app.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional Neural Networks\n",
    "\n",
    "## Project: Write an Algorithm for Landmark Classification\n",
    "\n",
    "### A simple app\n",
    "\n",
    "In this notebook we build a very simple app that uses our exported model.\n",
    "\n",
    "> <img src=\"static_images/icons/noun-info-2558213.png\" alt=\"?\" style=\"width:25px\"/> Note how we are not importing anything from our source code (we do not use any module from the ``src`` directory). This is because the exported model, differently from the model weights, is a standalone serialization of our model and therefore it does not need anything else. You can ship that file to anybody, and as long as they can import ``torch``, they will be able to use your model. This is very important for releasing pytorch models to production.\n",
    "\n",
    "### Test your app\n",
    "Go to a search engine for images (like Google Images) and search for images of some of the landmarks, like the Eiffel Tower, the Golden Gate Bridge, Machu Picchu and so on. Save a few examples locally, then upload them to your app to see how your model behaves!\n",
    "\n",
    "The app will show the top 5 classes that the model think are most relevant for the picture you have uploaded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting ipywidgets\n",
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      "Installing collected packages: widgetsnbextension, jupyterlab-widgets, ipywidgets\n",
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     ]
    }
   ],
   "source": [
    "# !pip install ipywidgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "acd1b13d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting torchvision\n",
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      "Installing collected packages: mpmath, urllib3, typing-extensions, sympy, pillow, numpy, networkx, MarkupSafe, idna, filelock, charset-normalizer, certifi, requests, jinja2, torch, torchvision\n",
      "Successfully installed MarkupSafe-2.1.3 certifi-2023.7.22 charset-normalizer-3.2.0 filelock-3.12.3 idna-3.4 jinja2-3.1.2 mpmath-1.3.0 networkx-3.1 numpy-1.25.2 pillow-10.0.0 requests-2.31.0 sympy-1.12 torch-2.0.1 torchvision-0.15.2 typing-extensions-4.7.1 urllib3-2.0.4\n"
     ]
    }
   ],
   "source": [
    "# !pip install torchvision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3dcc41bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in c:\\users\\gaura\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (1.25.2)\n"
     ]
    }
   ],
   "source": [
    "# !pip install numpy --user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ee82413cc62c41f6b0f4d7b56f1e0cef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Label(value='Please upload a picture of a landmark'), FileUpload(value=(), description='Upload'…"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ipywidgets import VBox, Button, FileUpload, Output, Label\n",
    "from PIL import Image\n",
    "from IPython.display import display\n",
    "import io\n",
    "import numpy as np\n",
    "import torchvision\n",
    "import torchvision.transforms as T\n",
    "import torch\n",
    "\n",
    "# Decide which model you want to use among the ones exported\n",
    "learn_inf = torch.jit.load(\"checkpoints/original_exported.pt\")\n",
    "\n",
    "def on_click_classify(change):\n",
    "\n",
    "    with output:\n",
    "        print(\"clicked\")\n",
    "    # Load image that has been uploaded\n",
    "    fn = io.BytesIO(btn_upload.data[-1])\n",
    "\n",
    "    img = Image.open(fn)\n",
    "    img.load()\n",
    "\n",
    "    # Let's clear the previous output (if any)\n",
    "    out_pl.clear_output()\n",
    "\n",
    "    # Display the image\n",
    "    with out_pl:\n",
    "        print(\"Function button clicked!!\")\n",
    "        ratio = img.size[0] / img.size[1]\n",
    "        c = img.copy()\n",
    "        c.thumbnail([ratio * 200, 200])\n",
    "        display(c)\n",
    "\n",
    "    # Transform to tensor\n",
    "    timg = T.ToTensor()(img).unsqueeze_(0)\n",
    "\n",
    "    # Calling the model\n",
    "    softmax = learn_inf(timg).data.cpu().numpy().squeeze()\n",
    "    \n",
    "    # Get the indexes of the classes ordered by softmax\n",
    "    # (larger first)\n",
    "    idxs = np.argsort(softmax)[::-1]\n",
    "    \n",
    "    # Loop over the classes with the largest softmax\n",
    "    for i in range(5):\n",
    "        # Get softmax value\n",
    "        p = softmax[idxs[i]]\n",
    "    \n",
    "        # Get class name\n",
    "        landmark_name = learn_inf.class_names[idxs[i]]\n",
    "        \n",
    "        labels[i].value = f\"{landmark_name} (prob: {p:.2f})\"\n",
    "\n",
    "\n",
    "# Putting back btn_upload to a widget for next cell\n",
    "btn_upload = FileUpload()\n",
    "\n",
    "btn_run = Button(description=\"Classify\")\n",
    "btn_run.on_click(on_click_classify)\n",
    "\n",
    "labels = []\n",
    "for _ in range(5):\n",
    "    labels.append(Label())\n",
    "\n",
    "output = Output()\n",
    "\n",
    "out_pl = Output()\n",
    "out_pl.clear_output()\n",
    "\n",
    "wgs = [Label(\"Please upload a picture of a landmark\"), btn_upload, btn_run, out_pl,output]\n",
    "wgs.extend(labels)\n",
    "\n",
    "VBox(wgs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (optional) Standalone app or web app\n",
    "\n",
    "You can run this notebook as a standalone app on your computer by following these steps:\n",
    "\n",
    "1. Download this notebook in a directory on your machine\n",
    "2. Download the model export (for example, ``checkpoints/transfer_exported.pt``) in a subdirectory called ``checkpoints`` within the directory where you save the app.ipynb notebook\n",
    "3. Install voila if you don't have it already (``pip install voila``)\n",
    "4. Run your app: ``voila app.ipynb --show_tracebacks=True``\n",
    "5. Customize your notebook to make your app prettier and rerun voila\n",
    "\n",
    "You can also deploy this app as a website using Binder: https://voila.readthedocs.io/en/stable/deploy.html#deployment-on-binder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create your submission archive\n",
    "\n",
    "Now that you are done with your project, please run the following cell. It will generate a file containing all the code you have written, as well as the notebooks. Please submit that file to complete your project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "executing: jupyter nbconvert --to html app.ipynb\n",
      "[NbConvertApp] Converting notebook app.ipynb to html\n",
      "[NbConvertApp] Writing 295299 bytes to app.html\n",
      "executing: jupyter nbconvert --to html udacity-project-3.ipynb\n",
      "[NbConvertApp] Converting notebook udacity-project-3.ipynb to html\n",
      "[NbConvertApp] Writing 582360 bytes to udacity-project-3.html\n",
      "executing: jupyter nbconvert --to html cnn_from_scratch.ipynb\n",
      "[NbConvertApp] Converting notebook cnn_from_scratch.ipynb to html\n",
      "[NbConvertApp] Writing 1154472 bytes to cnn_from_scratch.html\n",
      "executing: jupyter nbconvert --to html transfer_learning.ipynb\n",
      "[NbConvertApp] Converting notebook transfer_learning.ipynb to html\n",
      "[NbConvertApp] Writing 406200 bytes to transfer_learning.html\n",
      "Adding files to submission_2023-08-29T07h55m.tar.gz\n",
      "src/train.py\n",
      "src/create_submit_pkg.py\n",
      "src/helpers.py\n",
      "src/__init__.py\n",
      "src/optimization.py\n",
      "src/model.py\n",
      "src/data.py\n",
      "src/transfer.py\n",
      "src/predictor.py\n",
      "app.ipynb\n",
      "udacity-project-3.ipynb\n",
      "cnn_from_scratch.ipynb\n",
      "transfer_learning.ipynb\n",
      "cnn_from_scratch.html\n",
      "app.html\n",
      "transfer_learning.html\n",
      "udacity-project-3.html\n",
      "\n",
      "----------------------------------------------------------------\n",
      "Done. Please submit the file submission_2023-08-29T07h55m.tar.gz\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# !python src/create_submit_pkg.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "12314cdb4c574d96b91d0bbb62a65632",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Click Me', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "241d9940351046398b1aa85976d13f9a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipywidgets import Button, Output\n",
    "\n",
    "# Create a button\n",
    "button = Button(description=\"Click Me\")\n",
    "\n",
    "# Create an output widget to display messages\n",
    "output = Output()\n",
    "\n",
    "# Define a function to be called when the button is clicked\n",
    "def on_button_click(b):\n",
    "    with output:\n",
    "        print(\"clickev;dfknd\")\n",
    "\n",
    "# Attach the function to the button's click event\n",
    "button.on_click(on_button_click)\n",
    "\n",
    "# Display the button and the output widget\n",
    "display(button, output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5c747811",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ab36f7f0e6814841a1d15389f2ae028d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Button(description='Classify', style=ButtonStyle()), Output(), Output(), Label(value=''), Label…"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ipywidgets import VBox, Button, FileUpload, Output, Label\n",
    "from PIL import Image\n",
    "from IPython.display import display\n",
    "import io\n",
    "import numpy as np\n",
    "import torchvision\n",
    "import torchvision.transforms as T\n",
    "import torch\n",
    "\n",
    "# Decide which model you want to use among the ones exported\n",
    "learn_inf = torch.jit.load(\"checkpoints/original_exported.pt\")\n",
    "\n",
    "def on_click_classify(change):\n",
    "    with output:\n",
    "        print(\"clicked\")\n",
    "        \n",
    "    # Load image using a path\n",
    "    img_path = r\"C:\\Files\\Download\\allfiles\\home\\landmark_images\\test\\01.Mount_Rainier_National_Park\\582dae52c08959a3.jpg\"  # Update with the actual image path\n",
    "    img = Image.open(img_path)\n",
    "    img.load()\n",
    "\n",
    "    # Let's clear the previous output (if any)\n",
    "    out_pl.clear_output()\n",
    "\n",
    "    # Display the image\n",
    "    with out_pl:\n",
    "        ratio = img.size[0] / img.size[1]\n",
    "        c = img.copy()\n",
    "        c.thumbnail([ratio * 200, 200])\n",
    "        display(c)\n",
    "\n",
    "    # Transform to tensor\n",
    "    timg = T.ToTensor()(img).unsqueeze_(0)\n",
    "\n",
    "    # Calling the model\n",
    "    softmax = learn_inf(timg).data.cpu().numpy().squeeze()\n",
    "    \n",
    "    # Get the indexes of the classes ordered by softmax\n",
    "    # (larger first)\n",
    "    idxs = np.argsort(softmax)[::-1]\n",
    "    \n",
    "    # Loop over the classes with the largest softmax\n",
    "    for i in range(5):\n",
    "        # Get softmax value\n",
    "        p = softmax[idxs[i]]\n",
    "    \n",
    "        # Get class name\n",
    "        landmark_name = learn_inf.class_names[idxs[i]]\n",
    "        \n",
    "        labels[i].value = f\"{landmark_name} (prob: {p:.2f})\"\n",
    "\n",
    "\n",
    "# Putting back btn_upload to a widget for next cell\n",
    "btn_run = Button(description=\"Classify\")\n",
    "btn_run.on_click(on_click_classify)\n",
    "\n",
    "labels = []\n",
    "for _ in range(5):\n",
    "    labels.append(Label())\n",
    "\n",
    "output = Output()\n",
    "\n",
    "out_pl = Output()\n",
    "out_pl.clear_output()\n",
    "\n",
    "wgs = [btn_run, out_pl, output]\n",
    "wgs.extend(labels)\n",
    "\n",
    "VBox(wgs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "75ba4a7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting streamlit\n",
      "  Obtaining dependency information for streamlit from https://files.pythonhosted.org/packages/50/28/fdfe9711d553cfaf217cafc9b8ba21479c663b6504cc96652465b153e04b/streamlit-1.26.0-py2.py3-none-any.whl.metadata\n",
      "  Using cached streamlit-1.26.0-py2.py3-none-any.whl.metadata (8.0 kB)\n",
      "Collecting altair<6,>=4.0 (from streamlit)\n",
      "  Obtaining dependency information for altair<6,>=4.0 from https://files.pythonhosted.org/packages/2b/40/ff33821bca16cac30f8d9c3244ac961416f40bf2d3261a1250aabda33a6f/altair-5.1.0-py3-none-any.whl.metadata\n",
      "  Using cached altair-5.1.0-py3-none-any.whl.metadata (8.6 kB)\n",
      "Collecting blinker<2,>=1.0.0 (from streamlit)\n",
      "  Using cached blinker-1.6.2-py3-none-any.whl (13 kB)\n",
      "Collecting cachetools<6,>=4.0 (from streamlit)\n",
      "  Obtaining dependency information for cachetools<6,>=4.0 from https://files.pythonhosted.org/packages/a9/c9/c8a7710f2cedcb1db9224fdd4d8307c9e48cbddc46c18b515fefc0f1abbe/cachetools-5.3.1-py3-none-any.whl.metadata\n",
      "  Using cached cachetools-5.3.1-py3-none-any.whl.metadata (5.2 kB)\n",
      "Collecting click<9,>=7.0 (from streamlit)\n",
      "  Obtaining dependency information for click<9,>=7.0 from https://files.pythonhosted.org/packages/00/2e/d53fa4befbf2cfa713304affc7ca780ce4fc1fd8710527771b58311a3229/click-8.1.7-py3-none-any.whl.metadata\n",
      "  Using cached click-8.1.7-py3-none-any.whl.metadata (3.0 kB)\n",
      "Collecting importlib-metadata<7,>=1.4 (from streamlit)\n",
      "  Obtaining dependency information for importlib-metadata<7,>=1.4 from https://files.pythonhosted.org/packages/cc/37/db7ba97e676af155f5fcb1a35466f446eadc9104e25b83366e8088c9c926/importlib_metadata-6.8.0-py3-none-any.whl.metadata\n",
      "  Using cached importlib_metadata-6.8.0-py3-none-any.whl.metadata (5.1 kB)\n",
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      "Collecting pandas<3,>=1.3.0 (from streamlit)\n",
      "  Obtaining dependency information for pandas<3,>=1.3.0 from https://files.pythonhosted.org/packages/9e/71/756a1be6bee0209d8c0d8c5e3b9fc72c00373f384a4017095ec404aec3ad/pandas-2.0.3-cp311-cp311-win_amd64.whl.metadata\n",
      "  Using cached pandas-2.0.3-cp311-cp311-win_amd64.whl.metadata (18 kB)\n",
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      "Collecting rich<14,>=10.14.0 (from streamlit)\n",
      "  Obtaining dependency information for rich<14,>=10.14.0 from https://files.pythonhosted.org/packages/8d/5f/21a93b2ec205f4b79853ff6e838e3c99064d5dbe85ec6b05967506f14af0/rich-13.5.2-py3-none-any.whl.metadata\n",
      "  Using cached rich-13.5.2-py3-none-any.whl.metadata (18 kB)\n",
      "Requirement already satisfied: tenacity<9,>=8.1.0 in c:\\users\\gaura\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from streamlit) (8.2.3)\n",
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      "Collecting tzlocal<5,>=1.1 (from streamlit)\n",
      "  Obtaining dependency information for tzlocal<5,>=1.1 from https://files.pythonhosted.org/packages/55/a6/a75af44665e5e77e52f6789eef4f8bc056c8e039d96c804b975806942580/tzlocal-4.3.1-py3-none-any.whl.metadata\n",
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      "Requirement already satisfied: validators<1,>=0.2 in c:\\users\\gaura\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from streamlit) (0.21.2)\n",
      "Collecting gitpython!=3.1.19,<4,>=3.0.7 (from streamlit)\n",
      "  Obtaining dependency information for gitpython!=3.1.19,<4,>=3.0.7 from https://files.pythonhosted.org/packages/67/50/742c2fb60989b76ccf7302c7b1d9e26505d7054c24f08cc7ec187faaaea7/GitPython-3.1.32-py3-none-any.whl.metadata\n",
      "  Using cached GitPython-3.1.32-py3-none-any.whl.metadata (10.0 kB)\n",
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      "  Using cached pydeck-0.8.0-py2.py3-none-any.whl (4.7 MB)\n",
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      "Collecting jsonschema>=3.0 (from altair<6,>=4.0->streamlit)\n",
      "  Obtaining dependency information for jsonschema>=3.0 from https://files.pythonhosted.org/packages/2b/ff/af59fd34bc4d7ac3e6e0cd1f3c10317d329b6c1aee179e8b24ad9a79fbac/jsonschema-4.19.0-py3-none-any.whl.metadata\n",
      "  Using cached jsonschema-4.19.0-py3-none-any.whl.metadata (8.2 kB)\n",
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      "Collecting gitdb<5,>=4.0.1 (from gitpython!=3.1.19,<4,>=3.0.7->streamlit)\n",
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      "Collecting markdown-it-py>=2.2.0 (from rich<14,>=10.14.0->streamlit)\n",
      "  Obtaining dependency information for markdown-it-py>=2.2.0 from https://files.pythonhosted.org/packages/42/d7/1ec15b46af6af88f19b8e5ffea08fa375d433c998b8a7639e76935c14f1f/markdown_it_py-3.0.0-py3-none-any.whl.metadata\n",
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      "Collecting pytz-deprecation-shim (from tzlocal<5,>=1.1->streamlit)\n",
      "  Using cached pytz_deprecation_shim-0.1.0.post0-py2.py3-none-any.whl (15 kB)\n",
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      "Collecting attrs>=22.2.0 (from jsonschema>=3.0->altair<6,>=4.0->streamlit)\n",
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      "  Obtaining dependency information for jsonschema-specifications>=2023.03.6 from https://files.pythonhosted.org/packages/1c/24/83349ac2189cc2435e84da3f69ba3c97314d3c0622628e55171c6798ed80/jsonschema_specifications-2023.7.1-py3-none-any.whl.metadata\n",
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      "  Obtaining dependency information for referencing>=0.28.4 from https://files.pythonhosted.org/packages/be/8e/56d6f1e2d591f4d6cbcba446cac4a1b0dc4f584537e2071d9bcee8eeab6b/referencing-0.30.2-py3-none-any.whl.metadata\n",
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      "Installing collected packages: pytz-deprecation-shim, mdurl, importlib-metadata, gitdb, click, cachetools, blinker, attrs, tzlocal, referencing, pydeck, pandas, markdown-it-py, gitpython, rich, jsonschema-specifications, jsonschema, altair, streamlit\n",
      "Successfully installed altair-5.1.0 attrs-23.1.0 blinker-1.6.2 cachetools-5.3.1 click-8.1.7 gitdb-4.0.10 gitpython-3.1.32 importlib-metadata-6.8.0 jsonschema-4.19.0 jsonschema-specifications-2023.7.1 markdown-it-py-3.0.0 mdurl-0.1.2 pandas-2.0.3 pydeck-0.8.0 pytz-deprecation-shim-0.1.0.post0 referencing-0.30.2 rich-13.5.2 streamlit-1.26.0 tzlocal-4.3.1\n"
     ]
    }
   ],
   "source": [
    "!pip install streamlit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a86402b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-08-30 14:14:41.345 \n",
      "  \u001b[33m\u001b[1mWarning:\u001b[0m to view this Streamlit app on a browser, run it with the following\n",
      "  command:\n",
      "\n",
      "    streamlit run C:\\Users\\gaura\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\ipykernel_launcher.py [ARGUMENTS]\n"
     ]
    }
   ],
   "source": [
    "import streamlit as st\n",
    "from PIL import Image\n",
    "\n",
    "def main():\n",
    "    st.title(\"Image Uploader and Viewer\")\n",
    "    \n",
    "    uploaded_image = st.file_uploader(\"Upload an image\", type=[\"jpg\", \"png\", \"jpeg\"])\n",
    "\n",
    "    if uploaded_image is not None:\n",
    "        image = Image.open(uploaded_image)\n",
    "        st.image(image, caption=\"Uploaded Image\", use_column_width=True)\n",
    "    \n",
    "if __name__ == \"__main__\":\n",
    "    main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "600b7c55",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
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