zacpross commited on
Commit
43003ed
1 Parent(s): 0422a0a
Files changed (3) hide show
  1. README.md +13 -13
  2. app.ipynb +43 -38
  3. app.py +4 -4
README.md CHANGED
@@ -1,13 +1,13 @@
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- ---
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- title: Exercise_classifier
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- emoji: 🦀
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- colorFrom: red
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- colorTo: red
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- sdk: gradio
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- sdk_version: 2.9.4
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- app_file: app.py
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- pinned: false
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- license: apache-2.0
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- ---
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-
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- Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
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+ ---
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+ title: Exercise_classifier
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+ emoji: 🦀
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+ colorFrom: red
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+ colorTo: red
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+ sdk: gradio
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+ sdk_version: 2.9.4
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+ app_file: app.py
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+ pinned: false
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+ license: apache-2.0
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+ ---
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+
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+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
app.ipynb CHANGED
@@ -2,7 +2,7 @@
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  "cells": [
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  {
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  "cell_type": "code",
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- "execution_count": 26,
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  "id": "dcad9070",
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  "metadata": {},
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  "outputs": [],
@@ -12,42 +12,29 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": null,
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- "id": "af9d37c9",
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "#pip install fastai"
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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": 28,
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  "id": "b0d51b0e",
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  "metadata": {},
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  "outputs": [],
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  "source": [
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  "from fastai.vision.all import *\n",
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- "import gradio as gr\n",
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- "import pathlib\n",
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- "temp = pathlib.PosixPath\n",
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- "pathlib.PosixPath = pathlib.WindowsPath"
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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": 29,
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  "id": "0253652f",
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  "metadata": {},
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  "outputs": [
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  {
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  "data": {
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- "image/png": 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\n",
46
  "text/plain": [
47
  "PILImage mode=RGB size=192x128"
48
  ]
49
  },
50
- "execution_count": 29,
51
  "metadata": {},
52
  "output_type": "execute_result"
53
  }
@@ -60,7 +47,7 @@
60
  },
61
  {
62
  "cell_type": "code",
63
- "execution_count": 30,
64
  "id": "daa84358",
65
  "metadata": {},
66
  "outputs": [],
@@ -71,7 +58,7 @@
71
  },
72
  {
73
  "cell_type": "code",
74
- "execution_count": 31,
75
  "id": "11d1aebe",
76
  "metadata": {},
77
  "outputs": [],
@@ -86,7 +73,7 @@
86
  },
87
  {
88
  "cell_type": "code",
89
- "execution_count": 32,
90
  "id": "3a193e0a",
91
  "metadata": {},
92
  "outputs": [
@@ -127,15 +114,15 @@
127
  {
128
  "data": {
129
  "text/plain": [
130
- "{'person bench pressing': 0.24069958925247192,\n",
131
- " 'person overhead pressing': 0.6591660380363464,\n",
132
- " 'person deadlifting': 0.003008878091350198,\n",
133
- " 'person lateral raising': 0.019873108714818954,\n",
134
- " 'person bent over row': 0.04267517849802971,\n",
135
- " 'person barbell squatting': 0.03457728773355484}"
136
  ]
137
  },
138
- "execution_count": 32,
139
  "metadata": {},
140
  "output_type": "execute_result"
141
  }
@@ -146,7 +133,7 @@
146
  },
147
  {
148
  "cell_type": "code",
149
- "execution_count": 33,
150
  "id": "2789ad64",
151
  "metadata": {},
152
  "outputs": [
@@ -154,7 +141,7 @@
154
  "name": "stdout",
155
  "output_type": "stream",
156
  "text": [
157
- "Running on local URL: http://127.0.0.1:7862/\n",
158
  "\n",
159
  "To create a public link, set `share=True` in `launch()`.\n"
160
  ]
@@ -162,12 +149,12 @@
162
  {
163
  "data": {
164
  "text/plain": [
165
- "(<fastapi.applications.FastAPI at 0x1685e221c40>,\n",
166
- " 'http://127.0.0.1:7862/',\n",
167
  " None)"
168
  ]
169
  },
170
- "execution_count": 33,
171
  "metadata": {},
172
  "output_type": "execute_result"
173
  }
@@ -184,7 +171,7 @@
184
  },
185
  {
186
  "cell_type": "code",
187
- "execution_count": 34,
188
  "id": "06dd273a",
189
  "metadata": {},
190
  "outputs": [],
@@ -195,10 +182,25 @@
195
  },
196
  {
197
  "cell_type": "code",
198
- "execution_count": 35,
199
  "id": "fe3628e5",
200
  "metadata": {},
201
- "outputs": [],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
202
  "source": [
203
  "notebook2script('app.pynb')"
204
  ]
@@ -213,8 +215,11 @@
213
  }
214
  ],
215
  "metadata": {
 
 
 
216
  "kernelspec": {
217
- "display_name": "Python 3 (ipykernel)",
218
  "language": "python",
219
  "name": "python3"
220
  },
2
  "cells": [
3
  {
4
  "cell_type": "code",
5
+ "execution_count": 32,
6
  "id": "dcad9070",
7
  "metadata": {},
8
  "outputs": [],
12
  },
13
  {
14
  "cell_type": "code",
15
+ "execution_count": 33,
 
 
 
 
 
 
 
 
 
 
16
  "id": "b0d51b0e",
17
  "metadata": {},
18
  "outputs": [],
19
  "source": [
20
  "from fastai.vision.all import *\n",
21
+ "import gradio as gr"
 
 
 
22
  ]
23
  },
24
  {
25
  "cell_type": "code",
26
+ "execution_count": 34,
27
  "id": "0253652f",
28
  "metadata": {},
29
  "outputs": [
30
  {
31
  "data": {
32
+ "image/png": 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",
33
  "text/plain": [
34
  "PILImage mode=RGB size=192x128"
35
  ]
36
  },
37
+ "execution_count": 34,
38
  "metadata": {},
39
  "output_type": "execute_result"
40
  }
47
  },
48
  {
49
  "cell_type": "code",
50
+ "execution_count": 35,
51
  "id": "daa84358",
52
  "metadata": {},
53
  "outputs": [],
58
  },
59
  {
60
  "cell_type": "code",
61
+ "execution_count": 36,
62
  "id": "11d1aebe",
63
  "metadata": {},
64
  "outputs": [],
73
  },
74
  {
75
  "cell_type": "code",
76
+ "execution_count": 37,
77
  "id": "3a193e0a",
78
  "metadata": {},
79
  "outputs": [
114
  {
115
  "data": {
116
  "text/plain": [
117
+ "{'person bench pressing': 0.2406989336013794,\n",
118
+ " 'person overhead pressing': 0.6591675281524658,\n",
119
+ " 'person deadlifting': 0.003008909523487091,\n",
120
+ " 'person lateral raising': 0.019873008131980896,\n",
121
+ " 'person bent over row': 0.04267449676990509,\n",
122
+ " 'person barbell squatting': 0.0345771498978138}"
123
  ]
124
  },
125
+ "execution_count": 37,
126
  "metadata": {},
127
  "output_type": "execute_result"
128
  }
133
  },
134
  {
135
  "cell_type": "code",
136
+ "execution_count": 38,
137
  "id": "2789ad64",
138
  "metadata": {},
139
  "outputs": [
141
  "name": "stdout",
142
  "output_type": "stream",
143
  "text": [
144
+ "Running on local URL: http://127.0.0.1:7863/\n",
145
  "\n",
146
  "To create a public link, set `share=True` in `launch()`.\n"
147
  ]
149
  {
150
  "data": {
151
  "text/plain": [
152
+ "(<fastapi.applications.FastAPI at 0x7f525a802370>,\n",
153
+ " 'http://127.0.0.1:7863/',\n",
154
  " None)"
155
  ]
156
  },
157
+ "execution_count": 38,
158
  "metadata": {},
159
  "output_type": "execute_result"
160
  }
171
  },
172
  {
173
  "cell_type": "code",
174
+ "execution_count": 39,
175
  "id": "06dd273a",
176
  "metadata": {},
177
  "outputs": [],
182
  },
183
  {
184
  "cell_type": "code",
185
+ "execution_count": 41,
186
  "id": "fe3628e5",
187
  "metadata": {},
188
+ "outputs": [
189
+ {
190
+ "ename": "FileNotFoundError",
191
+ "evalue": "[Errno 2] No such file or directory: '/mnt/z/Documents1/Repositories/exercise_classifier/{lib_name}/_nbdev.py'",
192
+ "output_type": "error",
193
+ "traceback": [
194
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
195
+ "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
196
+ "\u001b[1;32m/mnt/z/Documents1/Repositories/exercise_classifier/app.ipynb Cell 9'\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell://wsl%2Bubuntu-20.04/mnt/z/Documents1/Repositories/exercise_classifier/app.ipynb#ch0000008vscode-remote?line=0'>1</a>\u001b[0m notebook2script(\u001b[39m'\u001b[39;49m\u001b[39mapp.ipynb\u001b[39;49m\u001b[39m'\u001b[39;49m)\n",
197
+ "File \u001b[0;32m~/mambaforge/lib/python3.9/site-packages/nbdev/export.py:441\u001b[0m, in \u001b[0;36mnotebook2script\u001b[0;34m(fname, silent, to_dict, bare)\u001b[0m\n\u001b[1;32m <a href='file:///home/zac-ubuntu-wsl/mambaforge/lib/python3.9/site-packages/nbdev/export.py?line=438'>439</a>\u001b[0m d \u001b[39m=\u001b[39m collections\u001b[39m.\u001b[39mdefaultdict(\u001b[39mlist\u001b[39m) \u001b[39mif\u001b[39;00m to_dict \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m <a href='file:///home/zac-ubuntu-wsl/mambaforge/lib/python3.9/site-packages/nbdev/export.py?line=439'>440</a>\u001b[0m modules \u001b[39m=\u001b[39m create_mod_files(files, to_dict, bare\u001b[39m=\u001b[39mbare)\n\u001b[0;32m--> <a href='file:///home/zac-ubuntu-wsl/mambaforge/lib/python3.9/site-packages/nbdev/export.py?line=440'>441</a>\u001b[0m \u001b[39mfor\u001b[39;00m f \u001b[39min\u001b[39;00m \u001b[39msorted\u001b[39m(files): d \u001b[39m=\u001b[39m _notebook2script(f, modules, silent\u001b[39m=\u001b[39;49msilent, to_dict\u001b[39m=\u001b[39;49md, bare\u001b[39m=\u001b[39;49mbare)\n\u001b[1;32m <a href='file:///home/zac-ubuntu-wsl/mambaforge/lib/python3.9/site-packages/nbdev/export.py?line=441'>442</a>\u001b[0m \u001b[39mif\u001b[39;00m to_dict: \u001b[39mreturn\u001b[39;00m d\n\u001b[1;32m <a href='file:///home/zac-ubuntu-wsl/mambaforge/lib/python3.9/site-packages/nbdev/export.py?line=442'>443</a>\u001b[0m \u001b[39melif\u001b[39;00m fname \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m: add_init(get_config()\u001b[39m.\u001b[39mpath(\u001b[39m\"\u001b[39m\u001b[39mlib_path\u001b[39m\u001b[39m\"\u001b[39m))\n",
198
+ "File \u001b[0;32m~/mambaforge/lib/python3.9/site-packages/nbdev/export.py:376\u001b[0m, in \u001b[0;36m_notebook2script\u001b[0;34m(fname, modules, silent, to_dict, bare)\u001b[0m\n\u001b[1;32m <a href='file:///home/zac-ubuntu-wsl/mambaforge/lib/python3.9/site-packages/nbdev/export.py?line=373'>374</a>\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39m(fname_out, \u001b[39m'\u001b[39m\u001b[39ma\u001b[39m\u001b[39m'\u001b[39m, encoding\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mutf8\u001b[39m\u001b[39m'\u001b[39m) \u001b[39mas\u001b[39;00m f: f\u001b[39m.\u001b[39mwrite(code)\n\u001b[1;32m <a href='file:///home/zac-ubuntu-wsl/mambaforge/lib/python3.9/site-packages/nbdev/export.py?line=374'>375</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00me\u001b[39m}\u001b[39;00m\u001b[39m.py\u001b[39m\u001b[39m'\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m mod\u001b[39m.\u001b[39mmodules: mod\u001b[39m.\u001b[39mmodules\u001b[39m.\u001b[39mappend(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00me\u001b[39m}\u001b[39;00m\u001b[39m.py\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m--> <a href='file:///home/zac-ubuntu-wsl/mambaforge/lib/python3.9/site-packages/nbdev/export.py?line=375'>376</a>\u001b[0m \u001b[39mif\u001b[39;00m has_setting: save_nbdev_module(mod)\n\u001b[1;32m <a href='file:///home/zac-ubuntu-wsl/mambaforge/lib/python3.9/site-packages/nbdev/export.py?line=377'>378</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m silent: \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mConverted \u001b[39m\u001b[39m{\u001b[39;00mfname\u001b[39m.\u001b[39mname\u001b[39m}\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m <a href='file:///home/zac-ubuntu-wsl/mambaforge/lib/python3.9/site-packages/nbdev/export.py?line=378'>379</a>\u001b[0m \u001b[39mreturn\u001b[39;00m to_dict\n",
199
+ "File \u001b[0;32m~/mambaforge/lib/python3.9/site-packages/nbdev/export.py:286\u001b[0m, in \u001b[0;36msave_nbdev_module\u001b[0;34m(mod)\u001b[0m\n\u001b[1;32m <a href='file:///home/zac-ubuntu-wsl/mambaforge/lib/python3.9/site-packages/nbdev/export.py?line=283'>284</a>\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mSave `mod` inside <code>_nbdev</code>\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m <a href='file:///home/zac-ubuntu-wsl/mambaforge/lib/python3.9/site-packages/nbdev/export.py?line=284'>285</a>\u001b[0m fname \u001b[39m=\u001b[39m get_config()\u001b[39m.\u001b[39mpath(\u001b[39m\"\u001b[39m\u001b[39mlib_path\u001b[39m\u001b[39m\"\u001b[39m)\u001b[39m/\u001b[39m\u001b[39m'\u001b[39m\u001b[39m_nbdev.py\u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m--> <a href='file:///home/zac-ubuntu-wsl/mambaforge/lib/python3.9/site-packages/nbdev/export.py?line=285'>286</a>\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39;49m(fname, \u001b[39m'\u001b[39;49m\u001b[39mr\u001b[39;49m\u001b[39m'\u001b[39;49m) \u001b[39mas\u001b[39;00m f: code \u001b[39m=\u001b[39m f\u001b[39m.\u001b[39mread()\n\u001b[1;32m <a href='file:///home/zac-ubuntu-wsl/mambaforge/lib/python3.9/site-packages/nbdev/export.py?line=286'>287</a>\u001b[0m t \u001b[39m=\u001b[39m \u001b[39mr\u001b[39m\u001b[39m'\u001b[39m\u001b[39m,\u001b[39m\u001b[39m\\\u001b[39m\u001b[39mn \u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mjoin([\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mk\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m: \u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mv\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m \u001b[39mfor\u001b[39;00m k,v \u001b[39min\u001b[39;00m mod\u001b[39m.\u001b[39mindex\u001b[39m.\u001b[39mitems()])\n\u001b[1;32m <a href='file:///home/zac-ubuntu-wsl/mambaforge/lib/python3.9/site-packages/nbdev/export.py?line=287'>288</a>\u001b[0m code \u001b[39m=\u001b[39m _re_index_idx\u001b[39m.\u001b[39msub(\u001b[39m\"\u001b[39m\u001b[39mindex = \u001b[39m\u001b[39m{\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m+\u001b[39m t \u001b[39m+\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m}\u001b[39m\u001b[39m\"\u001b[39m, code)\n",
200
+ "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/mnt/z/Documents1/Repositories/exercise_classifier/{lib_name}/_nbdev.py'"
201
+ ]
202
+ }
203
+ ],
204
  "source": [
205
  "notebook2script('app.pynb')"
206
  ]
215
  }
216
  ],
217
  "metadata": {
218
+ "interpreter": {
219
+ "hash": "1cc00754fef6b3add70ae5c9c7fbd78954fc8b6bbb919f91eaedfd0a199a67e5"
220
+ },
221
  "kernelspec": {
222
+ "display_name": "Python 3.9.10 ('base')",
223
  "language": "python",
224
  "name": "python3"
225
  },
app.py CHANGED
@@ -1,17 +1,17 @@
1
  # AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb (unless otherwise specified).
2
 
3
- __all__ = ['classify_image', 'categories', 'image', 'label', 'examples', 'intf']
4
 
5
  # Cell
6
- from fastai.vision.all import *
7
- import gradio as gr
8
-
9
  learn = load_learner('model.pkl')
 
 
10
  categories = ('person bench pressing','person overhead pressing', 'person deadlifting', 'person lateral raising', 'person bent over row', 'person barbell squatting')
11
 
12
  def classify_image(img):
13
  pred,idx,probs = learn.predict(img)
14
  return dict(zip(categories, map(float, probs)))
 
15
  # Cell
16
  image = gr.inputs.Image(shape=(192,192))
17
  label = gr.outputs.Label()
1
  # AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb (unless otherwise specified).
2
 
3
+ __all__ = ['learn', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf']
4
 
5
  # Cell
 
 
 
6
  learn = load_learner('model.pkl')
7
+
8
+ # Cell
9
  categories = ('person bench pressing','person overhead pressing', 'person deadlifting', 'person lateral raising', 'person bent over row', 'person barbell squatting')
10
 
11
  def classify_image(img):
12
  pred,idx,probs = learn.predict(img)
13
  return dict(zip(categories, map(float, probs)))
14
+
15
  # Cell
16
  image = gr.inputs.Image(shape=(192,192))
17
  label = gr.outputs.Label()