sycorax/basic

1{2 "cells": [3  {4   "cell_type": "code",5   "execution_count": 2,6   "id": "3dc68167",7   "metadata": {},8   "outputs": [9    {10     "name": "stdout",11     "output_type": "stream",12     "text": [13      "Collecting gradio\n",14      "  Downloading gradio-3.1.1-py3-none-any.whl (5.6 MB)\n",15      "     ---------------------------------------- 5.6/5.6 MB 1.8 MB/s eta 0:00:00\n",16      "Requirement already satisfied: requests in c:\\users\\user\\anaconda3\\envs\\fastbook\\lib\\site-packages (from gradio) (2.28.1)\n",17      "Requirement already satisfied: pandas in c:\\users\\user\\anaconda3\\envs\\fastbook\\lib\\site-packages (from gradio) (1.4.3)\n",18      "Collecting python-multipart\n",19      "  Downloading python-multipart-0.0.5.tar.gz (32 kB)\n",20      "  Preparing metadata (setup.py): started\n",21      "  Preparing metadata (setup.py): finished with status 'done'\n",22      "Collecting orjson\n",23      "  Downloading orjson-3.7.8-cp39-none-win_amd64.whl (199 kB)\n",24   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satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\user\\anaconda3\\envs\\fastbook\\lib\\site-packages (from requests->gradio) (1.26.9)\n",77      "Collecting starlette==0.19.1\n",78      "  Downloading starlette-0.19.1-py3-none-any.whl (63 kB)\n",79      "     ---------------------------------------- 63.3/63.3 kB 1.7 MB/s eta 0:00:00\n",80      "Requirement already satisfied: typing-extensions>=3.10.0 in c:\\users\\user\\anaconda3\\envs\\fastbook\\lib\\site-packages (from starlette==0.19.1->fastapi->gradio) (4.1.1)\n",81      "Collecting anyio<5,>=3.4.0\n",82      "  Downloading anyio-3.6.1-py3-none-any.whl (80 kB)\n",83      "     ---------------------------------------- 80.6/80.6 kB 1.5 MB/s eta 0:00:00\n",84      "Collecting httpcore<0.16.0,>=0.15.0\n",85      "  Downloading httpcore-0.15.0-py3-none-any.whl (68 kB)\n",86      "     ---------------------------------------- 68.4/68.4 kB 1.9 MB/s eta 0:00:00\n",87      "Collecting sniffio\n",88      "  Downloading 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in c:\\users\\user\\anaconda3\\envs\\fastbook\\lib\\site-packages (from paramiko->gradio) (37.0.1)\n",109      "Collecting bcrypt>=3.1.3\n",110      "  Downloading bcrypt-3.2.2-cp36-abi3-win_amd64.whl (29 kB)\n",111      "Requirement already satisfied: click>=7.0 in c:\\users\\user\\anaconda3\\envs\\fastbook\\lib\\site-packages (from uvicorn->gradio) (8.0.4)\n",112      "Requirement already satisfied: cffi>=1.1 in c:\\users\\user\\anaconda3\\envs\\fastbook\\lib\\site-packages (from bcrypt>=3.1.3->paramiko->gradio) (1.15.0)\n",113      "Requirement already satisfied: colorama in c:\\users\\user\\anaconda3\\envs\\fastbook\\lib\\site-packages (from click>=7.0->uvicorn->gradio) (0.4.5)\n",114      "Collecting uc-micro-py\n",115      "  Downloading uc_micro_py-1.0.1-py3-none-any.whl (6.2 kB)\n",116      "Requirement already satisfied: pycparser in c:\\users\\user\\anaconda3\\envs\\fastbook\\lib\\site-packages (from cffi>=1.1->bcrypt>=3.1.3->paramiko->gradio) (2.21)\n",117      "Building wheels for collected packages: ffmpy, python-multipart\n",118      "  Building wheel for ffmpy (setup.py): started\n",119      "  Building wheel for ffmpy (setup.py): finished with status 'done'\n",120      "  Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4712 sha256=1fe71da8c013b00dabcc5ce91736a7cbae5d11e5cffd14952f4d98bb3bb6c3ec\n",121      "  Stored in directory: c:\\users\\user\\appdata\\local\\pip\\cache\\wheels\\91\\e2\\96\\f676aa08bfd789328c6576cd0f1fde4a3d686703bb0c247697\n",122      "  Building wheel for python-multipart (setup.py): started\n",123      "  Building wheel for python-multipart (setup.py): finished with status 'done'\n",124      "  Created wheel for python-multipart: filename=python_multipart-0.0.5-py3-none-any.whl size=31678 sha256=1345045cf932f12fad196ca3548da6d1428f3b7faf10e0616525fbad5c6ab381\n",125      "  Stored in directory: c:\\users\\user\\appdata\\local\\pip\\cache\\wheels\\fe\\04\\d1\\a10661cc45f03c3cecda50deb2d2c22f57b4e84a75b2a5987e\n",126      "Successfully built ffmpy python-multipart\n",127      "Installing collected packages: rfc3986, pydub, monotonic, ffmpy, uc-micro-py, sniffio, python-multipart, pycryptodome, orjson, mdurl, h11, backoff, uvicorn, pynacl, markdown-it-py, linkify-it-py, bcrypt, anyio, analytics-python, starlette, paramiko, mdit-py-plugins, httpcore, httpx, fastapi, gradio\n",128      "Successfully installed analytics-python-1.4.0 anyio-3.6.1 backoff-1.10.0 bcrypt-3.2.2 fastapi-0.79.0 ffmpy-0.3.0 gradio-3.1.1 h11-0.12.0 httpcore-0.15.0 httpx-0.23.0 linkify-it-py-1.0.3 markdown-it-py-2.1.0 mdit-py-plugins-0.3.0 mdurl-0.1.1 monotonic-1.6 orjson-3.7.8 paramiko-2.11.0 pycryptodome-3.15.0 pydub-0.25.1 pynacl-1.5.0 python-multipart-0.0.5 rfc3986-1.5.0 sniffio-1.2.0 starlette-0.19.1 uc-micro-py-1.0.1 uvicorn-0.18.2\n"129     ]130    }131   ],132   "source": [133    "#|export\n",134    "from fastai.vision.all import *\n",135    "import gradio as gr"136   ]137  },138  {139   "cell_type": "code",140   "execution_count": 3,141   "id": "194cb52e",142   "metadata": {},143   "outputs": [],144   "source": [145    "def is_cat(x): return x[0].isupper()"146   ]147  },148  {149   "cell_type": "code",150   "execution_count": 5,151   "id": "29a42adf",152   "metadata": {},153   "outputs": [154    {155     "data": {156      "image/png": 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\n",157      "text/plain": [158       "PILImage mode=RGB size=192x128"159      ]160     },161     "execution_count": 5,162     "metadata": {},163     "output_type": "execute_result"164    }165   ],166   "source": [167    "im = PILImage.create('dog.jpg')\n",168    "im.thumbnail((192, 192))\n",169    "im"170   ]171  },172  {173   "cell_type": "code",174   "execution_count": 6,175   "id": "fb2f72ad",176   "metadata": {},177   "outputs": [],178   "source": [179    "#|export\n",180    "learn = load_learner('model.pkl')"181   ]182  },183  {184   "cell_type": "code",185   "execution_count": 8,186   "id": "8b6dd9ea",187   "metadata": {},188   "outputs": [189    {190     "data": {191      "text/html": [192       "\n",193       "<style>\n",194       "    /* Turns off some styling */\n",195       "    progress {\n",196       "        /* gets rid of default border in Firefox and Opera. */\n",197       "        border: none;\n",198       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",199       "        background-size: auto;\n",200       "    }\n",201       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",202       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",203       "    }\n",204       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",205       "        background: #F44336;\n",206       "    }\n",207       "</style>\n"208      ],209      "text/plain": [210       "<IPython.core.display.HTML object>"211      ]212     },213     "metadata": {},214     "output_type": "display_data"215    },216    {217     "data": {218      "text/html": [],219      "text/plain": [220       "<IPython.core.display.HTML object>"221      ]222     },223     "metadata": {},224     "output_type": "display_data"225    },226    {227     "name": "stdout",228     "output_type": "stream",229     "text": [230      "CPU times: total: 391 ms\n",231      "Wall time: 48.8 ms\n"232     ]233    },234    {235     "data": {236      "text/plain": [237       "('False', TensorBase(0), TensorBase([1.0000e+00, 3.9524e-07]))"238      ]239     },240     "execution_count": 8,241     "metadata": {},242     "output_type": "execute_result"243    }244   ],245   "source": [246    "%time learn.predict(im)"247   ]248  },249  {250   "cell_type": "code",251   "execution_count": 9,252   "id": "92f1ca82",253   "metadata": {},254   "outputs": [],255   "source": [256    "#|export\n",257    "categories = ('Dog', 'Cat')\n",258    "\n",259    "def classify_image(img):\n",260    "    pred, idx, probs = learn.predict(img)\n",261    "    return dict(zip(categories, map(float, probs)))"262   ]263  },264  {265   "cell_type": "code",266   "execution_count": 15,267   "id": "7240f81a",268   "metadata": {},269   "outputs": [270    {271     "data": {272      "text/html": [273       "\n",274       "<style>\n",275       "    /* Turns off some styling */\n",276       "    progress {\n",277       "        /* gets rid of default border in Firefox and Opera. */\n",278       "        border: none;\n",279       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",280       "        background-size: auto;\n",281       "    }\n",282       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",283       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",284       "    }\n",285       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",286       "        background: #F44336;\n",287       "    }\n",288       "</style>\n"289      ],290      "text/plain": [291       "<IPython.core.display.HTML object>"292      ]293     },294     "metadata": {},295     "output_type": "display_data"296    },297    {298     "data": {299      "text/html": [],300      "text/plain": [301       "<IPython.core.display.HTML object>"302      ]303     },304     "metadata": {},305     "output_type": "display_data"306    },307    {308     "data": {309      "text/plain": [310       "{'Dog': 0.9999996423721313, 'Cat': 3.952414147079253e-07}"311      ]312     },313     "execution_count": 15,314     "metadata": {},315     "output_type": "execute_result"316    }317   ],318   "source": [319    "classify_image(im)\n",320    "# classify_image(PILImage.create('dunno.jpg'))"321   ]322  },323  {324   "cell_type": "code",325   "execution_count": 17,326   "id": "0c76751e",327   "metadata": {},328   "outputs": [329    {330     "name": "stderr",331     "output_type": "stream",332     "text": [333      "C:\\Users\\User\\anaconda3\\envs\\fastbook\\lib\\site-packages\\gradio\\outputs.py:196: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",334      "  warnings.warn(\n",335      "C:\\Users\\User\\anaconda3\\envs\\fastbook\\lib\\site-packages\\gradio\\deprecation.py:40: UserWarning: The 'type' parameter has been deprecated. Use the Number component instead.\n",336      "  warnings.warn(value)\n"337     ]338    },339    {340     "name": "stdout",341     "output_type": "stream",342     "text": [343      "Running on local URL:  http://127.0.0.1:7860/\n",344      "\n",345      "To create a public link, set `share=True` in `launch()`.\n"346     ]347    },348    {349     "data": {350      "text/plain": [351       "(<gradio.routes.App at 0x139361ef430>, 'http://127.0.0.1:7860/', None)"352      ]353     },354     "execution_count": 17,355     "metadata": {},356     "output_type": "execute_result"357    }358   ],359   "source": [360    "#|export\n",361    "image = gr.inputs.Image(shape=(192, 192))\n",362    "label = gr.outputs.Label()\n",363    "examples = ['dog.jpg', 'cat.jpg', 'dunno.jpg']\n",364    "\n",365    "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",366    "intf.launch(inline=False)"367   ]368  },369  {370   "cell_type": "code",371   "execution_count": 30,372   "id": "9fcdbb3c",373   "metadata": {},374   "outputs": [375    {376     "name": "stdout",377     "output_type": "stream",378     "text": [379      "Requirement already satisfied: notebook2script in c:\\users\\user\\anaconda3\\envs\\fastbook\\lib\\site-packages (0.2.1)\n",380      "Requirement already satisfied: nbconvert>=5.6.1 in c:\\users\\user\\anaconda3\\envs\\fastbook\\lib\\site-packages (from notebook2script) (6.4.4)\n",381      "Requirement already satisfied: consolekit>=0.6.0 in c:\\users\\user\\anaconda3\\envs\\fastbook\\lib\\site-packages (from notebook2script) (1.4.1)\n",382      "Requirement already satisfied: ipython>=7.14.0 in c:\\users\\user\\anaconda3\\envs\\fastbook\\lib\\site-packages (from notebook2script) (8.4.0)\n",383      "Requirement already satisfied: domdf-python-tools>=2.8.1 in c:\\users\\user\\anaconda3\\envs\\fastbook\\lib\\site-packages (from notebook2script) (3.3.0)\n",384      "Requirement already satisfied: astroid<=2.5,>=2.4.0 in c:\\users\\user\\anaconda3\\envs\\fastbook\\lib\\site-packages (from notebook2script) (2.5)\n",385      "Requirement already satisfied: pylint>=2.5.2 in c:\\users\\user\\anaconda3\\envs\\fastbook\\lib\\site-packages (from notebook2script) (2.7.1)\n",386      "Requirement already satisfied: pre-commit-hooks>=3.3.0 in c:\\users\\user\\anaconda3\\envs\\fastbook\\lib\\site-packages (from notebook2script) (4.3.0)\n",387      "Requirement already satisfied: isort>=5.5.2 in c:\\users\\user\\anaconda3\\envs\\fastbook\\lib\\site-packages (from notebook2script) (5.6.4)\n",388      "Requirement already satisfied: yapf-isort>=0.5.5 in c:\\users\\user\\anaconda3\\envs\\fastbook\\lib\\site-packages (from notebook2script) (0.6.0)\n",389      "Requirement already satisfied: click>=7.1.2 in c:\\users\\user\\anaconda3\\envs\\fastbook\\lib\\site-packages (from notebook2script) (8.0.4)\n",390      "Requirement already satisfied: wrapt<1.13,>=1.11 in c:\\users\\user\\anaconda3\\envs\\fastbook\\lib\\site-packages (from astroid<=2.5,>=2.4.0->notebook2script) (1.12.1)\n",391      "Requirement already satisfied: lazy-object-proxy>=1.4.0 in c:\\users\\user\\anaconda3\\envs\\fastbook\\lib\\site-packages (from astroid<=2.5,>=2.4.0->notebook2script) (1.7.1)\n",392      "Requirement already satisfied: colorama in c:\\users\\user\\anaconda3\\envs\\fastbook\\lib\\site-packages (from click>=7.1.2->notebook2script) (0.4.5)"393     ]394    }395   ],396   "source": [397    "!pip install notebook2script"398   ]399  },400  {401   "cell_type": "code",402   "execution_count": 36,403   "id": "62aece67",404   "metadata": {},405   "outputs": [],406   "source": [407    "from notebook2script import *"408   ]409  },410  {411   "cell_type": "code",412   "execution_count": 39,413   "id": "0464714e",414   "metadata": {},415   "outputs": [],416   "source": [417    "notebook2script.convert_notebook('app.ipynb', 'app.py')"418   ]419  },420  {421   "cell_type": "code",422   "execution_count": null,423   "id": "c18e140b",424   "metadata": {},425   "outputs": [],426   "source": []427  }428 ],429 "metadata": {430  "kernelspec": {431   "display_name": "Python 3 (ipykernel)",432   "language": "python",433   "name": "python3"434  },435  "language_info": {436   "codemirror_mode": {437    "name": "ipython",438    "version": 3439   },440   "file_extension": ".py",441   "mimetype": "text/x-python",442   "name": "python",443   "nbconvert_exporter": "python",444   "pygments_lexer": "ipython3",445   "version": "3.9.12"446  },447  "toc": {448   "base_numbering": 1,449   "nav_menu": {},450   "number_sections": true,451   "sideBar": true,452   "skip_h1_title": false,453   "title_cell": "Table of Contents",454   "title_sidebar": "Contents",455   "toc_cell": false,456   "toc_position": {},457   "toc_section_display": true,458   "toc_window_display": false459  },460  "varInspector": {461   "cols": {462    "lenName": 16,463    "lenType": 16,464    "lenVar": 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