{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from google.colab import files\n",
        "files.upload()  # Pilih file `kaggle.json`\n",
        "!mkdir -p ~/.kaggle\n",
        "!mv kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json  # Beri izin agar hanya bisa dibaca oleh user"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "POfz5XVlc9xa",
        "outputId": "4503619e-4add-4e09-dbb2-d5c558eb523f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-b512316d-32ca-4c45-93f7-0218e4690a08\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-b512316d-32ca-4c45-93f7-0218e4690a08\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jOIYpzjamlaj",
        "outputId": "e0998e34-ffc2-4ab1-b691-31ba1344b0f1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.11/dist-packages (1.7.4.2)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.11/dist-packages (from kaggle) (6.2.0)\n",
            "Requirement already satisfied: certifi>=14.05.14 in /usr/local/lib/python3.11/dist-packages (from kaggle) (2025.1.31)\n",
            "Requirement already satisfied: charset-normalizer in /usr/local/lib/python3.11/dist-packages (from kaggle) (3.4.1)\n",
            "Requirement already satisfied: idna in /usr/local/lib/python3.11/dist-packages (from kaggle) (3.10)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.11/dist-packages (from kaggle) (5.29.4)\n",
            "Requirement already satisfied: python-dateutil>=2.5.3 in /usr/local/lib/python3.11/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.11/dist-packages (from kaggle) (8.0.4)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from kaggle) (2.32.3)\n",
            "Requirement already satisfied: setuptools>=21.0.0 in /usr/local/lib/python3.11/dist-packages (from kaggle) (75.2.0)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.11/dist-packages (from kaggle) (1.17.0)\n",
            "Requirement already satisfied: text-unidecode in /usr/local/lib/python3.11/dist-packages (from kaggle) (1.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (from kaggle) (4.67.1)\n",
            "Requirement already satisfied: urllib3>=1.15.1 in /usr/local/lib/python3.11/dist-packages (from kaggle) (2.3.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.11/dist-packages (from kaggle) (0.5.1)\n",
            "Dataset URL: https://www.kaggle.com/datasets/rafliindrawan/acne-dataset\n",
            "License(s): unknown\n",
            "Archive:  acne-dataset.zip\n",
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          ]
        }
      ],
      "source": [
        "import os\n",
        "from google.colab import files\n",
        "!pip install kaggle\n",
        "!kaggle datasets download -d rafliindrawan/acne-dataset\n",
        "acne_dir = os.path.join(\"/content/merged_dataset\", \"Acne\", \"data\")\n",
        "os.makedirs(acne_dir, exist_ok=True)\n",
        "!unzip acne-dataset.zip -d merged_dataset/Acne/data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content\n",
        "!git clone https://github.com/Fliw/Progressive-GAN-pytorch.git\n",
        "%cd Progressive-GAN-pytorch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hNngFZTzmvdg",
        "outputId": "b8d8b112-7f1f-4b60-89a7-12e2485814db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
            "Cloning into 'Progressive-GAN-pytorch'...\n",
            "remote: Enumerating objects: 95, done.\u001b[K\n",
            "remote: Counting objects: 100% (79/79), done.\u001b[K\n",
            "remote: Compressing objects: 100% (72/72), done.\u001b[K\n",
            "remote: Total 95 (delta 50), reused 7 (delta 7), pack-reused 16 (from 1)\u001b[K\n",
            "Receiving objects: 100% (95/95), 34.27 KiB | 548.00 KiB/s, done.\n",
            "Resolving deltas: 100% (50/50), done.\n",
            "/content/Progressive-GAN-pytorch\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python train.py --path /content/merged_dataset/Acne --init_step 6 --checkpoint \"/content/model\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iOSfcrKumxpp",
        "outputId": "1f8f465f-ac72-4e4b-fd93-20be59f752cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading checkpoints from /content/model...\n",
            "Loading checkpoints from /content/model...\n",
            "Optimizers loaded successfully!\n",
            "  1% 499/50000 [04:14<6:57:02,  1.98it/s]500; G: 1.315; D: 1.543; Grad: 1.461; Alpha: 0.020\n",
            "  2% 999/50000 [08:27<6:53:34,  1.97it/s]1000; G: 7.876; D: 2.738; Grad: 0.805; Alpha: 0.040\n",
            "  2% 1039/50000 [08:49<6:55:49,  1.96it/s]\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/Progressive-GAN-pytorch/train.py\", line 281, in <module>\n",
            "    train(generator, discriminator, args.init_step, loader, args.total_iter, args.start_iter)\n",
            "  File \"/content/Progressive-GAN-pytorch/train.py\", line 118, in train\n",
            "    real_image = real_image.to(device)\n",
            "                 ^^^^^^^^^^^^^^^^^^^^^\n",
            "KeyboardInterrupt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Copot pydantic & klien Gradio bermasalah\n",
        "!pip uninstall -y pydantic gradio_client\n",
        "\n",
        "# 2. Pasang versi kompatibel\n",
        "!pip install \"pydantic==2.10.6\" \"gradio>=4.17.0\" \"gradio_client>=0.13.0\"\n",
        "\n",
        "# 3. RESTART runtime / kernel Python\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pg7pRwNWSNqD",
        "outputId": "546bdcd8-1526-4a65-b333-aba5beb5f634"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found existing installation: pydantic 2.10.6\n",
            "Uninstalling pydantic-2.10.6:\n",
            "  Successfully uninstalled pydantic-2.10.6\n",
            "Found existing installation: gradio_client 1.8.0\n",
            "Uninstalling gradio_client-1.8.0:\n",
            "  Successfully uninstalled gradio_client-1.8.0\n",
            "Collecting pydantic==2.10.6\n",
            "  Using cached pydantic-2.10.6-py3-none-any.whl.metadata (30 kB)\n",
            "Requirement already satisfied: gradio>=4.17.0 in /usr/local/lib/python3.11/dist-packages (5.25.2)\n",
            "Collecting gradio_client>=0.13.0\n",
            "  Using cached gradio_client-1.8.0-py3-none-any.whl.metadata (7.1 kB)\n",
            "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.11/dist-packages (from pydantic==2.10.6) (0.7.0)\n",
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            "Using cached pydantic-2.10.6-py3-none-any.whl (431 kB)\n",
            "Using cached gradio_client-1.8.0-py3-none-any.whl (322 kB)\n",
            "Installing collected packages: pydantic, gradio_client\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-genai 1.10.0 requires websockets<15.1.0,>=13.0.0, but you have websockets 12.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed gradio_client-1.8.0 pydantic-2.10.6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/Progressive-GAN-pytorch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DoLS73PwTbmW",
        "outputId": "ef4d74e6-755f-46cd-d4cb-bba6c9c8e440"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/Progressive-GAN-pytorch\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch, os, gradio as gr, numpy as np\n",
        "from torchvision import utils, transforms\n",
        "from progan_modules import Generator\n",
        "\n",
        "# ====== konfigurasi tetap ======\n",
        "CHECKPOINT_DIR = \"/content/model\"\n",
        "Z_DIM, CHANNEL_SIZE = 128, 128\n",
        "DEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "FIXED_STEP  = 6        # 256×256\n",
        "FIXED_ALPHA = 0.0      # sesuai slider di screenshot\n",
        "# ==============================\n",
        "\n",
        "g_running = Generator(CHANNEL_SIZE, Z_DIM, pixel_norm=False, tanh=False).to(DEVICE)\n",
        "g_running.load_state_dict(torch.load(os.path.join(CHECKPOINT_DIR, \"g.model\"), map_location=DEVICE))\n",
        "g_running.eval()\n",
        "\n",
        "to_pil = transforms.ToPILImage()\n",
        "\n",
        "@torch.inference_mode()\n",
        "def sample_images(n_images: int = 50,\n",
        "                  seed: int | None = None):\n",
        "    if seed is not None and seed >= 0:\n",
        "        torch.manual_seed(seed); np.random.seed(seed)\n",
        "    else:\n",
        "        torch.seed()\n",
        "\n",
        "    z = torch.randn(n_images, Z_DIM, device=DEVICE)\n",
        "    imgs = g_running(z, step=FIXED_STEP, alpha=FIXED_ALPHA).cpu()\n",
        "\n",
        "    grid = utils.make_grid(imgs, nrow=10, normalize=True, value_range=(-1, 1))\n",
        "    return to_pil(grid)\n",
        "\n",
        "### Gradio UI – hanya 2 input (Jumlah gambar & Seed)\n",
        "demo = gr.Interface(\n",
        "    fn=sample_images,\n",
        "    inputs=[\n",
        "        gr.Slider(1, 200, value=50, step=10, label=\"Jumlah Gambar (kelipatan 10)\"),\n",
        "        gr.Number(value=-1, precision=0, label=\"Seed (‑1 = acak)\"),\n",
        "    ],\n",
        "    outputs=gr.Image(type=\"pil\", label=\"Grid Hasil\"),\n",
        "    title=\"Progressive Growing Generative Adversarial Network\",\n",
        "    description=\"contoh implementasi PGGAN untuk dataset jerawat\",\n",
        "    allow_flagging=\"never\",\n",
        ")\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    demo.queue()\n",
        "    demo.launch(show_api=False, share=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 650
        },
        "id": "WepABixDSBVB",
        "outputId": "6eb98315-7c18-4f6f-bf98-7162e30f8f76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/gradio/interface.py:415: UserWarning: The `allow_flagging` parameter in `Interface` is deprecated.Use `flagging_mode` instead.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "* Running on public URL: https://4112175f2aaeb31f14.gradio.live\n",
            "\n",
            "This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://4112175f2aaeb31f14.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        }
      ]
    }
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}