first app built for huggingface

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.ipynb merge=nbdev-merge

.gitconfig

@@ -0,0 +1,11 @@
+# Generated by nbdev_install_hooks
+#
+# If you need to disable this instrumentation do:
+#   git config --local --unset include.path
+#
+# To restore:
+#   git config --local include.path ../.gitconfig
+#
+[merge "nbdev-merge"]
+	name = resolve conflicts with nbdev_fix
+	driver = nbdev_merge %O %A %B %P

app.py

@@ -1,7 +0,0 @@
-import gradio as gr
-
-def greet(name):
-    return "Hello " + name + "!!"
-
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()

main.ipynb

@@ -0,0 +1,322 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "30364f49-507a-474b-ac8c-bc60668a83d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|default_exp main"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "22c0675e-0de0-4e5f-a92b-a903b2f6874a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install -Uqq fastai"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "63fa0ea9-142e-44ed-96fa-751e8dacc3ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: gradio in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (3.16.1)\n",
+      "Requirement already satisfied: pydantic in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from gradio) (1.8.2)\n",
+      "Requirement already satisfied: websockets>=10.0 in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from gradio) (10.4)\n",
+      "Requirement already satisfied: fsspec in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from gradio) (2022.11.0)\n",
+      "Requirement already satisfied: matplotlib in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from gradio) (3.6.2)\n",
+      "Requirement already satisfied: pyyaml in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from gradio) (6.0)\n",
+      "Requirement already satisfied: requests in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from gradio) (2.28.1)\n",
+      "Requirement already satisfied: uvicorn in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from gradio) (0.20.0)\n",
+      "Requirement already satisfied: numpy in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from gradio) (1.23.5)\n",
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+      "Requirement already satisfied: entrypoints in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from altair>=4.2.0->gradio) (0.4)\n",
+      "Requirement already satisfied: jsonschema>=3.0 in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from altair>=4.2.0->gradio) (4.16.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.1 in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from pandas->gradio) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from pandas->gradio) (2022.7)\n",
+      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from aiohttp->gradio) (4.0.2)\n",
+      "Requirement already satisfied: yarl<2.0,>=1.0 in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from aiohttp->gradio) (1.8.2)\n",
+      "Requirement already satisfied: frozenlist>=1.1.1 in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from aiohttp->gradio) (1.3.3)\n",
+      "Requirement already satisfied: attrs>=17.3.0 in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from aiohttp->gradio) (22.1.0)\n",
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+      "Requirement already satisfied: aiosignal>=1.1.2 in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from aiohttp->gradio) (1.3.1)\n",
+      "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from aiohttp->gradio) (2.0.4)\n",
+      "Requirement already satisfied: starlette==0.22.0 in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from fastapi->gradio) (0.22.0)\n",
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+      "Requirement already satisfied: httpcore<0.17.0,>=0.15.0 in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from httpx->gradio) (0.16.3)\n",
+      "Requirement already satisfied: certifi in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from httpx->gradio) (2022.12.7)\n",
+      "Requirement already satisfied: rfc3986[idna2008]<2,>=1.3 in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from httpx->gradio) (1.5.0)\n",
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+      "Requirement already satisfied: mdurl~=0.1 in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from markdown-it-py[linkify,plugins]->gradio) (0.1.2)\n",
+      "Requirement already satisfied: mdit-py-plugins in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from markdown-it-py[linkify,plugins]->gradio) (0.3.3)\n",
+      "Requirement already satisfied: linkify-it-py~=1.0 in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from markdown-it-py[linkify,plugins]->gradio) (1.0.3)\n",
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+      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from matplotlib->gradio) (1.4.4)\n",
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+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from requests->gradio) (1.26.13)\n",
+      "Requirement already satisfied: h11>=0.8 in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from uvicorn->gradio) (0.14.0)\n",
+      "Requirement already satisfied: click>=7.0 in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from uvicorn->gradio) (8.0.4)\n",
+      "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from jsonschema>=3.0->altair>=4.2.0->gradio) (0.18.0)\n",
+      "Requirement already satisfied: uc-micro-py in /opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages (from linkify-it-py~=1.0->markdown-it-py[linkify,plugins]->gradio) (1.0.1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install gradio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "7658cf93-38a8-4f3c-a773-ec7b7777652d",
+   "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": 24,
+   "id": "9c6c2b95-2f0d-4ac5-8d9c-59b420ba8d8e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = untar_data(URLs.PETS)/'images'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "a892c11a-33bc-4947-a7b2-04237df332d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dls = ImageDataLoaders.from_name_func('.',\n",
+    "                                     get_image_files(path), valid_pct=0.2, seed=42,\n",
+    "                                     label_func=is_cat,\n",
+    "                                     item_tfms=Resize(192))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "1a83ccd5-ec38-4f03-b937-fabe5f9c63b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "learn = load_learner('model.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "9a4418cc-4bb4-4e9c-9ae8-0ba1f42e7af7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "categories = ('Dog', 'Cat')\n",
+    "\n",
+    "def classify(img):\n",
+    "    pred,idx,probs = learn.predict(img)\n",
+    "    return dict(zip(categories, map(float, probs)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "79085d0b-8db7-43d4-9da3-0d764c302888",
+   "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': 3.103109945650634e-14, 'Cat': 1.0}"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "classify('cat.jpg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "b1155832-5e29-4f88-aea4-85a6261bedfd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages/gradio/inputs.py:257: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
+      "  warnings.warn(\n",
+      "/opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
+      "  warnings.warn(value)\n",
+      "/opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages/gradio/outputs.py:197: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
+      "  warnings.warn(\n",
+      "/opt/homebrew/Caskroom/miniforge/base/envs/fastai/lib/python3.10/site-packages/gradio/deprecation.py:40: UserWarning: The 'type' parameter has been deprecated. Use the Number component instead.\n",
+      "  warnings.warn(value)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7861\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#|export\n",
+    "image = gr.inputs.Image(shape=(192, 192))\n",
+    "labels = gr.outputs.Label()\n",
+    "examples = ['dog.jpg', 'cat.jpg']\n",
+    "\n",
+    "intf = gr.Interface(fn=classify, inputs=image, outputs=labels, examples=examples)\n",
+    "intf.launch(inline=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "419e727b-5fc0-4a19-94e3-7c9c9c8c22ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Export successful\n"
+     ]
+    }
+   ],
+   "source": [
+    "import nbdev\n",
+    "nbdev.export.nb_export('main.ipynb', '.')\n",
+    "print('Export successful')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b864b212-17a0-4737-a541-1ead7e3c762a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "fastai",
+   "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": "3.10.8"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4b6371f9b180b180e3c0fc2dc539c52593b72b3e75aed3593bd0a5845eb81bd2"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

main.py

@@ -0,0 +1,28 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: main.ipynb.
+
+# %% auto 0
+__all__ = ['learn', 'categories', 'image', 'labels', 'examples', 'intf', 'is_cat', 'classify']
+
+# %% main.ipynb 3
+from fastai.vision.all import *
+import gradio as gr
+
+def is_cat(x): return x[0].isupper()
+
+# %% main.ipynb 6
+learn = load_learner('model.pkl')
+
+# %% main.ipynb 7
+categories = ('Dog', 'Cat')
+
+def classify(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float, probs)))
+
+# %% main.ipynb 9
+image = gr.inputs.Image(shape=(192, 192))
+labels = gr.outputs.Label()
+examples = ['dog.jpg', 'cat.jpg']
+
+intf = gr.Interface(fn=classify, inputs=image, outputs=labels, examples=examples)
+intf.launch(inline=False)

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7824a16eaea51baa955cc628d777947501d66dce80f11f115b12c40df00a9b92
+size 47056529

saving-a-basic-fastai-model.ipynb

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+{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## Saving a Cats v Dogs Model","metadata":{"id":"98d53c05"}},{"cell_type":"markdown","source":"This is a minimal example showing how to train a fastai model on Kaggle, and save it so you can use it in your app.","metadata":{}},{"cell_type":"code","source":"# Make sure we've got the latest version of fastai:\n!pip install -Uqq fastai","metadata":{"id":"evvA0fqvSblq","outputId":"ba21b811-767c-459a-ccdf-044758720a55","_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-05-03T05:51:37.948558Z","iopub.execute_input":"2022-05-03T05:51:37.949032Z","iopub.status.idle":"2022-05-03T05:51:59.531217Z","shell.execute_reply.started":"2022-05-03T05:51:37.948947Z","shell.execute_reply":"2022-05-03T05:51:59.530294Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"First, import all the stuff we need from fastai:","metadata":{}},{"cell_type":"code","source":"from fastai.vision.all import *","metadata":{"id":"44eb0ad3","execution":{"iopub.status.busy":"2022-05-03T05:51:59.533878Z","iopub.execute_input":"2022-05-03T05:51:59.534478Z","iopub.status.idle":"2022-05-03T05:52:02.177975Z","shell.execute_reply.started":"2022-05-03T05:51:59.534432Z","shell.execute_reply":"2022-05-03T05:52:02.177267Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Download and decompress our dataset, which is pictures of dogs and cats:","metadata":{}},{"cell_type":"code","source":"path = untar_data(URLs.PETS)/'images'","metadata":{"execution":{"iopub.status.busy":"2022-05-03T05:52:02.180192Z","iopub.execute_input":"2022-05-03T05:52:02.180691Z","iopub.status.idle":"2022-05-03T05:53:02.465242Z","shell.execute_reply.started":"2022-05-03T05:52:02.180651Z","shell.execute_reply":"2022-05-03T05:53:02.464516Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We need a way to label our images as dogs or cats. In this dataset, pictures of cats are given a filename that starts with a capital letter:","metadata":{}},{"cell_type":"code","source":"def is_cat(x): return x[0].isupper() ","metadata":{"id":"44eb0ad3","execution":{"iopub.status.busy":"2022-05-03T05:53:02.467289Z","iopub.execute_input":"2022-05-03T05:53:02.467572Z","iopub.status.idle":"2022-05-03T05:53:02.474701Z","shell.execute_reply.started":"2022-05-03T05:53:02.467536Z","shell.execute_reply":"2022-05-03T05:53:02.474109Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now we can create our `DataLoaders`:","metadata":{}},{"cell_type":"code","source":"dls = ImageDataLoaders.from_name_func('.',\n    get_image_files(path), valid_pct=0.2, seed=42,\n    label_func=is_cat,\n    item_tfms=Resize(192))","metadata":{"id":"44eb0ad3","execution":{"iopub.status.busy":"2022-05-03T05:53:02.475754Z","iopub.execute_input":"2022-05-03T05:53:02.476084Z","iopub.status.idle":"2022-05-03T05:53:06.703777Z","shell.execute_reply.started":"2022-05-03T05:53:02.476052Z","shell.execute_reply":"2022-05-03T05:53:06.703023Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"... and train our model, a resnet18 (to keep it small and fast):","metadata":{}},{"cell_type":"code","source":"learn = vision_learner(dls, resnet18, metrics=error_rate)\nlearn.fine_tune(3)","metadata":{"id":"c107f724","outputId":"fcc1de68-7c8b-43f5-b9eb-fcdb0773ef07","execution":{"iopub.status.busy":"2022-05-03T05:53:28.092381Z","iopub.execute_input":"2022-05-03T05:53:28.093059Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now we can export our trained `Learner`. This contains all the information needed to run the model:","metadata":{}},{"cell_type":"code","source":"learn.export('model.pkl')","metadata":{"id":"ae2bc6ac","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Finally, open the Kaggle sidebar on the right if it's not already, and find the section marked \"Output\". Open the `/kaggle/working` folder, and you'll see `model.pkl`. Click on it, then click on the menu on the right that appears, and choose \"Download\". After a few seconds, your model will be downloaded to your computer, where you can then create your app that uses the model.","metadata":{"id":"Q2HTrQKTf3BV"}}]}