Readded blog post link.

.idea/FloodDetector.iml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/discord.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="DiscordProjectSettings">
+    <option name="show" value="ASK" />
+    <option name="description" value="" />
+  </component>
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/FloodDetector.iml" filepath="$PROJECT_DIR$/.idea/FloodDetector.iml" />
+    </modules>
+  </component>
+</project>

app.ipynb

@@ -2,40 +2,54 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": null,
    "outputs": [],
    "source": [
     "#|default_exp app"
-   ]
+   ],
+   "metadata": {
+    "collapsed": false
+   }
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "# Flood or no flood?"
-   ],
    "metadata": {
     "collapsed": false
-   }
+   },
+   "source": [
+    "# Flood or no flood?"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "outputs": [],
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/salmannaqvi/lib/python3.10/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: dlopen(/Users/salmannaqvi/lib/python3.10/site-packages/torchvision/image.so, 0x0006): Symbol not found: (__ZN2at4_ops19empty_memory_format4callEN3c108ArrayRefINS2_6SymIntEEENS2_8optionalINS2_10ScalarTypeEEENS6_INS2_6LayoutEEENS6_INS2_6DeviceEEENS6_IbEENS6_INS2_12MemoryFormatEEE)\n",
+      "  Referenced from: '/Users/salmannaqvi/lib/python3.10/site-packages/torchvision/image.so'\n",
+      "  Expected in: '/Users/salmannaqvi/lib/python3.10/site-packages/torch/lib/libtorch_cpu.dylib'\n",
+      "  warn(f\"Failed to load image Python extension: {e}\")\n"
+     ]
+    }
+   ],
    "source": [
     "#|export\n",
     "import gradio as gr\n",
     "from fastai.vision.all import *"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -49,36 +63,36 @@
    ],
    "source": [
     "image = PILImage.create('images/test_images/1.jpeg'); image"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "## Create learner."
-   ],
    "metadata": {
     "collapsed": false
-   }
+   },
+   "source": [
+    "## Create learner."
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "#|export\n",
     "\n",
     "learner = load_learner('model/flood_classifier.pkl')"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -107,23 +121,23 @@
    ],
    "source": [
     "learner.predict(image)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "## Create classification function."
-   ],
    "metadata": {
     "collapsed": false
-   }
+   },
+   "source": [
+    "## Create classification function."
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "#|export\n",
@@ -133,14 +147,14 @@
     "def classify_image(image):\n",
     "    prediction, index, probabilities = learner.predict(image)\n",
     "    return dict(zip(categories, map(float, probabilities)))"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -169,23 +183,23 @@
    ],
    "source": [
     "classify_image(PILImage.create('images/example_images/flooded/1.jpeg'))"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "## Intialize attributes for the interface."
-   ],
    "metadata": {
     "collapsed": false
-   }
+   },
+   "source": [
+    "## Intialize attributes for the interface."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "#|export\n",
@@ -202,18 +216,17 @@
     "              \" This model was trained on the ResNet18 architecture and the \" \\\n",
     "              \"fastai library.\" \\\n",
     "              \" Check out the associated blog post with the link below!\"\n",
-    "article = \"<p style='text-align: center; font-size: 36px'><a \" \\\n",
-    "          \"href='https://forbo7.github\" \\\n",
-    "          \".io/ForBlog/fastai/image%20classification/2022/09/12/Detecting\" \\\n",
-    "          \"-Floods-for-Disaster-Relief.html' targets='_blank'>Blog Post</a></p>'\""
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+    "article = \"\"\"\n",
+    "<p style='text-align: center; font-size: 36px'><a href='https://forbo7.github.io/forblog/posts/5_detecting_floods_for_disaster_relief.html'>Blog Post</a></p>\n",
+    "\"\"\""
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -226,40 +239,34 @@
    ],
    "source": [
     "examples"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "## Create the interface."
-   ],
    "metadata": {
     "collapsed": false
-   }
+   },
+   "source": [
+    "## Create the interface."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running on local URL:  http://127.0.0.1:7869\n",
-      "\n",
-      "To create a public link, set `share=True` in `launch()`.\n"
+     "ename": "NameError",
+     "evalue": "name 'gr' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[0;31mNameError\u001B[0m                                 Traceback (most recent call last)",
+      "Cell \u001B[0;32mIn [1], line 4\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[38;5;66;03m#|export\u001B[39;00m\n\u001B[1;32m      2\u001B[0m \n\u001B[1;32m      3\u001B[0m \u001B[38;5;66;03m# Perhaps I can make the interface below with **kwargs?\u001B[39;00m\n\u001B[0;32m----> 4\u001B[0m interface \u001B[38;5;241m=\u001B[39m \u001B[43mgr\u001B[49m\u001B[38;5;241m.\u001B[39mInterface(fn\u001B[38;5;241m=\u001B[39mclassify_image, inputs\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mimage\u001B[39m\u001B[38;5;124m'\u001B[39m, outputs\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mlabel\u001B[39m\u001B[38;5;124m'\u001B[39m,\n\u001B[1;32m      5\u001B[0m                          examples\u001B[38;5;241m=\u001B[39mexamples, title\u001B[38;5;241m=\u001B[39mtitle,\n\u001B[1;32m      6\u001B[0m                          description\u001B[38;5;241m=\u001B[39mdescription, article\u001B[38;5;241m=\u001B[39marticle)\n\u001B[1;32m      7\u001B[0m interface\u001B[38;5;241m.\u001B[39mlaunch(inline\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m, enable_queue\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n",
+      "\u001B[0;31mNameError\u001B[0m: name 'gr' is not defined"
      ]
-    },
-    {
-     "data": {
-      "text/plain": "(<gradio.routes.App at 0x29d4a8040>, 'http://127.0.0.1:7869/', None)"
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -270,55 +277,52 @@
     "                         examples=examples, title=title,\n",
     "                         description=description, article=article)\n",
     "interface.launch(inline=False, enable_queue=True)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "## Export"
-   ],
    "metadata": {
     "collapsed": false
-   }
+   },
+   "source": [
+    "## Export"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "from nbdev.export import nb_export"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "nb_export('app.ipynb', '.')"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
-   "outputs": [],
-   "source": [],
    "metadata": {
     "collapsed": false
-   }
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.10.7 ('salmannaqvi')",
    "language": "python",
    "name": "python3"
   },
@@ -332,7 +336,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "version": "3.10.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e2325d4fde750cacac60ba4c06f149a07b5aad47b4e6bf1c4dca1c7b2184bba3"
+   }
   }
  },
  "nbformat": 4,

app.py

@@ -31,8 +31,9 @@ description = "An image classifier that can tell whether an image is flooded " \
               " This model was trained on the ResNet18 architecture and the " \
               "fastai library." \
               " Check out the associated blog post with the link below!"
-article = "<p style='text-align: center; font-size: 36px'><a href='https://forbo7.github.io/forblog/posts/2_bear_classifier_model.html' " \
-          "targets='_blank'><Blog Post</a></p>"
+article = """
+<p style='text-align: center; font-size: 36px'><a href='https://forbo7.github.io/forblog/posts/5_detecting_floods_for_disaster_relief.html'>Blog Post</a></p>
+"""
 
 # %% app.ipynb 14
 # Perhaps I can make the interface below with **kwargs?