fruit recognizer

app.ipynb

@@ -0,0 +1,243 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|default_exp app"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7860\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#|export\n",
+    "# import gradio as gr\n",
+    "# # from fastai.vision.all import vision_learner\n",
+    "\n",
+    "# def greet(val1, val2):\n",
+    "#     return int(val1) + int(val2)\n",
+    "\n",
+    "# iface = gr.Interface(fn=greet, inputs=[\"text\", \"text\"], outputs=\"text\")\n",
+    "# iface.launch(share=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Closing server running on port: 7860\n"
+     ]
+    }
+   ],
+   "source": [
+    "iface.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "from fastai.vision.all import *\n",
+    "\n",
+    "def label_func(fname):\n",
+    "    return fname.name.split('_')[1].split('.')[0].strip()\n",
+    "\n",
+    "learner = load_learner('fruits.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "categories = learner.dls.vocab\n",
+    "\n",
+    "def classify_image(img):\n",
+    "    pred, pred_idx, probs = learner.predict(img)\n",
+    "    return dict(zip(categories, map(float, probs)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7860\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#|export\n",
+    "from re import I\n",
+    "import gradio as gr\n",
+    "\n",
+    "ifc = gr.Interface(fn=classify_image,\n",
+    "                   inputs=gr.Image(type='pil', label='Input Image'),\n",
+    "                   outputs=gr.Label(num_top_classes=6)\n",
+    "    )\n",
+    "ifc.launch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Closing server running on port: 7860\n"
+     ]
+    }
+   ],
+   "source": [
+    "ifc.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from doctest import debug\n",
+    "from doctest import debug\n",
+    "from os import name\n",
+    "import nbdev\n",
+    "# ?nbdev.export.nb_export\n",
+    "!rm app.py\n",
+    "val = nbdev.export.nb_export('./app.ipynb', lib_path='.')\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

app.py

@@ -1,22 +1,39 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
 
 # %% auto 0
-__all__ = ['iface', 'greet']
+__all__ = ['learner', 'categories', 'ifc', 'label_func', 'classify_image']
 
 # %% app.ipynb 1
-import gradio as gr
-# from fastai.vision.all import vision_learner
+# import gradio as gr
+# # from fastai.vision.all import vision_learner
 
-def greet(val1, val2):
-    return int(val1) + int(val2)
+# def greet(val1, val2):
+#     return int(val1) + int(val2)
 
-iface = gr.Interface(fn=greet, inputs=["text", "text"], outputs="text")
-iface.launch(share=False)
+# iface = gr.Interface(fn=greet, inputs=["text", "text"], outputs="text")
+# iface.launch(share=False)
 
 # %% app.ipynb 3
-# from fastai.vision.all import *
+from fastai.vision.all import *
+
+def label_func(fname):
+    return fname.name.split('_')[1].split('.')[0].strip()
+
+learner = load_learner('fruits.pkl')
 
-# def label_func(fname):
-#     return fname.name.split('_')[1].split('.')[0].strip()
+# %% app.ipynb 4
+categories = learner.dls.vocab
+
+def classify_image(img):
+    pred, pred_idx, probs = learner.predict(img)
+    return dict(zip(categories, map(float, probs)))
+
+# %% app.ipynb 5
+from re import I
+import gradio as gr
 
-# learner = load_learner('/mnt/p/lab/ai_env/course22/fruits.pkl')
+ifc = gr.Interface(fn=classify_image,
+                   inputs=gr.Image(type='pil', label='Input Image'),
+                   outputs=gr.Label(num_top_classes=6)
+    )
+ifc.launch()

fruits.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4c81151c9fdd91bcfd3b0202335f0aab57da72cab31db15a9d5bd261272a344f
+size 87485933

requirements.txt

@@ -0,0 +1 @@
+fastai