Drone or bird classifier

app.ipynb

@@ -0,0 +1,110 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| default_exp app"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install -Uqq fastbook\n",
+    "!pip install gradio\n",
+    "!pip install nbdev"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "from fastai.vision.all import *\n",
+    "import gradio as gr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "learner = load_learner('model.pkl')\n",
+    "\n",
+    "categories = ('Bird', 'Drone')\n",
+    "\n",
+    "def calssify_images(img):\n",
+    "    pred, idx, probs = learner.predict(img)\n",
+    "    return dict(zip(categories, map(float, probs)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "image = gr.inputs.Image(shape = (192, 192))\n",
+    "label = gr.outputs.Label()\n",
+    "examples = ['BirdExample1.jpg', 'BirdExample2.jpg', 'DroneExample1.jpg', 'DroneExample2.jpg']\n",
+    "\n",
+    "intf = gr.Interface(fn = calssify_images, inputs = image, outputs = label, examples = examples)\n",
+    "intf.launch(inline = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Export successful\n"
+     ]
+    }
+   ],
+   "source": [
+    "import nbdev\n",
+    "nbdev.export.nb_export('app.ipynb', './')\n",
+    "print('Export successful')"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "2376d2b9915f38786098b2b3250c4b9f66c08129e4576f9e739de38b6074d39d"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.8.12 ('datasci-env-py38')",
+   "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": "3.8.12"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

app.py

@@ -1,7 +1,25 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
+
+# %% auto 0
+__all__ = ['learner', 'categories', 'image', 'label', 'examples', 'intf', 'calssify_images']
+
+# %% app.ipynb 2
+from fastai.vision.all import *
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+# %% app.ipynb 3
+learner = load_learner('model.pkl')
+
+categories = ('Bird', 'Drone')
+
+def calssify_images(img):
+    pred, idx, probs = learner.predict(img)
+    return dict(zip(categories, map(float, probs)))
+
+# %% app.ipynb 4
+image = gr.inputs.Image(shape = (192, 192))
+label = gr.outputs.Label()
+examples = ['BirdExample1.jpg', 'BirdExample2.jpg', 'DroneExample1.jpg', 'DroneExample2.jpg']
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+intf = gr.Interface(fn = calssify_images, inputs = image, outputs = label, examples = examples)
+intf.launch(inline = False)