{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ece62dd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|default_exp app"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b4408b5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: gradio in c:\\users\\musa\\anaconda3\\lib\\site-packages (4.19.2)\n",
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      "Requirement already satisfied: httpx>=0.24.1 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from gradio) (0.27.0)\n",
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      "Requirement already satisfied: pandas<3.0,>=1.0 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from gradio) (1.4.2)\n",
      "Requirement already satisfied: matplotlib~=3.0 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from gradio) (3.5.1)\n",
      "Requirement already satisfied: python-multipart>=0.0.9 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from gradio) (0.0.9)\n",
      "Requirement already satisfied: huggingface-hub>=0.19.3 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from gradio) (0.21.3)\n",
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      "Requirement already satisfied: numpy~=1.0 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from gradio) (1.21.5)\n",
      "Requirement already satisfied: uvicorn>=0.14.0 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from gradio) (0.27.1)\n",
      "Requirement already satisfied: typer[all]<1.0,>=0.9 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from gradio) (0.9.0)\n",
      "Requirement already satisfied: fastapi in c:\\users\\musa\\anaconda3\\lib\\site-packages (from gradio) (0.110.0)\n",
      "Requirement already satisfied: semantic-version~=2.0 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from gradio) (2.10.0)\n",
      "Requirement already satisfied: packaging in c:\\users\\musa\\anaconda3\\lib\\site-packages (from gradio) (21.3)\n",
      "Requirement already satisfied: aiofiles<24.0,>=22.0 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from gradio) (23.2.1)\n",
      "Requirement already satisfied: gradio-client==0.10.1 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from gradio) (0.10.1)\n",
      "Requirement already satisfied: fsspec in c:\\users\\musa\\anaconda3\\lib\\site-packages (from gradio-client==0.10.1->gradio) (2024.2.0)\n",
      "Requirement already satisfied: websockets<12.0,>=10.0 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from gradio-client==0.10.1->gradio) (11.0.3)\n",
      "Requirement already satisfied: toolz in c:\\users\\musa\\anaconda3\\lib\\site-packages (from altair<6.0,>=4.2.0->gradio) (0.11.2)\n",
      "Requirement already satisfied: jsonschema>=3.0 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from altair<6.0,>=4.2.0->gradio) (4.4.0)\n",
      "Requirement already satisfied: httpcore==1.* in c:\\users\\musa\\anaconda3\\lib\\site-packages (from httpx>=0.24.1->gradio) (1.0.4)\n",
      "Requirement already satisfied: sniffio in c:\\users\\musa\\anaconda3\\lib\\site-packages (from httpx>=0.24.1->gradio) (1.2.0)\n",
      "Requirement already satisfied: certifi in c:\\users\\musa\\anaconda3\\lib\\site-packages (from httpx>=0.24.1->gradio) (2021.10.8)\n",
      "Requirement already satisfied: anyio in c:\\users\\musa\\anaconda3\\lib\\site-packages (from httpx>=0.24.1->gradio) (3.5.0)\n",
      "Requirement already satisfied: idna in c:\\users\\musa\\anaconda3\\lib\\site-packages (from httpx>=0.24.1->gradio) (3.3)\n",
      "Requirement already satisfied: h11<0.15,>=0.13 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from httpcore==1.*->httpx>=0.24.1->gradio) (0.14.0)\n",
      "Requirement already satisfied: tqdm>=4.42.1 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from huggingface-hub>=0.19.3->gradio) (4.64.0)\n",
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      "Requirement already satisfied: zipp>=3.1.0 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from importlib-resources<7.0,>=1.3->gradio) (3.7.0)\n",
      "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio) (0.18.0)\n",
      "Requirement already satisfied: attrs>=17.4.0 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio) (21.4.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from matplotlib~=3.0->gradio) (2.8.2)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from matplotlib~=3.0->gradio) (0.11.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from matplotlib~=3.0->gradio) (1.3.2)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from matplotlib~=3.0->gradio) (4.25.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from matplotlib~=3.0->gradio) (3.0.4)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from pandas<3.0,>=1.0->gradio) (2021.3)\n",
      "Requirement already satisfied: pydantic-core==2.16.3 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from pydantic>=2.0->gradio) (2.16.3)\n",
      "Requirement already satisfied: annotated-types>=0.4.0 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from pydantic>=2.0->gradio) (0.6.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib~=3.0->gradio) (1.16.0)\n",
      "Requirement already satisfied: colorama in c:\\users\\musa\\anaconda3\\lib\\site-packages (from tqdm>=4.42.1->huggingface-hub>=0.19.3->gradio) (0.4.6)\n",
      "Requirement already satisfied: click<9.0.0,>=7.1.1 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from typer[all]<1.0,>=0.9->gradio) (8.0.4)\n",
      "Requirement already satisfied: shellingham<2.0.0,>=1.3.0 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from typer[all]<1.0,>=0.9->gradio) (1.5.4)\n",
      "Requirement already satisfied: rich<14.0.0,>=10.11.0 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from typer[all]<1.0,>=0.9->gradio) (13.7.1)\n",
      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from rich<14.0.0,>=10.11.0->typer[all]<1.0,>=0.9->gradio) (2.17.2)\n",
      "Requirement already satisfied: markdown-it-py>=2.2.0 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from rich<14.0.0,>=10.11.0->typer[all]<1.0,>=0.9->gradio) (3.0.0)\n",
      "Requirement already satisfied: mdurl~=0.1 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from markdown-it-py>=2.2.0->rich<14.0.0,>=10.11.0->typer[all]<1.0,>=0.9->gradio) (0.1.2)\n",
      "Requirement already satisfied: starlette<0.37.0,>=0.36.3 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from fastapi->gradio) (0.36.3)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from requests->huggingface-hub>=0.19.3->gradio) (2.0.4)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from requests->huggingface-hub>=0.19.3->gradio) (1.26.9)\n"
     ]
    }
   ],
   "source": [
    "!pip install gradio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a31399d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "from fastai.vision.all import*\n",
    "import gradio as gr\n",
    "\n",
    "def is_cat(x): return x[0].isupper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f2e9dfa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "PILImage mode=RGB size=192x108"
      ]
     },
     "execution_count": 17,
     "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"
    },
    {
     "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": [
    "im= PILImage.create('d1.jpg')\n",
    "im.thumbnail((192,192))\n",
    "im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fb9d6c5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pathlib\n",
    "temp = pathlib.PosixPath\n",
    "pathlib.PosixPath = pathlib.WindowsPath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "245a81e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "learn = load_learner('model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "80033fd6",
   "metadata": {},
   "outputs": [
    {
     "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"
    },
    {
     "data": {
      "text/plain": [
       "('True', tensor(1), tensor([1.1735e-11, 1.0000e+00]))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.predict(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a2935c39",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "categories = (\"Dog\", \"Cat\")\n",
    "\n",
    "def classify_image(img):\n",
    "    pred,idx,probs = learn.predict(img)\n",
    "    return dict(zip(categories, map(float,probs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9d635f81",
   "metadata": {},
   "outputs": [
    {
     "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"
    },
    {
     "data": {
      "text/plain": [
       "{'Dog': 1.1735399110812672e-11, 'Cat': 1.0}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classify_image(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "21b18c19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7862\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7862/\" 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": 28,
     "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",
    "image = gr.Image(type='pil')\n",
    "label = gr.Label()\n",
    "examples=['dogt1.jpg','Siberian-Cat.jpg']\n",
    "\n",
    "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
    "intf.launch(inline=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6c08aaf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nbdev.export import nb_export\n",
    "nb_export('deploy.ipynb', '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b80229df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in c:\\users\\musa\\anaconda3\\lib\\site-packages (from packaging->fastcore>=1.5.27->nbdev) (3.0.4)\n",
      "Requirement already satisfied: executing in c:\\users\\musa\\anaconda3\\lib\\site-packages (from stack-data->ipython->execnb>=0.1.4->nbdev) (0.8.3)\n",
      "Requirement already satisfied: pure-eval in c:\\users\\musa\\anaconda3\\lib\\site-packages (from stack-data->ipython->execnb>=0.1.4->nbdev) (0.2.2)\n",
      "Installing collected packages: ghapi, execnb, astunparse, nbdev\n",
      "Successfully installed astunparse-1.6.3 execnb-0.1.5 ghapi-1.0.4 nbdev-2.3.13\n"
     ]
    }
   ],
   "source": [
    "!pip install nbdev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41ce0060",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}