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Runtime error
Runtime error
Commit ·
0edec7d
1
Parent(s): 271a2eb
First model
Browse files- .gitattributes +1 -0
- .ipynb_checkpoints/app-checkpoint.ipynb +301 -0
- .ipynb_checkpoints/app-checkpoint.py +29 -0
- 2app.py +7 -0
- app.ipynb +301 -0
- app.py +29 -0
- cat.jfif:Zone.Identifier +4 -0
- cat.jpg +0 -0
- cat.jpg:Zone.Identifier +3 -0
- dog.jpg +0 -0
- model.pkl +3 -0
- model.pkl:Zone.Identifier +4 -0
- origin.jpg:Zone.Identifier +4 -0
- practice_model/app.py +29 -0
- requirements.txt +0 -0
.gitattributes
CHANGED
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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model.pkl filter=lfs diff=lfs merge=lfs -text
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.ipynb_checkpoints/app-checkpoint.ipynb
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| 1 |
+
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "be6ef229-f7a7-458b-9275-10d569380a4f",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install -Uqq fastai\n",
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"!pip install -Uqq gradio\n",
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"!pip install -Uqq nbdev"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "a7527112-d0c2-4e8e-b5dd-0cef8dad8954",
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"metadata": {},
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"outputs": [],
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"source": [
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"#|default_exp app"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "8aa9caf6-a26e-4575-85ac-fdd8656d8174",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/doyin/mambaforge/envs/notebook/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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}
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],
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"source": [
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"#|export\n",
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"\n",
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| 43 |
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"from fastai.vision.all import *\n",
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| 44 |
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"import gradio as gr\n",
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"\n",
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"def is_cat(x): return x[0].isupper()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "50a67548-eccc-4225-a8e6-ebc5b4fadb20",
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"metadata": {},
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"outputs": [
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+
{
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"data": {
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"image/jpeg": 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japyR1rRuIC6gqePWkMD4A2hgarl0M+ax0sdpCZ44+QshxxXbW/gfTkiQMxDsmTuPNc1oZUapbkkMI5ARmup8Q3l3M4mUGOMDbx3FLUcYoyF0GKPVjbDDJt3AipTb2S5XOyRc7gals7yQqDD94nG6su+WSTUDk/MTziixql5CXk0UjoIIttdTqGqDw14Bv9Xyolig/dgdC5+Vf1Irm0smWVQ3Q965f4r+IBF4f07w/CxLvL59x7KMhR+eT+FZtroacr6nlUjm4nDTHeWbLMe5J5qlqv72VpcBVDbcAdB2rQMajBPORxVTUwEtmIzkkD6YpGhktEG+v86tWLCUtbyHBxuU+4qqHBAPeiOXbco/YHJ+lSI6OSEQWQQNgt8w+vaoNR1/VpFWGO5KwwIuACBn396kknW5tEdD07VkXUeWznjrTfkMT7a924W7uJQhOW2dTXRaVcRi3WGJWKJkgMcnB9a5nySiBwVOecZ5q/a3xskDKvzPwpqUBuRMGiX0IFe//B29kuvCssDyBvs05QL3AIB/xr51tpRuCEg44xXqnwe1g2PixtPd8R3sTKAf7y/MP03fnVvYTPdHeRJxgKUIx15qZlV1KsAQeoNIyBxginAYGKklJ3I22xRYA4AwAK4DxXo8UrwyF9rMSTmu7nubeCQB5FEhHC55Nc3fRxXsc8l6CqryhHatKZlU10PPpnvLKI2kilQ/LccEe1c3crG95tHQV199Ab+GZzM800fESqvWseLw/rNxcRuNNkJAxgrit1ZamDu9Ctp9tHeTCHIXJwCfWptYsFsbpIBKCUwWK+9aUPgjXyjt9lRCeQC/StPT/h7qE2Xv7iOPPUD5jSul1GoSfQwbCf7PchzxhgeK37nWJL9fJxx0Fcc0rhww655rorGbaTEoUrIAdx7GlJWNIWbNzTnitLdoJMGTORVmG1tknWZzuZyeOuKpSxeUI0ZCT13AVZBCIMQSM/8ACQpNc7vJ6HVFqO6L0klsYcNgHdgV4D45n+3a9POh3r5zIn0XgV7I8N9J8xt5toOclTxXierxJDcSSRtkh5GznOT2ojDlRUp87uY8kp8oHoQMYqrOBPbspPzMcj61Tm1KYttcKQPbBpsd0HwpODRckoMCrEdxTlAJ64qzdQ/vSV545PvVcIV5qREgu5UBRWIXOcUjTZPJJ/GoTSUAT+cFwVH506OV5Jg7880iQGRMj0p0ZVCPagDRtp/36ljgs3Fdhoeqf2T4p0m9yAIJ0dvpnB/TNeexM0lwm3s2RXSTuPOjUn5gBmquM+zwwKgggg8ioZRK8YCOEbPJxnivPvhT4pm1vw99huW33FjhC5PLRn7p/TH4V6ByaEiWrmbc6NbTXiXLFzIvfPWrElskkflyRhk9KnLdicGo5J0iUl5FA+tO7EopEccEcKgRQxoB6Cpck8dKrSahbou4Pu9hVRtXXny4ix9zSuUompj1YmlC+lc43iOVXZWgVCPU1GdQv77BWQRx+q0udFcjNF9F0l5Fc6dASvT5BVuO0s0ACWsK46fIKibUdNU4a9iB6/eFNfVtOjAP2yIgnGd1Xzd2Z8vkX/3YwNiflTg47KB+FZ51jTRwbpOnY1G3iDSozg3S568ClzLuFmZnxB1x9F8I3LRHFxdf6PEfTd1P4DNfO2qYTSvPGP8AWYGPTvXo3xi1+O8fTba0m3RKru3puJAB/LNeZaxdLHpdvbsASY95H1ov2KSOTvYdsu5eVPINVBwc1olxJGY27cj2qi6EMRUgWwwbHfIqNm/hC9KjilMYxgEGrI2svHegCgQc0Ac1YdVB5NM2jqKAJYzi3b1zVfJJIFOZuwNSWkPmyDjNIC/ptqFPmt2FXskyCRv4j0qv54SPy4/u9CalzmBM8HJ6Uxne/DPXZdI8TRorDZdDyW/mP1Fe2T6tN5YZrjaueor5q0CfydesHxnE6ZA7/MK+rl06zReLdSPfmizew+ZLc5mXUisgbzHdCMZGTzVa9lLBCWkZT6Gusn022niKCMJxwV4rIl8PsBgNu+pwTUSjNFRnEoIUjTHmlRjoTkmmSEso+yxM27qc8Van0RkcEg57c1DLMunRqkybozwdtTqtyvO5PZaULk7ryVUYjoB1/GtuLS7aA5UEj68VzTazF5JEcRYEcFecVNba1cwlVVt6norU41ILciSl0Z//2Q==",
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",
|
| 59 |
+
"text/plain": [
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| 60 |
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"PILImage mode=RGB size=192x128"
|
| 61 |
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]
|
| 62 |
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},
|
| 63 |
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"execution_count": 4,
|
| 64 |
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"metadata": {},
|
| 65 |
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"output_type": "execute_result"
|
| 66 |
+
}
|
| 67 |
+
],
|
| 68 |
+
"source": [
|
| 69 |
+
"im = PILImage.create(\"dog.jpg\")\n",
|
| 70 |
+
"im.thumbnail((192, 192))\n",
|
| 71 |
+
"im"
|
| 72 |
+
]
|
| 73 |
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},
|
| 74 |
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{
|
| 75 |
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"cell_type": "code",
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| 76 |
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"execution_count": 5,
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"id": "74013587-8326-449a-9ead-99e03c3bad79",
|
| 78 |
+
"metadata": {},
|
| 79 |
+
"outputs": [],
|
| 80 |
+
"source": [
|
| 81 |
+
"#|export\n",
|
| 82 |
+
"learner = load_learner(\"model.pkl\")"
|
| 83 |
+
]
|
| 84 |
+
},
|
| 85 |
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{
|
| 86 |
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"cell_type": "code",
|
| 87 |
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"execution_count": 6,
|
| 88 |
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"id": "f6c07862-2e08-4e13-895f-b36f55930ee2",
|
| 89 |
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"metadata": {},
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| 90 |
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"outputs": [
|
| 91 |
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{
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| 92 |
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"data": {
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| 93 |
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"text/html": [
|
| 94 |
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"\n",
|
| 95 |
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"<style>\n",
|
| 96 |
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" /* Turns off some styling */\n",
|
| 97 |
+
" progress {\n",
|
| 98 |
+
" /* gets rid of default border in Firefox and Opera. */\n",
|
| 99 |
+
" border: none;\n",
|
| 100 |
+
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
|
| 101 |
+
" background-size: auto;\n",
|
| 102 |
+
" }\n",
|
| 103 |
+
" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
|
| 104 |
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" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
|
| 105 |
+
" }\n",
|
| 106 |
+
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
|
| 107 |
+
" background: #F44336;\n",
|
| 108 |
+
" }\n",
|
| 109 |
+
"</style>\n"
|
| 110 |
+
],
|
| 111 |
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"text/plain": [
|
| 112 |
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"<IPython.core.display.HTML object>"
|
| 113 |
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]
|
| 114 |
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},
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| 115 |
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"metadata": {},
|
| 116 |
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"output_type": "display_data"
|
| 117 |
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},
|
| 118 |
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{
|
| 119 |
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"data": {
|
| 120 |
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"text/html": [],
|
| 121 |
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"text/plain": [
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| 122 |
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"<IPython.core.display.HTML object>"
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| 123 |
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},
|
| 125 |
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"metadata": {},
|
| 126 |
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"output_type": "display_data"
|
| 127 |
+
},
|
| 128 |
+
{
|
| 129 |
+
"data": {
|
| 130 |
+
"text/plain": [
|
| 131 |
+
"('False', TensorImage(0), TensorImage([9.9995e-01, 4.6857e-05]))"
|
| 132 |
+
]
|
| 133 |
+
},
|
| 134 |
+
"execution_count": 6,
|
| 135 |
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"metadata": {},
|
| 136 |
+
"output_type": "execute_result"
|
| 137 |
+
}
|
| 138 |
+
],
|
| 139 |
+
"source": [
|
| 140 |
+
"learner.predict(im)"
|
| 141 |
+
]
|
| 142 |
+
},
|
| 143 |
+
{
|
| 144 |
+
"cell_type": "code",
|
| 145 |
+
"execution_count": 7,
|
| 146 |
+
"id": "200130a4-f80a-468d-814f-59ad7d785ddb",
|
| 147 |
+
"metadata": {},
|
| 148 |
+
"outputs": [],
|
| 149 |
+
"source": [
|
| 150 |
+
"#|export\n",
|
| 151 |
+
"categories = ('Dog', 'Cat')\n",
|
| 152 |
+
"\n",
|
| 153 |
+
"def classify_image(img):\n",
|
| 154 |
+
" pred, idx, probs = learner.predict(img)\n",
|
| 155 |
+
" return dict(zip(categories, map(float, probs)))\n",
|
| 156 |
+
" "
|
| 157 |
+
]
|
| 158 |
+
},
|
| 159 |
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{
|
| 160 |
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"cell_type": "code",
|
| 161 |
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"execution_count": 8,
|
| 162 |
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"id": "017a2950-14ee-4627-9cd1-43d325992142",
|
| 163 |
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"metadata": {},
|
| 164 |
+
"outputs": [
|
| 165 |
+
{
|
| 166 |
+
"data": {
|
| 167 |
+
"text/html": [
|
| 168 |
+
"\n",
|
| 169 |
+
"<style>\n",
|
| 170 |
+
" /* Turns off some styling */\n",
|
| 171 |
+
" progress {\n",
|
| 172 |
+
" /* gets rid of default border in Firefox and Opera. */\n",
|
| 173 |
+
" border: none;\n",
|
| 174 |
+
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
|
| 175 |
+
" background-size: auto;\n",
|
| 176 |
+
" }\n",
|
| 177 |
+
" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
|
| 178 |
+
" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
|
| 179 |
+
" }\n",
|
| 180 |
+
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
|
| 181 |
+
" background: #F44336;\n",
|
| 182 |
+
" }\n",
|
| 183 |
+
"</style>\n"
|
| 184 |
+
],
|
| 185 |
+
"text/plain": [
|
| 186 |
+
"<IPython.core.display.HTML object>"
|
| 187 |
+
]
|
| 188 |
+
},
|
| 189 |
+
"metadata": {},
|
| 190 |
+
"output_type": "display_data"
|
| 191 |
+
},
|
| 192 |
+
{
|
| 193 |
+
"data": {
|
| 194 |
+
"text/html": [],
|
| 195 |
+
"text/plain": [
|
| 196 |
+
"<IPython.core.display.HTML object>"
|
| 197 |
+
]
|
| 198 |
+
},
|
| 199 |
+
"metadata": {},
|
| 200 |
+
"output_type": "display_data"
|
| 201 |
+
},
|
| 202 |
+
{
|
| 203 |
+
"data": {
|
| 204 |
+
"text/plain": [
|
| 205 |
+
"{'Dog': 0.9999531507492065, 'Cat': 4.685666863224469e-05}"
|
| 206 |
+
]
|
| 207 |
+
},
|
| 208 |
+
"execution_count": 8,
|
| 209 |
+
"metadata": {},
|
| 210 |
+
"output_type": "execute_result"
|
| 211 |
+
}
|
| 212 |
+
],
|
| 213 |
+
"source": [
|
| 214 |
+
"classify_image(im)"
|
| 215 |
+
]
|
| 216 |
+
},
|
| 217 |
+
{
|
| 218 |
+
"cell_type": "code",
|
| 219 |
+
"execution_count": 9,
|
| 220 |
+
"id": "5199d03b-2eca-4c97-b619-f0b3a4c043d0",
|
| 221 |
+
"metadata": {},
|
| 222 |
+
"outputs": [
|
| 223 |
+
{
|
| 224 |
+
"name": "stdout",
|
| 225 |
+
"output_type": "stream",
|
| 226 |
+
"text": [
|
| 227 |
+
"Running on local URL: http://127.0.0.1:7860\n",
|
| 228 |
+
"\n",
|
| 229 |
+
"To create a public link, set `share=True` in `launch()`.\n"
|
| 230 |
+
]
|
| 231 |
+
},
|
| 232 |
+
{
|
| 233 |
+
"data": {
|
| 234 |
+
"text/plain": []
|
| 235 |
+
},
|
| 236 |
+
"execution_count": 9,
|
| 237 |
+
"metadata": {},
|
| 238 |
+
"output_type": "execute_result"
|
| 239 |
+
}
|
| 240 |
+
],
|
| 241 |
+
"source": [
|
| 242 |
+
"#|export\n",
|
| 243 |
+
"image = gr.Image(shape=(192, 192))\n",
|
| 244 |
+
"label = gr.Label()\n",
|
| 245 |
+
"examples = ['dog.jpg', 'cat.jpg']\n",
|
| 246 |
+
"\n",
|
| 247 |
+
"intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
|
| 248 |
+
"intf.launch(inline=False)"
|
| 249 |
+
]
|
| 250 |
+
},
|
| 251 |
+
{
|
| 252 |
+
"cell_type": "code",
|
| 253 |
+
"execution_count": 16,
|
| 254 |
+
"id": "fa7a430b-0bd2-475f-abc5-0baf282b4a48",
|
| 255 |
+
"metadata": {},
|
| 256 |
+
"outputs": [],
|
| 257 |
+
"source": [
|
| 258 |
+
"import nbdev"
|
| 259 |
+
]
|
| 260 |
+
},
|
| 261 |
+
{
|
| 262 |
+
"cell_type": "code",
|
| 263 |
+
"execution_count": 20,
|
| 264 |
+
"id": "50b49273-30d7-4ec9-bbd5-f45ff9e5129f",
|
| 265 |
+
"metadata": {},
|
| 266 |
+
"outputs": [],
|
| 267 |
+
"source": [
|
| 268 |
+
"nbdev.export.nb_export('app.ipynb', './')"
|
| 269 |
+
]
|
| 270 |
+
},
|
| 271 |
+
{
|
| 272 |
+
"cell_type": "code",
|
| 273 |
+
"execution_count": null,
|
| 274 |
+
"id": "b43cf307-311f-4285-b347-c023fdb73b30",
|
| 275 |
+
"metadata": {},
|
| 276 |
+
"outputs": [],
|
| 277 |
+
"source": []
|
| 278 |
+
}
|
| 279 |
+
],
|
| 280 |
+
"metadata": {
|
| 281 |
+
"kernelspec": {
|
| 282 |
+
"display_name": "Python [conda env:notebook]",
|
| 283 |
+
"language": "python",
|
| 284 |
+
"name": "conda-env-notebook-py"
|
| 285 |
+
},
|
| 286 |
+
"language_info": {
|
| 287 |
+
"codemirror_mode": {
|
| 288 |
+
"name": "ipython",
|
| 289 |
+
"version": 3
|
| 290 |
+
},
|
| 291 |
+
"file_extension": ".py",
|
| 292 |
+
"mimetype": "text/x-python",
|
| 293 |
+
"name": "python",
|
| 294 |
+
"nbconvert_exporter": "python",
|
| 295 |
+
"pygments_lexer": "ipython3",
|
| 296 |
+
"version": "3.11.5"
|
| 297 |
+
}
|
| 298 |
+
},
|
| 299 |
+
"nbformat": 4,
|
| 300 |
+
"nbformat_minor": 5
|
| 301 |
+
}
|
.ipynb_checkpoints/app-checkpoint.py
ADDED
|
@@ -0,0 +1,29 @@
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
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|
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|
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|
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|
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|
|
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|
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|
|
|
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|
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|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
|
| 2 |
+
|
| 3 |
+
# %% auto 0
|
| 4 |
+
__all__ = ['learner', 'categories', 'image', 'label', 'examples', 'intf', 'is_cat', 'classify_image']
|
| 5 |
+
|
| 6 |
+
# %% app.ipynb 2
|
| 7 |
+
from fastai.vision.all import *
|
| 8 |
+
import gradio as gr
|
| 9 |
+
|
| 10 |
+
def is_cat(x): return x[0].isupper()
|
| 11 |
+
|
| 12 |
+
# %% app.ipynb 4
|
| 13 |
+
learner = load_learner("model.pkl")
|
| 14 |
+
|
| 15 |
+
# %% app.ipynb 6
|
| 16 |
+
categories = ('Dog', 'Cat')
|
| 17 |
+
|
| 18 |
+
def classify_image(img):
|
| 19 |
+
pred, idx, probs = learner.predict(img)
|
| 20 |
+
return dict(zip(categories, map(float, probs)))
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
# %% app.ipynb 8
|
| 24 |
+
image = gr.Image(shape=(192, 192))
|
| 25 |
+
label = gr.Label()
|
| 26 |
+
examples = ['dog.jpg', 'cat.jpg']
|
| 27 |
+
|
| 28 |
+
intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
|
| 29 |
+
intf.launch(inline=False)
|
2app.py
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import gradio as gr
|
| 2 |
+
|
| 3 |
+
def greet(name):
|
| 4 |
+
return "Hello " + name + "!!"
|
| 5 |
+
|
| 6 |
+
iface = gr.Interface(fn=greet, inputs="text", outputs="text")
|
| 7 |
+
iface.launch()
|
app.ipynb
ADDED
|
@@ -0,0 +1,301 @@
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
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|
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|
|
|
|
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|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "code",
|
| 5 |
+
"execution_count": 14,
|
| 6 |
+
"id": "be6ef229-f7a7-458b-9275-10d569380a4f",
|
| 7 |
+
"metadata": {},
|
| 8 |
+
"outputs": [],
|
| 9 |
+
"source": [
|
| 10 |
+
"!pip install -Uqq fastai\n",
|
| 11 |
+
"!pip install -Uqq gradio\n",
|
| 12 |
+
"!pip install -Uqq nbdev"
|
| 13 |
+
]
|
| 14 |
+
},
|
| 15 |
+
{
|
| 16 |
+
"cell_type": "code",
|
| 17 |
+
"execution_count": 2,
|
| 18 |
+
"id": "a7527112-d0c2-4e8e-b5dd-0cef8dad8954",
|
| 19 |
+
"metadata": {},
|
| 20 |
+
"outputs": [],
|
| 21 |
+
"source": [
|
| 22 |
+
"#|default_exp app"
|
| 23 |
+
]
|
| 24 |
+
},
|
| 25 |
+
{
|
| 26 |
+
"cell_type": "code",
|
| 27 |
+
"execution_count": 3,
|
| 28 |
+
"id": "8aa9caf6-a26e-4575-85ac-fdd8656d8174",
|
| 29 |
+
"metadata": {},
|
| 30 |
+
"outputs": [
|
| 31 |
+
{
|
| 32 |
+
"name": "stderr",
|
| 33 |
+
"output_type": "stream",
|
| 34 |
+
"text": [
|
| 35 |
+
"/home/doyin/mambaforge/envs/notebook/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
|
| 36 |
+
" from .autonotebook import tqdm as notebook_tqdm\n"
|
| 37 |
+
]
|
| 38 |
+
}
|
| 39 |
+
],
|
| 40 |
+
"source": [
|
| 41 |
+
"#|export\n",
|
| 42 |
+
"\n",
|
| 43 |
+
"from fastai.vision.all import *\n",
|
| 44 |
+
"import gradio as gr\n",
|
| 45 |
+
"\n",
|
| 46 |
+
"def is_cat(x): return x[0].isupper()"
|
| 47 |
+
]
|
| 48 |
+
},
|
| 49 |
+
{
|
| 50 |
+
"cell_type": "code",
|
| 51 |
+
"execution_count": 4,
|
| 52 |
+
"id": "50a67548-eccc-4225-a8e6-ebc5b4fadb20",
|
| 53 |
+
"metadata": {},
|
| 54 |
+
"outputs": [
|
| 55 |
+
{
|
| 56 |
+
"data": {
|
| 57 |
+
"image/jpeg": 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",
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",
|
| 59 |
+
"text/plain": [
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| 60 |
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"PILImage mode=RGB size=192x128"
|
| 61 |
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]
|
| 62 |
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},
|
| 63 |
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"execution_count": 4,
|
| 64 |
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"metadata": {},
|
| 65 |
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"output_type": "execute_result"
|
| 66 |
+
}
|
| 67 |
+
],
|
| 68 |
+
"source": [
|
| 69 |
+
"im = PILImage.create(\"dog.jpg\")\n",
|
| 70 |
+
"im.thumbnail((192, 192))\n",
|
| 71 |
+
"im"
|
| 72 |
+
]
|
| 73 |
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},
|
| 74 |
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{
|
| 75 |
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"cell_type": "code",
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| 76 |
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"execution_count": 5,
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"id": "74013587-8326-449a-9ead-99e03c3bad79",
|
| 78 |
+
"metadata": {},
|
| 79 |
+
"outputs": [],
|
| 80 |
+
"source": [
|
| 81 |
+
"#|export\n",
|
| 82 |
+
"learner = load_learner(\"model.pkl\")"
|
| 83 |
+
]
|
| 84 |
+
},
|
| 85 |
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{
|
| 86 |
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"cell_type": "code",
|
| 87 |
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"execution_count": 6,
|
| 88 |
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"id": "f6c07862-2e08-4e13-895f-b36f55930ee2",
|
| 89 |
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"metadata": {},
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| 90 |
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"outputs": [
|
| 91 |
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{
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| 92 |
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"data": {
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| 93 |
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"text/html": [
|
| 94 |
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"\n",
|
| 95 |
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"<style>\n",
|
| 96 |
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" /* Turns off some styling */\n",
|
| 97 |
+
" progress {\n",
|
| 98 |
+
" /* gets rid of default border in Firefox and Opera. */\n",
|
| 99 |
+
" border: none;\n",
|
| 100 |
+
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
|
| 101 |
+
" background-size: auto;\n",
|
| 102 |
+
" }\n",
|
| 103 |
+
" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
|
| 104 |
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" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
|
| 105 |
+
" }\n",
|
| 106 |
+
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
|
| 107 |
+
" background: #F44336;\n",
|
| 108 |
+
" }\n",
|
| 109 |
+
"</style>\n"
|
| 110 |
+
],
|
| 111 |
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"text/plain": [
|
| 112 |
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"<IPython.core.display.HTML object>"
|
| 113 |
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]
|
| 114 |
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},
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| 115 |
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"metadata": {},
|
| 116 |
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"output_type": "display_data"
|
| 117 |
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},
|
| 118 |
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{
|
| 119 |
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"data": {
|
| 120 |
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"text/html": [],
|
| 121 |
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"text/plain": [
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| 122 |
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"<IPython.core.display.HTML object>"
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| 123 |
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},
|
| 125 |
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"metadata": {},
|
| 126 |
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"output_type": "display_data"
|
| 127 |
+
},
|
| 128 |
+
{
|
| 129 |
+
"data": {
|
| 130 |
+
"text/plain": [
|
| 131 |
+
"('False', TensorImage(0), TensorImage([9.9995e-01, 4.6857e-05]))"
|
| 132 |
+
]
|
| 133 |
+
},
|
| 134 |
+
"execution_count": 6,
|
| 135 |
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"metadata": {},
|
| 136 |
+
"output_type": "execute_result"
|
| 137 |
+
}
|
| 138 |
+
],
|
| 139 |
+
"source": [
|
| 140 |
+
"learner.predict(im)"
|
| 141 |
+
]
|
| 142 |
+
},
|
| 143 |
+
{
|
| 144 |
+
"cell_type": "code",
|
| 145 |
+
"execution_count": 7,
|
| 146 |
+
"id": "200130a4-f80a-468d-814f-59ad7d785ddb",
|
| 147 |
+
"metadata": {},
|
| 148 |
+
"outputs": [],
|
| 149 |
+
"source": [
|
| 150 |
+
"#|export\n",
|
| 151 |
+
"categories = ('Dog', 'Cat')\n",
|
| 152 |
+
"\n",
|
| 153 |
+
"def classify_image(img):\n",
|
| 154 |
+
" pred, idx, probs = learner.predict(img)\n",
|
| 155 |
+
" return dict(zip(categories, map(float, probs)))\n",
|
| 156 |
+
" "
|
| 157 |
+
]
|
| 158 |
+
},
|
| 159 |
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{
|
| 160 |
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"cell_type": "code",
|
| 161 |
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"execution_count": 8,
|
| 162 |
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"id": "017a2950-14ee-4627-9cd1-43d325992142",
|
| 163 |
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"metadata": {},
|
| 164 |
+
"outputs": [
|
| 165 |
+
{
|
| 166 |
+
"data": {
|
| 167 |
+
"text/html": [
|
| 168 |
+
"\n",
|
| 169 |
+
"<style>\n",
|
| 170 |
+
" /* Turns off some styling */\n",
|
| 171 |
+
" progress {\n",
|
| 172 |
+
" /* gets rid of default border in Firefox and Opera. */\n",
|
| 173 |
+
" border: none;\n",
|
| 174 |
+
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
|
| 175 |
+
" background-size: auto;\n",
|
| 176 |
+
" }\n",
|
| 177 |
+
" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
|
| 178 |
+
" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
|
| 179 |
+
" }\n",
|
| 180 |
+
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
|
| 181 |
+
" background: #F44336;\n",
|
| 182 |
+
" }\n",
|
| 183 |
+
"</style>\n"
|
| 184 |
+
],
|
| 185 |
+
"text/plain": [
|
| 186 |
+
"<IPython.core.display.HTML object>"
|
| 187 |
+
]
|
| 188 |
+
},
|
| 189 |
+
"metadata": {},
|
| 190 |
+
"output_type": "display_data"
|
| 191 |
+
},
|
| 192 |
+
{
|
| 193 |
+
"data": {
|
| 194 |
+
"text/html": [],
|
| 195 |
+
"text/plain": [
|
| 196 |
+
"<IPython.core.display.HTML object>"
|
| 197 |
+
]
|
| 198 |
+
},
|
| 199 |
+
"metadata": {},
|
| 200 |
+
"output_type": "display_data"
|
| 201 |
+
},
|
| 202 |
+
{
|
| 203 |
+
"data": {
|
| 204 |
+
"text/plain": [
|
| 205 |
+
"{'Dog': 0.9999531507492065, 'Cat': 4.685666863224469e-05}"
|
| 206 |
+
]
|
| 207 |
+
},
|
| 208 |
+
"execution_count": 8,
|
| 209 |
+
"metadata": {},
|
| 210 |
+
"output_type": "execute_result"
|
| 211 |
+
}
|
| 212 |
+
],
|
| 213 |
+
"source": [
|
| 214 |
+
"classify_image(im)"
|
| 215 |
+
]
|
| 216 |
+
},
|
| 217 |
+
{
|
| 218 |
+
"cell_type": "code",
|
| 219 |
+
"execution_count": 9,
|
| 220 |
+
"id": "5199d03b-2eca-4c97-b619-f0b3a4c043d0",
|
| 221 |
+
"metadata": {},
|
| 222 |
+
"outputs": [
|
| 223 |
+
{
|
| 224 |
+
"name": "stdout",
|
| 225 |
+
"output_type": "stream",
|
| 226 |
+
"text": [
|
| 227 |
+
"Running on local URL: http://127.0.0.1:7860\n",
|
| 228 |
+
"\n",
|
| 229 |
+
"To create a public link, set `share=True` in `launch()`.\n"
|
| 230 |
+
]
|
| 231 |
+
},
|
| 232 |
+
{
|
| 233 |
+
"data": {
|
| 234 |
+
"text/plain": []
|
| 235 |
+
},
|
| 236 |
+
"execution_count": 9,
|
| 237 |
+
"metadata": {},
|
| 238 |
+
"output_type": "execute_result"
|
| 239 |
+
}
|
| 240 |
+
],
|
| 241 |
+
"source": [
|
| 242 |
+
"#|export\n",
|
| 243 |
+
"image = gr.Image(shape=(192, 192))\n",
|
| 244 |
+
"label = gr.Label()\n",
|
| 245 |
+
"examples = ['dog.jpg', 'cat.jpg']\n",
|
| 246 |
+
"\n",
|
| 247 |
+
"intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
|
| 248 |
+
"intf.launch(inline=False)"
|
| 249 |
+
]
|
| 250 |
+
},
|
| 251 |
+
{
|
| 252 |
+
"cell_type": "code",
|
| 253 |
+
"execution_count": 16,
|
| 254 |
+
"id": "fa7a430b-0bd2-475f-abc5-0baf282b4a48",
|
| 255 |
+
"metadata": {},
|
| 256 |
+
"outputs": [],
|
| 257 |
+
"source": [
|
| 258 |
+
"import nbdev"
|
| 259 |
+
]
|
| 260 |
+
},
|
| 261 |
+
{
|
| 262 |
+
"cell_type": "code",
|
| 263 |
+
"execution_count": 20,
|
| 264 |
+
"id": "50b49273-30d7-4ec9-bbd5-f45ff9e5129f",
|
| 265 |
+
"metadata": {},
|
| 266 |
+
"outputs": [],
|
| 267 |
+
"source": [
|
| 268 |
+
"nbdev.export.nb_export('app.ipynb', './')"
|
| 269 |
+
]
|
| 270 |
+
},
|
| 271 |
+
{
|
| 272 |
+
"cell_type": "code",
|
| 273 |
+
"execution_count": null,
|
| 274 |
+
"id": "b43cf307-311f-4285-b347-c023fdb73b30",
|
| 275 |
+
"metadata": {},
|
| 276 |
+
"outputs": [],
|
| 277 |
+
"source": []
|
| 278 |
+
}
|
| 279 |
+
],
|
| 280 |
+
"metadata": {
|
| 281 |
+
"kernelspec": {
|
| 282 |
+
"display_name": "Python [conda env:notebook]",
|
| 283 |
+
"language": "python",
|
| 284 |
+
"name": "conda-env-notebook-py"
|
| 285 |
+
},
|
| 286 |
+
"language_info": {
|
| 287 |
+
"codemirror_mode": {
|
| 288 |
+
"name": "ipython",
|
| 289 |
+
"version": 3
|
| 290 |
+
},
|
| 291 |
+
"file_extension": ".py",
|
| 292 |
+
"mimetype": "text/x-python",
|
| 293 |
+
"name": "python",
|
| 294 |
+
"nbconvert_exporter": "python",
|
| 295 |
+
"pygments_lexer": "ipython3",
|
| 296 |
+
"version": "3.11.5"
|
| 297 |
+
}
|
| 298 |
+
},
|
| 299 |
+
"nbformat": 4,
|
| 300 |
+
"nbformat_minor": 5
|
| 301 |
+
}
|
app.py
ADDED
|
@@ -0,0 +1,29 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
|
| 2 |
+
|
| 3 |
+
# %% auto 0
|
| 4 |
+
__all__ = ['learner', 'categories', 'image', 'label', 'examples', 'intf', 'is_cat', 'classify_image']
|
| 5 |
+
|
| 6 |
+
# %% app.ipynb 2
|
| 7 |
+
from fastai.vision.all import *
|
| 8 |
+
import gradio as gr
|
| 9 |
+
|
| 10 |
+
def is_cat(x): return x[0].isupper()
|
| 11 |
+
|
| 12 |
+
# %% app.ipynb 4
|
| 13 |
+
learner = load_learner("model.pkl")
|
| 14 |
+
|
| 15 |
+
# %% app.ipynb 6
|
| 16 |
+
categories = ('Dog', 'Cat')
|
| 17 |
+
|
| 18 |
+
def classify_image(img):
|
| 19 |
+
pred, idx, probs = learner.predict(img)
|
| 20 |
+
return dict(zip(categories, map(float, probs)))
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
# %% app.ipynb 8
|
| 24 |
+
image = gr.Image(shape=(192, 192))
|
| 25 |
+
label = gr.Label()
|
| 26 |
+
examples = ['dog.jpg', 'cat.jpg']
|
| 27 |
+
|
| 28 |
+
intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
|
| 29 |
+
intf.launch(inline=False)
|
cat.jfif:Zone.Identifier
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[ZoneTransfer]
|
| 2 |
+
ZoneId=3
|
| 3 |
+
ReferrerUrl=https://www.google.com/
|
| 4 |
+
HostUrl=https://www.google.com/
|
cat.jpg
ADDED
|
cat.jpg:Zone.Identifier
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[ZoneTransfer]
|
| 2 |
+
LastWriterPackageFamilyName=Microsoft.MSPaint_8wekyb3d8bbwe
|
| 3 |
+
ZoneId=3
|
dog.jpg
ADDED
|
model.pkl
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1c09d8f778f89c9eb588eefd9a5ba094eb163cd3ea708a4ea567a83e924f149f
|
| 3 |
+
size 47059947
|
model.pkl:Zone.Identifier
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[ZoneTransfer]
|
| 2 |
+
ZoneId=3
|
| 3 |
+
ReferrerUrl=https://www.kaggle.com/
|
| 4 |
+
HostUrl=https://www.kaggleusercontent.com/kf/94626315/eyJhbGciOiJkaXIiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2In0..YqC8JseUcnoDxnFSVjE21A.MVmsti6q9rF230GGeZZ3B3gIYMyFL_ND8k_ZDKQJ8a_udDlpX2b4ZC3gnCnoV3uKjKmbfIa3N6Tz2_hp3sfOTBYDozyAAa9M3HYGXDRcv-Gdji0s6VkzavodZVuxBbIUnK5qFabv7y2_6YXjU01PvJT6MTEUASFl3YovzUFOxb_BgTt1zhx2qrGnI6gMRj0tJU4F3uidfP8j05zcp62AEIPzGdbYnOFGFGQuiARQcZGI22VDyn9EQHLT-TZkrvR6J0Pmyy6zFh80_ICpbNfx8rYUzKvuTuzPWUEn6MZEGYBJb9I9NcqIvUzPMRv7pSvht_yITk-v3TG4GUvU_8JOk_ad4esfHULviX_iQR3IabL5ZVr1WzzvqWlL1sIl4gIWUCdL0fv_qsVynkANNxH5V4lvxyyKffsmE73qpNnCivut0hidW6B6Ir1kqmyTEhsy1fL_331MyoZK9eohFnYhJt1okLrzSINWfIHUQAHmAeiorGnFjPOYBfCXkXLHXrbUnsTvPcld6ubmQX47ckZ_G7kBqcD6v8s3yk1XceFoVtKKZrgJwqt-fIEUIysYnU7WWQiA30bXZU6R_l4-qFGarWe4C-ktxIr2lLjfmBogrEoqCBd5RG9fqM1-4eLAKTyBaw4dkZ1pqk9KRdKDQKjl8v7hi1QE58112T460XsVkSc.tYRH50OPEsUCwvwPwlH6mQ/model.pkl
|
origin.jpg:Zone.Identifier
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[ZoneTransfer]
|
| 2 |
+
ZoneId=3
|
| 3 |
+
ReferrerUrl=https://www.google.com/
|
| 4 |
+
HostUrl=https://assets.rbl.ms/33297188/origin.jpg
|
practice_model/app.py
ADDED
|
@@ -0,0 +1,29 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# AUTOGENERATED! DO NOT EDIT! File to edit: ../app.ipynb.
|
| 2 |
+
|
| 3 |
+
# %% auto 0
|
| 4 |
+
__all__ = ['learner', 'categories', 'image', 'label', 'examples', 'intf', 'is_cat', 'classify_image']
|
| 5 |
+
|
| 6 |
+
# %% ../app.ipynb 2
|
| 7 |
+
from fastai.vision.all import *
|
| 8 |
+
import gradio as gr
|
| 9 |
+
|
| 10 |
+
def is_cat(x): return x[0].isupper()
|
| 11 |
+
|
| 12 |
+
# %% ../app.ipynb 4
|
| 13 |
+
learner = load_learner("model.pkl")
|
| 14 |
+
|
| 15 |
+
# %% ../app.ipynb 6
|
| 16 |
+
categories = ('Dog', 'Cat')
|
| 17 |
+
|
| 18 |
+
def classify_image(img):
|
| 19 |
+
pred, idx, probs = learner.predict(img)
|
| 20 |
+
return dict(zip(categories, map(float, probs)))
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
# %% ../app.ipynb 8
|
| 24 |
+
image = gr.Image(shape=(192, 192))
|
| 25 |
+
label = gr.Label()
|
| 26 |
+
examples = ['dog.jpg', 'cat.jpg']
|
| 27 |
+
|
| 28 |
+
intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
|
| 29 |
+
intf.launch(inline=False)
|
requirements.txt
ADDED
|
File without changes
|