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.gitignore ADDED
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+ bear_env/
.ipynb_checkpoints/Bearify_nb-checkpoint.ipynb ADDED
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+ {
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": 1,
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+ "metadata": {
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+ "id": "UySFk1vPKxb_"
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+ },
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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": "markdown",
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+ "metadata": {
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+ "id": "gT0wxrhGKIxL"
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+ },
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+ "source": [
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+ "# Bearify"
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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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+ "metadata": {
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+ "id": "Fg2er2rQLApV"
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+ },
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+ "outputs": [],
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+ "source": [
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+ "#|export\n",
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+ "from fastai.vision.all import *\n",
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+ "import gradio as gr\n",
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+ "\n",
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+ "def which_bear(x): pass"
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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": 9,
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+ "metadata": {
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+ "colab": {
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+ "base_uri": "https://localhost:8080/",
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+ "height": 209
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+ },
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+ "id": "vBBjPghILOjq",
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+ "outputId": "caa4c037-3d1e-43ae-a8e2-0f9c79198a2d"
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+ },
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+ "outputs": [
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+ {
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+ "data": {
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",
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",
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+ "text/plain": [
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+ "PILImage mode=RGB size=192x150"
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+ ]
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+ },
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+ "execution_count": 9,
59
+ "metadata": {},
60
+ "output_type": "execute_result"
61
+ }
62
+ ],
63
+ "source": [
64
+ "im = PILImage.create('images/teddy.jpg')\n",
65
+ "im.thumbnail((192,192))\n",
66
+ "im"
67
+ ]
68
+ },
69
+ {
70
+ "cell_type": "code",
71
+ "execution_count": 6,
72
+ "metadata": {},
73
+ "outputs": [],
74
+ "source": [
75
+ "import pathlib"
76
+ ]
77
+ },
78
+ {
79
+ "cell_type": "code",
80
+ "execution_count": 7,
81
+ "metadata": {},
82
+ "outputs": [],
83
+ "source": [
84
+ "temp = pathlib.PosixPath\n",
85
+ "pathlib.PosixPath = pathlib.WindowsPath"
86
+ ]
87
+ },
88
+ {
89
+ "cell_type": "code",
90
+ "execution_count": 8,
91
+ "metadata": {
92
+ "id": "Ko1vxtuzACNo"
93
+ },
94
+ "outputs": [],
95
+ "source": [
96
+ "learn = load_learner('bear_model.pkl')"
97
+ ]
98
+ },
99
+ {
100
+ "cell_type": "code",
101
+ "execution_count": 15,
102
+ "metadata": {},
103
+ "outputs": [],
104
+ "source": [
105
+ "pathlib.PosixPath = temp"
106
+ ]
107
+ },
108
+ {
109
+ "cell_type": "code",
110
+ "execution_count": 16,
111
+ "metadata": {
112
+ "colab": {
113
+ "base_uri": "https://localhost:8080/",
114
+ "height": 34
115
+ },
116
+ "id": "N4lUOFyom35W",
117
+ "outputId": "d363cb16-e67f-4829-a776-8af408671170"
118
+ },
119
+ "outputs": [
120
+ {
121
+ "data": {
122
+ "text/html": [
123
+ "\n",
124
+ "<style>\n",
125
+ " /* Turns off some styling */\n",
126
+ " progress {\n",
127
+ " /* gets rid of default border in Firefox and Opera. */\n",
128
+ " border: none;\n",
129
+ " /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
130
+ " background-size: auto;\n",
131
+ " }\n",
132
+ " progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
133
+ " background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
134
+ " }\n",
135
+ " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
136
+ " background: #F44336;\n",
137
+ " }\n",
138
+ "</style>\n"
139
+ ],
140
+ "text/plain": [
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+ "<IPython.core.display.HTML object>"
142
+ ]
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+ },
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+ "metadata": {},
145
+ "output_type": "display_data"
146
+ },
147
+ {
148
+ "data": {
149
+ "text/html": [],
150
+ "text/plain": [
151
+ "<IPython.core.display.HTML object>"
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+ ]
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+ },
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+ "metadata": {},
155
+ "output_type": "display_data"
156
+ },
157
+ {
158
+ "data": {
159
+ "text/plain": [
160
+ "('teddy', tensor(2), tensor([3.1420e-05, 7.4805e-06, 9.9996e-01]))"
161
+ ]
162
+ },
163
+ "execution_count": 16,
164
+ "metadata": {},
165
+ "output_type": "execute_result"
166
+ }
167
+ ],
168
+ "source": [
169
+ "learn.predict(im)"
170
+ ]
171
+ },
172
+ {
173
+ "cell_type": "code",
174
+ "execution_count": 12,
175
+ "metadata": {
176
+ "id": "k8MzL29fm5wO"
177
+ },
178
+ "outputs": [],
179
+ "source": [
180
+ "categories = ('Teddy', 'Black', 'Grizzly')\n",
181
+ "\n",
182
+ "def classify_image(img):\n",
183
+ " pred, idx, probs = learn.predict(img)\n",
184
+ " return dict(zip(categories, map(float, probs)))"
185
+ ]
186
+ },
187
+ {
188
+ "cell_type": "code",
189
+ "execution_count": 13,
190
+ "metadata": {
191
+ "colab": {
192
+ "base_uri": "https://localhost:8080/",
193
+ "height": 69
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+ },
195
+ "id": "R_dNtPRtoPER",
196
+ "outputId": "95b072b8-736f-424d-98dd-2a99e5078bef"
197
+ },
198
+ "outputs": [
199
+ {
200
+ "data": {
201
+ "text/html": [
202
+ "\n",
203
+ "<style>\n",
204
+ " /* Turns off some styling */\n",
205
+ " progress {\n",
206
+ " /* gets rid of default border in Firefox and Opera. */\n",
207
+ " border: none;\n",
208
+ " /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
209
+ " background-size: auto;\n",
210
+ " }\n",
211
+ " progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
212
+ " background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
213
+ " }\n",
214
+ " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
215
+ " background: #F44336;\n",
216
+ " }\n",
217
+ "</style>\n"
218
+ ],
219
+ "text/plain": [
220
+ "<IPython.core.display.HTML object>"
221
+ ]
222
+ },
223
+ "metadata": {},
224
+ "output_type": "display_data"
225
+ },
226
+ {
227
+ "data": {
228
+ "text/html": [],
229
+ "text/plain": [
230
+ "<IPython.core.display.HTML object>"
231
+ ]
232
+ },
233
+ "metadata": {},
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+ "output_type": "display_data"
235
+ },
236
+ {
237
+ "data": {
238
+ "text/plain": [
239
+ "{'Teddy': 3.141982961096801e-05,\n",
240
+ " 'Black': 7.480457043129718e-06,\n",
241
+ " 'Grizzly': 0.9999611377716064}"
242
+ ]
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+ },
244
+ "execution_count": 13,
245
+ "metadata": {},
246
+ "output_type": "execute_result"
247
+ }
248
+ ],
249
+ "source": [
250
+ "classify_image(im)"
251
+ ]
252
+ },
253
+ {
254
+ "cell_type": "code",
255
+ "execution_count": 14,
256
+ "metadata": {
257
+ "colab": {
258
+ "base_uri": "https://localhost:8080/",
259
+ "height": 211
260
+ },
261
+ "id": "Uc2M0zOEoR6b",
262
+ "outputId": "08c190d2-b5ad-43d1-aa00-f4c452152024"
263
+ },
264
+ "outputs": [
265
+ {
266
+ "ename": "AttributeError",
267
+ "evalue": "module 'gradio' has no attribute 'inputs'",
268
+ "output_type": "error",
269
+ "traceback": [
270
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
271
+ "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
272
+ "Cell \u001b[1;32mIn[14], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m image \u001b[38;5;241m=\u001b[39m \u001b[43mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[38;5;241m.\u001b[39mImage(shape \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m192\u001b[39m,\u001b[38;5;241m192\u001b[39m))\n\u001b[0;32m 2\u001b[0m labels \u001b[38;5;241m=\u001b[39m gr\u001b[38;5;241m.\u001b[39moutputs\u001b[38;5;241m.\u001b[39mLabel()\n\u001b[0;32m 4\u001b[0m intf \u001b[38;5;241m=\u001b[39m gr\u001b[38;5;241m.\u001b[39mInterface(fn\u001b[38;5;241m=\u001b[39mclassify_image, inputs\u001b[38;5;241m=\u001b[39mimage, outputs\u001b[38;5;241m=\u001b[39mlabels)\n",
273
+ "\u001b[1;31mAttributeError\u001b[0m: module 'gradio' has no attribute 'inputs'"
274
+ ]
275
+ }
276
+ ],
277
+ "source": [
278
+ "image = gr.inputs.Image(shape = (192,192))\n",
279
+ "labels = gr.outputs.Label()\n",
280
+ "\n",
281
+ "intf = gr.Interface(fn=classify_image, inputs=image, outputs=labels)\n",
282
+ "intf.launch(inline=False)"
283
+ ]
284
+ },
285
+ {
286
+ "cell_type": "code",
287
+ "execution_count": null,
288
+ "metadata": {},
289
+ "outputs": [],
290
+ "source": []
291
+ }
292
+ ],
293
+ "metadata": {
294
+ "colab": {
295
+ "provenance": []
296
+ },
297
+ "kernelspec": {
298
+ "display_name": "bear_env",
299
+ "language": "python",
300
+ "name": "bear_env"
301
+ },
302
+ "language_info": {
303
+ "codemirror_mode": {
304
+ "name": "ipython",
305
+ "version": 3
306
+ },
307
+ "file_extension": ".py",
308
+ "mimetype": "text/x-python",
309
+ "name": "python",
310
+ "nbconvert_exporter": "python",
311
+ "pygments_lexer": "ipython3",
312
+ "version": "3.10.9"
313
+ },
314
+ "toc": {
315
+ "base_numbering": 1,
316
+ "nav_menu": {},
317
+ "number_sections": true,
318
+ "sideBar": true,
319
+ "skip_h1_title": false,
320
+ "title_cell": "Table of Contents",
321
+ "title_sidebar": "Contents",
322
+ "toc_cell": false,
323
+ "toc_position": {},
324
+ "toc_section_display": true,
325
+ "toc_window_display": false
326
+ }
327
+ },
328
+ "nbformat": 4,
329
+ "nbformat_minor": 4
330
+ }
Bearify_nb.ipynb ADDED
@@ -0,0 +1,326 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 1,
6
+ "metadata": {
7
+ "id": "UySFk1vPKxb_"
8
+ },
9
+ "outputs": [],
10
+ "source": [
11
+ "#|default_exp app"
12
+ ]
13
+ },
14
+ {
15
+ "cell_type": "markdown",
16
+ "metadata": {
17
+ "id": "gT0wxrhGKIxL"
18
+ },
19
+ "source": [
20
+ "# Bearify"
21
+ ]
22
+ },
23
+ {
24
+ "cell_type": "code",
25
+ "execution_count": 2,
26
+ "metadata": {
27
+ "colab": {
28
+ "base_uri": "https://localhost:8080/"
29
+ },
30
+ "collapsed": true,
31
+ "id": "HHP6YSgf_WOu",
32
+ "outputId": "4b096ca9-b0a3-467c-b69b-1880fce6087e"
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+ },
34
+ "outputs": [
35
+ {
36
+ "output_type": "stream",
37
+ "name": "stdout",
38
+ "text": [
39
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.3/12.3 MB\u001b[0m \u001b[31m37.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
40
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.0/92.0 kB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
41
+ "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
42
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m315.9/315.9 kB\u001b[0m \u001b[31m29.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
43
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.6/75.6 kB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
44
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m142.5/142.5 kB\u001b[0m \u001b[31m16.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
45
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.7/8.7 MB\u001b[0m \u001b[31m74.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
46
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m47.2/47.2 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
47
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.8/60.8 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
48
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.9/129.9 kB\u001b[0m \u001b[31m15.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
49
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
50
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
51
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.9/71.9 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
52
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.6/53.6 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
53
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m307.7/307.7 kB\u001b[0m \u001b[31m22.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
54
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m341.4/341.4 kB\u001b[0m \u001b[31m22.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
55
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m47.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
56
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m49.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
57
+ "\u001b[?25h Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
58
+ "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
59
+ "spacy 3.7.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\n",
60
+ "weasel 0.3.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\u001b[0m\u001b[31m\n",
61
+ "\u001b[0m"
62
+ ]
63
+ }
64
+ ],
65
+ "source": [
66
+ "!pip install -Uqq gradio"
67
+ ]
68
+ },
69
+ {
70
+ "cell_type": "code",
71
+ "execution_count": 3,
72
+ "metadata": {
73
+ "id": "Fg2er2rQLApV"
74
+ },
75
+ "outputs": [],
76
+ "source": [
77
+ "#|export\n",
78
+ "from fastai.vision.all import *\n",
79
+ "import gradio as gr\n",
80
+ "\n",
81
+ "def which_bear(x): pass"
82
+ ]
83
+ },
84
+ {
85
+ "cell_type": "code",
86
+ "execution_count": 8,
87
+ "metadata": {
88
+ "colab": {
89
+ "base_uri": "https://localhost:8080/",
90
+ "height": 209
91
+ },
92
+ "id": "vBBjPghILOjq",
93
+ "outputId": "caa4c037-3d1e-43ae-a8e2-0f9c79198a2d"
94
+ },
95
+ "outputs": [
96
+ {
97
+ "output_type": "execute_result",
98
+ "data": {
99
+ "text/plain": [
100
+ "PILImage mode=RGB size=192x192"
101
+ ],
102
+ "image/png": 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\n"
103
+ },
104
+ "metadata": {},
105
+ "execution_count": 8
106
+ }
107
+ ],
108
+ "source": [
109
+ "im = PILImage.create('/content/teddy.jpg')\n",
110
+ "im.thumbnail((192,192))\n",
111
+ "im"
112
+ ]
113
+ },
114
+ {
115
+ "cell_type": "code",
116
+ "execution_count": 5,
117
+ "metadata": {
118
+ "id": "Ko1vxtuzACNo"
119
+ },
120
+ "outputs": [],
121
+ "source": [
122
+ "learn = load_learner('/content/bear_model.pkl')"
123
+ ]
124
+ },
125
+ {
126
+ "cell_type": "code",
127
+ "source": [
128
+ "learn.predict(im)"
129
+ ],
130
+ "metadata": {
131
+ "colab": {
132
+ "base_uri": "https://localhost:8080/",
133
+ "height": 34
134
+ },
135
+ "id": "N4lUOFyom35W",
136
+ "outputId": "d363cb16-e67f-4829-a776-8af408671170"
137
+ },
138
+ "execution_count": 9,
139
+ "outputs": [
140
+ {
141
+ "output_type": "display_data",
142
+ "data": {
143
+ "text/plain": [
144
+ "<IPython.core.display.HTML object>"
145
+ ],
146
+ "text/html": [
147
+ "\n",
148
+ "<style>\n",
149
+ " /* Turns off some styling */\n",
150
+ " progress {\n",
151
+ " /* gets rid of default border in Firefox and Opera. */\n",
152
+ " border: none;\n",
153
+ " /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
154
+ " background-size: auto;\n",
155
+ " }\n",
156
+ " progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
157
+ " background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
158
+ " }\n",
159
+ " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
160
+ " background: #F44336;\n",
161
+ " }\n",
162
+ "</style>\n"
163
+ ]
164
+ },
165
+ "metadata": {}
166
+ },
167
+ {
168
+ "output_type": "display_data",
169
+ "data": {
170
+ "text/plain": [
171
+ "<IPython.core.display.HTML object>"
172
+ ],
173
+ "text/html": []
174
+ },
175
+ "metadata": {}
176
+ },
177
+ {
178
+ "output_type": "execute_result",
179
+ "data": {
180
+ "text/plain": [
181
+ "('teddy', tensor(2), tensor([4.8331e-05, 7.1999e-05, 9.9988e-01]))"
182
+ ]
183
+ },
184
+ "metadata": {},
185
+ "execution_count": 9
186
+ }
187
+ ]
188
+ },
189
+ {
190
+ "cell_type": "code",
191
+ "source": [
192
+ "categories = ('Teddy', 'Black', 'Grizzly')\n",
193
+ "\n",
194
+ "def classify_image(img):\n",
195
+ " pred, idx, probs = learn.predict(img)\n",
196
+ " return dict(zip(categories, map(float, probs)))"
197
+ ],
198
+ "metadata": {
199
+ "id": "k8MzL29fm5wO"
200
+ },
201
+ "execution_count": 10,
202
+ "outputs": []
203
+ },
204
+ {
205
+ "cell_type": "code",
206
+ "source": [
207
+ "classify_image(im)"
208
+ ],
209
+ "metadata": {
210
+ "colab": {
211
+ "base_uri": "https://localhost:8080/",
212
+ "height": 69
213
+ },
214
+ "id": "R_dNtPRtoPER",
215
+ "outputId": "95b072b8-736f-424d-98dd-2a99e5078bef"
216
+ },
217
+ "execution_count": 11,
218
+ "outputs": [
219
+ {
220
+ "output_type": "display_data",
221
+ "data": {
222
+ "text/plain": [
223
+ "<IPython.core.display.HTML object>"
224
+ ],
225
+ "text/html": [
226
+ "\n",
227
+ "<style>\n",
228
+ " /* Turns off some styling */\n",
229
+ " progress {\n",
230
+ " /* gets rid of default border in Firefox and Opera. */\n",
231
+ " border: none;\n",
232
+ " /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
233
+ " background-size: auto;\n",
234
+ " }\n",
235
+ " progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
236
+ " background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
237
+ " }\n",
238
+ " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
239
+ " background: #F44336;\n",
240
+ " }\n",
241
+ "</style>\n"
242
+ ]
243
+ },
244
+ "metadata": {}
245
+ },
246
+ {
247
+ "output_type": "display_data",
248
+ "data": {
249
+ "text/plain": [
250
+ "<IPython.core.display.HTML object>"
251
+ ],
252
+ "text/html": []
253
+ },
254
+ "metadata": {}
255
+ },
256
+ {
257
+ "output_type": "execute_result",
258
+ "data": {
259
+ "text/plain": [
260
+ "{'Teddy': 4.833127968595363e-05,\n",
261
+ " 'Black': 7.199876563390717e-05,\n",
262
+ " 'Grizzly': 0.9998795986175537}"
263
+ ]
264
+ },
265
+ "metadata": {},
266
+ "execution_count": 11
267
+ }
268
+ ]
269
+ },
270
+ {
271
+ "cell_type": "code",
272
+ "source": [
273
+ "image = gr.inputs.Image(shape = (192,192))\n",
274
+ "labels = gr.outputs.Label()\n",
275
+ "\n",
276
+ "intf = gr.Interface(fn=classify_image, inputs=image, outputs=labels)\n",
277
+ "intf.launch(inline=False)"
278
+ ],
279
+ "metadata": {
280
+ "colab": {
281
+ "base_uri": "https://localhost:8080/",
282
+ "height": 211
283
+ },
284
+ "id": "Uc2M0zOEoR6b",
285
+ "outputId": "08c190d2-b5ad-43d1-aa00-f4c452152024"
286
+ },
287
+ "execution_count": 16,
288
+ "outputs": [
289
+ {
290
+ "output_type": "error",
291
+ "ename": "AttributeError",
292
+ "evalue": "module 'gradio' has no attribute 'inputs'",
293
+ "traceback": [
294
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
295
+ "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
296
+ "\u001b[0;32m<ipython-input-16-b4d2dd17fd72>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m192\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m192\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mintf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInterface\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclassify_image\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mintf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlaunch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
297
+ "\u001b[0;31mAttributeError\u001b[0m: module 'gradio' has no attribute 'inputs'"
298
+ ]
299
+ }
300
+ ]
301
+ },
302
+ {
303
+ "cell_type": "code",
304
+ "source": [],
305
+ "metadata": {
306
+ "id": "bqK_vxTfpqBj"
307
+ },
308
+ "execution_count": null,
309
+ "outputs": []
310
+ }
311
+ ],
312
+ "metadata": {
313
+ "colab": {
314
+ "provenance": []
315
+ },
316
+ "kernelspec": {
317
+ "display_name": "Python 3",
318
+ "name": "python3"
319
+ },
320
+ "language_info": {
321
+ "name": "python"
322
+ }
323
+ },
324
+ "nbformat": 4,
325
+ "nbformat_minor": 0
326
+ }
README.md ADDED
@@ -0,0 +1,13 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ title: Bearify
3
+ emoji: 🏆
4
+ colorFrom: blue
5
+ colorTo: green
6
+ sdk: gradio
7
+ sdk_version: 4.31.3
8
+ app_file: app.py
9
+ pinned: false
10
+ license: apache-2.0
11
+ ---
12
+
13
+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ import gradio as gr
2
+
3
+ def greet(name):
4
+ return "Hello " + name + "!!"
5
+
6
+ demo = gr.Interface(fn=greet, inputs="text", outputs="text")
7
+ demo.launch()
requirements.txt ADDED
@@ -0,0 +1,181 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ aiofiles==23.2.1
2
+ altair==5.3.0
3
+ annotated-types==0.7.0
4
+ anyio==4.4.0
5
+ argon2-cffi==23.1.0
6
+ argon2-cffi-bindings==21.2.0
7
+ arrow==1.3.0
8
+ asttokens==2.4.1
9
+ astunparse==1.6.3
10
+ async-lru==2.0.4
11
+ attrs==23.2.0
12
+ Babel==2.15.0
13
+ beautifulsoup4==4.12.3
14
+ bleach==6.1.0
15
+ blis==0.7.11
16
+ catalogue==2.0.10
17
+ certifi==2024.2.2
18
+ cffi==1.16.0
19
+ charset-normalizer==3.3.2
20
+ click==8.1.7
21
+ cloudpathlib==0.16.0
22
+ colorama==0.4.6
23
+ comm==0.2.2
24
+ confection==0.1.4
25
+ contourpy==1.2.1
26
+ cycler==0.12.1
27
+ cymem==2.0.8
28
+ debugpy==1.8.1
29
+ decorator==5.1.1
30
+ defusedxml==0.7.1
31
+ dnspython==2.6.1
32
+ email_validator==2.1.1
33
+ exceptiongroup==1.2.1
34
+ execnb==0.1.6
35
+ executing==2.0.1
36
+ fastai==2.7.15
37
+ fastapi==0.111.0
38
+ fastapi-cli==0.0.4
39
+ fastcore==1.5.40
40
+ fastdownload==0.0.7
41
+ fastjsonschema==2.19.1
42
+ fastprogress==1.0.3
43
+ ffmpy==0.3.2
44
+ filelock==3.14.0
45
+ fonttools==4.52.4
46
+ fqdn==1.5.1
47
+ fsspec==2024.5.0
48
+ ghapi==1.0.5
49
+ gradio==4.31.5
50
+ gradio_client==0.16.4
51
+ h11==0.14.0
52
+ httpcore==1.0.5
53
+ httptools==0.6.1
54
+ httpx==0.27.0
55
+ huggingface-hub==0.23.2
56
+ idna==3.7
57
+ importlib_resources==6.4.0
58
+ intel-openmp==2021.4.0
59
+ ipykernel==6.29.4
60
+ ipython==8.24.0
61
+ isoduration==20.11.0
62
+ jedi==0.19.1
63
+ Jinja2==3.1.4
64
+ joblib==1.4.2
65
+ json5==0.9.25
66
+ jsonpointer==2.4
67
+ jsonschema==4.22.0
68
+ jsonschema-specifications==2023.12.1
69
+ jupyter-events==0.10.0
70
+ jupyter-lsp==2.2.5
71
+ jupyter_client==8.6.2
72
+ jupyter_core==5.7.2
73
+ jupyter_server==2.14.0
74
+ jupyter_server_terminals==0.5.3
75
+ jupyterlab==4.2.1
76
+ jupyterlab_pygments==0.3.0
77
+ jupyterlab_server==2.27.2
78
+ kiwisolver==1.4.5
79
+ langcodes==3.4.0
80
+ language_data==1.2.0
81
+ marisa-trie==1.1.1
82
+ markdown-it-py==3.0.0
83
+ MarkupSafe==2.1.5
84
+ matplotlib==3.9.0
85
+ matplotlib-inline==0.1.7
86
+ mdurl==0.1.2
87
+ mistune==3.0.2
88
+ mkl==2021.4.0
89
+ mpmath==1.3.0
90
+ murmurhash==1.0.10
91
+ nbclient==0.10.0
92
+ nbconvert==7.16.4
93
+ nbdev==2.3.23
94
+ nbformat==5.10.4
95
+ nest-asyncio==1.6.0
96
+ networkx==3.3
97
+ notebook==7.2.0
98
+ notebook_shim==0.2.4
99
+ numpy==1.26.4
100
+ orjson==3.10.3
101
+ overrides==7.7.0
102
+ packaging==24.0
103
+ pandas==2.2.2
104
+ pandocfilters==1.5.1
105
+ parso==0.8.4
106
+ pillow==10.3.0
107
+ platformdirs==4.2.2
108
+ preshed==3.0.9
109
+ prometheus_client==0.20.0
110
+ prompt_toolkit==3.0.45
111
+ psutil==5.9.8
112
+ pure-eval==0.2.2
113
+ pycparser==2.22
114
+ pydantic==2.7.1
115
+ pydantic_core==2.18.2
116
+ pydub==0.25.1
117
+ Pygments==2.18.0
118
+ pyparsing==3.1.2
119
+ python-dateutil==2.9.0.post0
120
+ python-dotenv==1.0.1
121
+ python-json-logger==2.0.7
122
+ python-multipart==0.0.9
123
+ pytz==2024.1
124
+ pywin32==306
125
+ pywinpty==2.0.13
126
+ PyYAML==6.0.1
127
+ pyzmq==26.0.3
128
+ referencing==0.35.1
129
+ requests==2.32.2
130
+ rfc3339-validator==0.1.4
131
+ rfc3986-validator==0.1.1
132
+ rich==13.7.1
133
+ rpds-py==0.18.1
134
+ ruff==0.4.5
135
+ scikit-learn==1.5.0
136
+ scipy==1.13.1
137
+ semantic-version==2.10.0
138
+ Send2Trash==1.8.3
139
+ shellingham==1.5.4
140
+ six==1.16.0
141
+ smart-open==6.4.0
142
+ sniffio==1.3.1
143
+ soupsieve==2.5
144
+ spacy==3.7.4
145
+ spacy-legacy==3.0.12
146
+ spacy-loggers==1.0.5
147
+ srsly==2.4.8
148
+ stack-data==0.6.3
149
+ starlette==0.37.2
150
+ sympy==1.12
151
+ tbb==2021.12.0
152
+ terminado==0.18.1
153
+ thinc==8.2.3
154
+ threadpoolctl==3.5.0
155
+ tinycss2==1.3.0
156
+ tomli==2.0.1
157
+ tomlkit==0.12.0
158
+ toolz==0.12.1
159
+ torch==2.3.0
160
+ torchaudio==2.3.0
161
+ torchvision==0.18.0
162
+ tornado==6.4
163
+ tqdm==4.66.4
164
+ traitlets==5.14.3
165
+ typer==0.9.4
166
+ types-python-dateutil==2.9.0.20240316
167
+ typing_extensions==4.12.0
168
+ tzdata==2024.1
169
+ ujson==5.10.0
170
+ uri-template==1.3.0
171
+ urllib3==2.2.1
172
+ uvicorn==0.30.0
173
+ wasabi==1.1.2
174
+ watchdog==4.0.1
175
+ watchfiles==0.22.0
176
+ wcwidth==0.2.13
177
+ weasel==0.3.4
178
+ webcolors==1.13
179
+ webencodings==0.5.1
180
+ websocket-client==1.8.0
181
+ websockets==11.0.3