Demo.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Demo.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kv1EnewEE9QI"
      },
      "source": [
        "!pip install anvil-uplink"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z5Rdkc5bSv-W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12161fc0-cf35-4f71-b5e6-3665da66e369"
      },
      "source": [
        "import anvil.server\n",
        "anvil.server.connect(\"Your server link here\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Connecting to wss://anvil.works/uplink\n",
            "Anvil websocket open\n",
            "Connected to \"Default environment\" as SERVER\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P842Gvf2iL-I"
      },
      "source": [
        "### Loading Libraries | Mounting our Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v4UTFShs1ooK"
      },
      "source": [
        "from fastai.vision import *\n",
        "from fastai.vision.interpret import *\n",
        "from fastai.callbacks.hooks import *\n",
        "from pathlib import Path\n",
        "from fastai.utils.mem import *\n",
        "torch.backends.cudnn.benchmark=True"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xpMHGy9o3zUe",
        "outputId": "e55d6ae6-7951-4f5b-c772-53eb379f1a2d"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2wXq4UPgiDzd"
      },
      "source": [
        "### Exporting the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A2TVgF_o3zIp"
      },
      "source": [
        "path = Path('gdrive/My Drive/Semantic_Segmentation/model')\n",
        "\n",
        "def acc_rtk(*_): pass\n",
        "model = load_learner(path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ceSLjslisde"
      },
      "source": [
        "### Importing Images"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "image = next(iter(uploaded))"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "KTqv4FGypc7J",
        "outputId": "ff37edfa-fe2a-46cb-a122-72f94f5598ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-8b4c223c-23ce-4cc2-b710-6d659fb04093\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-8b4c223c-23ce-4cc2-b710-6d659fb04093\">\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 src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving 000000529.png to 000000529.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QfdPBqFaf7_T"
      },
      "source": [
        "img = open_image('/content/' + image)\n",
        "color_map = model.predict(img)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "1e4Kx2gUheEV",
        "outputId": "febb7ffa-96b3-4f78-f285-fe93dc09670c"
      },
      "source": [
        "color_map[0] , color_map[0].show(figsize=(5,5))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(ImageSegment (1, 288, 352), None)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m9YPlq5mW86V"
      },
      "source": [
        "import anvil.media\n",
        "\n",
        "@anvil.server.callable\n",
        "def segment_image(file):\n",
        "  with anvil.media.TempFile(file) as f:\n",
        "    img = open_image(f)\n",
        "  seg = model.predict(img)\n",
        "  return img"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}