gradio-set
Browse files- .gradio/certificate.pem +31 -0
- app.py +33 -4
- cat.jpg +0 -0
- cdmodel.pkl +3 -0
- dog.jpg +0 -0
- dogs_cats.ipynb +0 -0
- test.ipynb +300 -0
.gradio/certificate.pem
ADDED
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-----BEGIN CERTIFICATE-----
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+
MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw
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emyPxgcYxn/eR44/KJ4EBs+lVDR3veyJm+kXQ99b21/+jh5Xos1AnX5iItreGCc=
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-----END CERTIFICATE-----
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app.py
CHANGED
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@@ -1,7 +1,36 @@
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import gradio as gr
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def greet(name):
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return "Hello " + name + "!!"
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from fastai.vision.all import *
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import gradio as gr
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def is_cat(x): return x[0].isupper()
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learn = load_learner('cdmodel.pkl')
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categories = ['Dog', 'Cat']
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def classify_image(img):
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pred,idx,probs = learn.predict(img)
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return dict(zip(categories, map(float,probs)))
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image = gr.Image()
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label = gr.Label()
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examples = ['dog.jpg', 'cat.jpg']
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intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
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intf.launch(inline=False, share=True)
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# import gradio as gr
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# def greet(name):
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# return "Hello " + name + "!!"
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# demo = gr.Interface(fn=greet, inputs="text", outputs="text")
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# demo.launch()
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cat.jpg
ADDED
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cdmodel.pkl
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:10a5f4c27e62caafffb02efd047b50c76457de3915c0f1568153278043eda335
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size 47059625
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dog.jpg
ADDED
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dogs_cats.ipynb
ADDED
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The diff for this file is too large to render.
See raw diff
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test.ipynb
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{
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"cells": [
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{
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| 4 |
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"cell_type": "code",
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| 5 |
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"execution_count": null,
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| 6 |
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"id": "56bf159e",
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| 7 |
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"metadata": {},
|
| 8 |
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"outputs": [],
|
| 9 |
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"source": [
|
| 10 |
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"#!default_exp app pip install -Uqq fastai"
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| 11 |
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]
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| 12 |
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},
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| 13 |
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{
|
| 14 |
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"cell_type": "code",
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| 15 |
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"execution_count": 1,
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| 16 |
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"id": "69f7c242",
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| 17 |
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"metadata": {},
|
| 18 |
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"outputs": [
|
| 19 |
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{
|
| 20 |
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"name": "stderr",
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| 21 |
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"output_type": "stream",
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| 22 |
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"text": [
|
| 23 |
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"c:\\Users\\hp\\anaconda3\\envs\\hug\\lib\\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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| 24 |
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" from .autonotebook import tqdm as notebook_tqdm\n"
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| 25 |
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]
|
| 26 |
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}
|
| 27 |
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],
|
| 28 |
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"source": [
|
| 29 |
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"#!export\n",
|
| 30 |
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"from fastai.vision.all import *\n",
|
| 31 |
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"import gradio as gr\n",
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| 32 |
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"\n",
|
| 33 |
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"def is_cat(x): return x[0].isupper() "
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| 34 |
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]
|
| 35 |
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},
|
| 36 |
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{
|
| 37 |
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"cell_type": "code",
|
| 38 |
+
"execution_count": 2,
|
| 39 |
+
"id": "fc58b688",
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| 40 |
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"metadata": {},
|
| 41 |
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"outputs": [],
|
| 42 |
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"source": [
|
| 43 |
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"im = PILImage.create('dog.jpg')\n",
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| 44 |
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"im.thumbnail((192,192))"
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| 45 |
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]
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| 46 |
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},
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| 47 |
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{
|
| 48 |
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"cell_type": "code",
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| 49 |
+
"execution_count": 3,
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| 50 |
+
"id": "8224e371",
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| 51 |
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"metadata": {},
|
| 52 |
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"outputs": [
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| 53 |
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{
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| 54 |
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"data": {
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| 55 |
+
"image/jpeg": 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hqbnXT2MPUAL7xHpltOAYvv4HritxdOt4T+7Ur9DRRTpikSW3zanMCTiOJNo7D71XrOFFkmkAw2RRRS+2V9kZdqNv4VwHje6ltLELERhgc5GaKKyqfEjSHwszfDUEbzxBl4+9+NejWiLgHHNFFb0jCZpFQyDIqFY1jHyjFFFdSOcnjkbHWnPRRViDcaKKKAP/2Q==",
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| 56 |
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",
|
| 57 |
+
"text/plain": [
|
| 58 |
+
"PILImage mode=RGB size=148x148"
|
| 59 |
+
]
|
| 60 |
+
},
|
| 61 |
+
"execution_count": 3,
|
| 62 |
+
"metadata": {},
|
| 63 |
+
"output_type": "execute_result"
|
| 64 |
+
}
|
| 65 |
+
],
|
| 66 |
+
"source": [
|
| 67 |
+
"im"
|
| 68 |
+
]
|
| 69 |
+
},
|
| 70 |
+
{
|
| 71 |
+
"cell_type": "code",
|
| 72 |
+
"execution_count": null,
|
| 73 |
+
"id": "86b6be3c",
|
| 74 |
+
"metadata": {},
|
| 75 |
+
"outputs": [
|
| 76 |
+
{
|
| 77 |
+
"name": "stderr",
|
| 78 |
+
"output_type": "stream",
|
| 79 |
+
"text": [
|
| 80 |
+
"c:\\Users\\hp\\anaconda3\\envs\\hug\\lib\\site-packages\\fastai\\learner.py:455: UserWarning: load_learner` uses Python's insecure pickle module, which can execute malicious arbitrary code when loading. Only load files you trust.\n",
|
| 81 |
+
"If you only need to load model weights and optimizer state, use the safe `Learner.load` instead.\n",
|
| 82 |
+
" warn(\"load_learner` uses Python's insecure pickle module, which can execute malicious arbitrary code when loading. Only load files you trust.\\nIf you only need to load model weights and optimizer state, use the safe `Learner.load` instead.\")\n"
|
| 83 |
+
]
|
| 84 |
+
}
|
| 85 |
+
],
|
| 86 |
+
"source": [
|
| 87 |
+
"#!export \n",
|
| 88 |
+
"learn = load_learner('cdmodel.pkl')"
|
| 89 |
+
]
|
| 90 |
+
},
|
| 91 |
+
{
|
| 92 |
+
"cell_type": "code",
|
| 93 |
+
"execution_count": 5,
|
| 94 |
+
"id": "e6775a56",
|
| 95 |
+
"metadata": {},
|
| 96 |
+
"outputs": [
|
| 97 |
+
{
|
| 98 |
+
"data": {
|
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+
"text/html": [
|
| 100 |
+
"\n",
|
| 101 |
+
"<style>\n",
|
| 102 |
+
" /* Turns off some styling */\n",
|
| 103 |
+
" progress {\n",
|
| 104 |
+
" /* gets rid of default border in Firefox and Opera. */\n",
|
| 105 |
+
" border: none;\n",
|
| 106 |
+
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
|
| 107 |
+
" background-size: auto;\n",
|
| 108 |
+
" }\n",
|
| 109 |
+
" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
|
| 110 |
+
" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
|
| 111 |
+
" }\n",
|
| 112 |
+
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
|
| 113 |
+
" background: #F44336;\n",
|
| 114 |
+
" }\n",
|
| 115 |
+
"</style>\n"
|
| 116 |
+
],
|
| 117 |
+
"text/plain": [
|
| 118 |
+
"<IPython.core.display.HTML object>"
|
| 119 |
+
]
|
| 120 |
+
},
|
| 121 |
+
"metadata": {},
|
| 122 |
+
"output_type": "display_data"
|
| 123 |
+
},
|
| 124 |
+
{
|
| 125 |
+
"data": {
|
| 126 |
+
"text/html": [],
|
| 127 |
+
"text/plain": [
|
| 128 |
+
"<IPython.core.display.HTML object>"
|
| 129 |
+
]
|
| 130 |
+
},
|
| 131 |
+
"metadata": {},
|
| 132 |
+
"output_type": "display_data"
|
| 133 |
+
},
|
| 134 |
+
{
|
| 135 |
+
"data": {
|
| 136 |
+
"text/plain": [
|
| 137 |
+
"('False', tensor(0), tensor([9.9994e-01, 5.5351e-05]))"
|
| 138 |
+
]
|
| 139 |
+
},
|
| 140 |
+
"execution_count": 5,
|
| 141 |
+
"metadata": {},
|
| 142 |
+
"output_type": "execute_result"
|
| 143 |
+
}
|
| 144 |
+
],
|
| 145 |
+
"source": [
|
| 146 |
+
"learn.predict(im)"
|
| 147 |
+
]
|
| 148 |
+
},
|
| 149 |
+
{
|
| 150 |
+
"cell_type": "code",
|
| 151 |
+
"execution_count": 6,
|
| 152 |
+
"id": "d28488a3",
|
| 153 |
+
"metadata": {},
|
| 154 |
+
"outputs": [],
|
| 155 |
+
"source": [
|
| 156 |
+
"#!export\n",
|
| 157 |
+
"\n",
|
| 158 |
+
"categories = ['Dog', 'Cat']\n",
|
| 159 |
+
"\n",
|
| 160 |
+
"def classify_image(img):\n",
|
| 161 |
+
" pred,idx,probs = learn.predict(img)\n",
|
| 162 |
+
" return dict(zip(categories, map(float,probs)))\n"
|
| 163 |
+
]
|
| 164 |
+
},
|
| 165 |
+
{
|
| 166 |
+
"cell_type": "code",
|
| 167 |
+
"execution_count": 7,
|
| 168 |
+
"id": "1d84df2e",
|
| 169 |
+
"metadata": {},
|
| 170 |
+
"outputs": [
|
| 171 |
+
{
|
| 172 |
+
"data": {
|
| 173 |
+
"text/html": [
|
| 174 |
+
"\n",
|
| 175 |
+
"<style>\n",
|
| 176 |
+
" /* Turns off some styling */\n",
|
| 177 |
+
" progress {\n",
|
| 178 |
+
" /* gets rid of default border in Firefox and Opera. */\n",
|
| 179 |
+
" border: none;\n",
|
| 180 |
+
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
|
| 181 |
+
" background-size: auto;\n",
|
| 182 |
+
" }\n",
|
| 183 |
+
" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
|
| 184 |
+
" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
|
| 185 |
+
" }\n",
|
| 186 |
+
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
|
| 187 |
+
" background: #F44336;\n",
|
| 188 |
+
" }\n",
|
| 189 |
+
"</style>\n"
|
| 190 |
+
],
|
| 191 |
+
"text/plain": [
|
| 192 |
+
"<IPython.core.display.HTML object>"
|
| 193 |
+
]
|
| 194 |
+
},
|
| 195 |
+
"metadata": {},
|
| 196 |
+
"output_type": "display_data"
|
| 197 |
+
},
|
| 198 |
+
{
|
| 199 |
+
"data": {
|
| 200 |
+
"text/html": [],
|
| 201 |
+
"text/plain": [
|
| 202 |
+
"<IPython.core.display.HTML object>"
|
| 203 |
+
]
|
| 204 |
+
},
|
| 205 |
+
"metadata": {},
|
| 206 |
+
"output_type": "display_data"
|
| 207 |
+
},
|
| 208 |
+
{
|
| 209 |
+
"data": {
|
| 210 |
+
"text/plain": [
|
| 211 |
+
"{'Dog': 0.9999446868896484, 'Cat': 5.535098898690194e-05}"
|
| 212 |
+
]
|
| 213 |
+
},
|
| 214 |
+
"execution_count": 7,
|
| 215 |
+
"metadata": {},
|
| 216 |
+
"output_type": "execute_result"
|
| 217 |
+
}
|
| 218 |
+
],
|
| 219 |
+
"source": [
|
| 220 |
+
"classify_image(im)"
|
| 221 |
+
]
|
| 222 |
+
},
|
| 223 |
+
{
|
| 224 |
+
"cell_type": "code",
|
| 225 |
+
"execution_count": 8,
|
| 226 |
+
"id": "e7126ab1",
|
| 227 |
+
"metadata": {},
|
| 228 |
+
"outputs": [
|
| 229 |
+
{
|
| 230 |
+
"name": "stdout",
|
| 231 |
+
"output_type": "stream",
|
| 232 |
+
"text": [
|
| 233 |
+
"* Running on local URL: http://127.0.0.1:7860\n",
|
| 234 |
+
"* Running on public URL: https://8efdc1e102025dcc05.gradio.live\n",
|
| 235 |
+
"\n",
|
| 236 |
+
"This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
|
| 237 |
+
]
|
| 238 |
+
},
|
| 239 |
+
{
|
| 240 |
+
"data": {
|
| 241 |
+
"text/plain": []
|
| 242 |
+
},
|
| 243 |
+
"execution_count": 8,
|
| 244 |
+
"metadata": {},
|
| 245 |
+
"output_type": "execute_result"
|
| 246 |
+
}
|
| 247 |
+
],
|
| 248 |
+
"source": [
|
| 249 |
+
"#!export\n",
|
| 250 |
+
"image = gr.Image()\n",
|
| 251 |
+
"label = gr.Label()\n",
|
| 252 |
+
"examples = ['dog.jpg', 'cat.jpg']\n",
|
| 253 |
+
"\n",
|
| 254 |
+
"intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
|
| 255 |
+
"intf.launch(inline=False, share=True)\n"
|
| 256 |
+
]
|
| 257 |
+
},
|
| 258 |
+
{
|
| 259 |
+
"cell_type": "code",
|
| 260 |
+
"execution_count": null,
|
| 261 |
+
"id": "27289cde",
|
| 262 |
+
"metadata": {},
|
| 263 |
+
"outputs": [],
|
| 264 |
+
"source": [
|
| 265 |
+
"#from nbdev.export import notebook2script\n"
|
| 266 |
+
]
|
| 267 |
+
},
|
| 268 |
+
{
|
| 269 |
+
"cell_type": "code",
|
| 270 |
+
"execution_count": null,
|
| 271 |
+
"id": "a51b1312",
|
| 272 |
+
"metadata": {},
|
| 273 |
+
"outputs": [],
|
| 274 |
+
"source": [
|
| 275 |
+
"#notebook2script('test.ipynb')"
|
| 276 |
+
]
|
| 277 |
+
}
|
| 278 |
+
],
|
| 279 |
+
"metadata": {
|
| 280 |
+
"kernelspec": {
|
| 281 |
+
"display_name": "hug",
|
| 282 |
+
"language": "python",
|
| 283 |
+
"name": "python3"
|
| 284 |
+
},
|
| 285 |
+
"language_info": {
|
| 286 |
+
"codemirror_mode": {
|
| 287 |
+
"name": "ipython",
|
| 288 |
+
"version": 3
|
| 289 |
+
},
|
| 290 |
+
"file_extension": ".py",
|
| 291 |
+
"mimetype": "text/x-python",
|
| 292 |
+
"name": "python",
|
| 293 |
+
"nbconvert_exporter": "python",
|
| 294 |
+
"pygments_lexer": "ipython3",
|
| 295 |
+
"version": "3.10.18"
|
| 296 |
+
}
|
| 297 |
+
},
|
| 298 |
+
"nbformat": 4,
|
| 299 |
+
"nbformat_minor": 5
|
| 300 |
+
}
|