower_right_slice = expanded_input_image[i*128-64:i*128+64, (j+1)*128-64:(j+1)*128+64]/255\n",
    "                lower_right_upsampled_slice = model.predict(lower_right_slice[np.newaxis, ...]) \n",
    "                resized_lower_right_slice = lower_right_upsampled_slice[0][64:192,64:192]\n",
    "                np.copyto(output_image[i*256-64:i*256+64,  (j+1)*256-64:(j+1)*256+64], resized_lower_right_slice)\n",
    "                \n",
    "                lower_left_slice = expanded_input_image[i*128-64:i*128+64, j*128-64:j*128+64]/255\n",
    "                lower_left_upsampled_slice = model.predict(lower_left_slice[np.newaxis, ...]) \n",
    "                resized_lower_left_slice = lower_left_upsampled_slice[0][64:192,64:192]\n",
    "                np.copyto(output_image[i*256-64:i*256+64,  j*256-64:j*256+64], resized_lower_left_slice)\n",
    "                \n",
    "\n",
    "                \n",
    "                \n",
    "    resized_output_image = output_image[0:input_height*2,0:input_width*2]\n",
    "    return resized_output_image\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cce833b0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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