Hugging Face's logo

Spaces:
William-Downing
/
Pets_Final_Classification
Runtime error

App Files Community
Pets_Final_Classification
File size: 194,706 Bytes
a1c5266
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|default_exp app"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: gradio==3.50 in /usr/local/lib/python3.10/dist-packages (3.50.0)\n",
      "Requirement already satisfied: numpy~=1.0 in /usr/local/lib/python3.10/dist-packages (from gradio==3.50) (1.26.2)\n",
      "Requirement already satisfied: aiofiles<24.0,>=22.0 in /usr/local/lib/python3.10/dist-packages (from gradio==3.50) (23.2.1)\n",
      "Requirement already satisfied: gradio-client==0.6.1 in /usr/local/lib/python3.10/dist-packages (from gradio==3.50) (0.6.1)\n",
      "Requirement already satisfied: orjson~=3.0 in /usr/local/lib/python3.10/dist-packages (from gradio==3.50) (3.9.10)\n",
      "Requirement already satisfied: semantic-version~=2.0 in /usr/local/lib/python3.10/dist-packages (from gradio==3.50) (2.10.0)\n",
      "Requirement already satisfied: matplotlib~=3.0 in /usr/local/lib/python3.10/dist-packages (from gradio==3.50) (3.8.2)\n",
      "Requirement already satisfied: huggingface-hub>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from gradio==3.50) (0.20.1)\n",
      "Requirement already satisfied: jinja2<4.0 in /usr/local/lib/python3.10/dist-packages (from gradio==3.50) (3.1.2)\n",
      "Requirement already satisfied: altair<6.0,>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from gradio==3.50) (5.2.0)\n",
      "Requirement already satisfied: typing-extensions~=4.0 in /usr/local/lib/python3.10/dist-packages (from gradio==3.50) (4.9.0)\n",
      "Requirement already satisfied: requests~=2.0 in /usr/local/lib/python3.10/dist-packages (from gradio==3.50) (2.31.0)\n",
      "Requirement already satisfied: httpx in /usr/local/lib/python3.10/dist-packages (from gradio==3.50) (0.26.0)\n",
      "Requirement already satisfied: pillow<11.0,>=8.0 in /usr/local/lib/python3.10/dist-packages (from gradio==3.50) (10.2.0)\n",
      "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from gradio==3.50) (23.2)\n",
      "Requirement already satisfied: fastapi in /usr/local/lib/python3.10/dist-packages (from gradio==3.50) (0.108.0)\n",
      "Requirement already satisfied: python-multipart in /usr/local/lib/python3.10/dist-packages (from gradio==3.50) (0.0.6)\n",
      "Requirement already satisfied: pandas<3.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from gradio==3.50) (2.1.4)\n",
      "Requirement already satisfied: markupsafe~=2.0 in /usr/local/lib/python3.10/dist-packages (from gradio==3.50) (2.1.3)\n",
      "Requirement already satisfied: uvicorn>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from gradio==3.50) (0.25.0)\n",
      "Requirement already satisfied: websockets<12.0,>=10.0 in /usr/local/lib/python3.10/dist-packages (from gradio==3.50) (11.0.3)\n",
      "Requirement already satisfied: ffmpy in /usr/local/lib/python3.10/dist-packages (from gradio==3.50) (0.3.1)\n",
      "Requirement already satisfied: importlib-resources<7.0,>=1.3 in /usr/local/lib/python3.10/dist-packages (from gradio==3.50) (6.1.1)\n",
      "Requirement already satisfied: pydub in /usr/local/lib/python3.10/dist-packages (from gradio==3.50) (0.25.1)\n",
      "Requirement already satisfied: pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,<3.0.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from gradio==3.50) (2.5.3)\n",
      "Requirement already satisfied: pyyaml<7.0,>=5.0 in /usr/lib/python3/dist-packages (from gradio==3.50) (5.4.1)\n",
      "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from gradio-client==0.6.1->gradio==3.50) (2023.12.2)\n",
      "Requirement already satisfied: toolz in /usr/local/lib/python3.10/dist-packages (from altair<6.0,>=4.2.0->gradio==3.50) (0.12.0)\n",
      "Requirement already satisfied: jsonschema>=3.0 in /usr/local/lib/python3.10/dist-packages (from altair<6.0,>=4.2.0->gradio==3.50) (4.20.0)\n",
      "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.14.0->gradio==3.50) (4.66.1)\n",
      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.14.0->gradio==3.50) (3.13.1)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /usr/lib/python3/dist-packages (from matplotlib~=3.0->gradio==3.50) (2.4.7)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio==3.50) (1.2.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio==3.50) (1.4.5)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio==3.50) (4.47.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio==3.50) (0.12.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/devcontainers/.local/lib/python3.10/site-packages (from matplotlib~=3.0->gradio==3.50) (2.8.2)\n",
      "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas<3.0,>=1.0->gradio==3.50) (2023.4)\n",
      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas<3.0,>=1.0->gradio==3.50) (2023.3.post1)\n",
      "Requirement already satisfied: pydantic-core==2.14.6 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,<3.0.0,>=1.7.4->gradio==3.50) (2.14.6)\n",
      "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,<3.0.0,>=1.7.4->gradio==3.50) (0.6.0)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests~=2.0->gradio==3.50) (2023.11.17)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests~=2.0->gradio==3.50) (2.1.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests~=2.0->gradio==3.50) (3.6)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests~=2.0->gradio==3.50) (3.3.2)\n",
      "Requirement already satisfied: h11>=0.8 in /usr/local/lib/python3.10/dist-packages (from uvicorn>=0.14.0->gradio==3.50) (0.14.0)\n",
      "Requirement already satisfied: click>=7.0 in /usr/local/lib/python3.10/dist-packages (from uvicorn>=0.14.0->gradio==3.50) (8.1.7)\n",
      "Requirement already satisfied: starlette<0.33.0,>=0.29.0 in /usr/local/lib/python3.10/dist-packages (from fastapi->gradio==3.50) (0.32.0.post1)\n",
      "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from httpx->gradio==3.50) (1.3.0)\n",
      "Requirement already satisfied: anyio in /usr/local/lib/python3.10/dist-packages (from httpx->gradio==3.50) (4.2.0)\n",
      "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx->gradio==3.50) (1.0.2)\n",
      "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio==3.50) (0.16.2)\n",
      "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio==3.50) (23.2.0)\n",
      "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio==3.50) (0.32.0)\n",
      "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio==3.50) (2023.12.1)\n",
      "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib~=3.0->gradio==3.50) (1.16.0)\n",
      "Requirement already satisfied: exceptiongroup>=1.0.2 in /home/devcontainers/.local/lib/python3.10/site-packages (from anyio->httpx->gradio==3.50) (1.2.0)\n",
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: fastai in /usr/local/lib/python3.10/dist-packages (2.7.13)\n",
      "Requirement already satisfied: fastdownload<2,>=0.0.5 in /usr/local/lib/python3.10/dist-packages (from fastai) (0.0.7)\n",
      "Requirement already satisfied: torchvision>=0.11 in /usr/local/lib/python3.10/dist-packages (from fastai) (0.16.2)\n",
      "Requirement already satisfied: pillow>=9.0.0 in /usr/local/lib/python3.10/dist-packages (from fastai) (10.2.0)\n",
      "Requirement already satisfied: fastcore<1.6,>=1.5.29 in /usr/local/lib/python3.10/dist-packages (from fastai) (1.5.29)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from fastai) (2.31.0)\n",
      "Requirement already satisfied: spacy<4 in /usr/local/lib/python3.10/dist-packages (from fastai) (3.7.2)\n",
      "Requirement already satisfied: torch<2.2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from fastai) (2.1.2)\n",
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from fastai) (3.8.2)\n",
      "Requirement already satisfied: pyyaml in /usr/lib/python3/dist-packages (from fastai) (5.4.1)\n",
      "Requirement already satisfied: fastprogress>=0.2.4 in /usr/local/lib/python3.10/dist-packages (from fastai) (1.0.3)\n",
      "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from fastai) (1.3.2)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from fastai) (1.11.4)\n",
      "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from fastai) (23.2)\n",
      "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from fastai) (2.1.4)\n",
      "Requirement already satisfied: pip in /usr/lib/python3/dist-packages (from fastai) (22.0.2)\n",
      "Requirement already satisfied: thinc<8.3.0,>=8.1.8 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (8.2.2)\n",
      "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (4.66.1)\n",
      "Requirement already satisfied: setuptools in /usr/lib/python3/dist-packages (from spacy<4->fastai) (59.6.0)\n",
      "Requirement already satisfied: typer<0.10.0,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (0.9.0)\n",
      "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (3.0.9)\n",
      "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (3.3.0)\n",
      "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (1.1.2)\n",
      "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (2.0.10)\n",
      "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (3.0.12)\n",
      "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (1.0.10)\n",
      "Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (6.4.0)\n",
      "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (2.0.8)\n",
      "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (2.5.3)\n",
      "Requirement already satisfied: weasel<0.4.0,>=0.1.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (0.3.4)\n",
      "Requirement already satisfied: numpy>=1.19.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (1.26.2)\n",
      "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (1.0.5)\n",
      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (3.1.2)\n",
      "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.10/dist-packages (from spacy<4->fastai) (2.4.8)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->fastai) (3.3.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->fastai) (2023.11.17)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->fastai) (3.6)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->fastai) (2.1.0)\n",
      "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch<2.2,>=1.10->fastai) (1.12)\n",
      "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch<2.2,>=1.10->fastai) (12.1.3.1)\n",
      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch<2.2,>=1.10->fastai) (12.1.105)\n",
      "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch<2.2,>=1.10->fastai) (2.1.0)\n",
      "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch<2.2,>=1.10->fastai) (11.4.5.107)\n",
      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch<2.2,>=1.10->fastai) (3.13.1)\n",
      "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch<2.2,>=1.10->fastai) (8.9.2.26)\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch<2.2,>=1.10->fastai) (12.1.105)\n",
      "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch<2.2,>=1.10->fastai) (12.1.105)\n",
      "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch<2.2,>=1.10->fastai) (2023.12.2)\n",
      "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch<2.2,>=1.10->fastai) (10.3.2.106)\n",
      "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch<2.2,>=1.10->fastai) (11.0.2.54)\n",
      "Requirement already satisfied: nvidia-nccl-cu12==2.18.1 in /usr/local/lib/python3.10/dist-packages (from torch<2.2,>=1.10->fastai) (2.18.1)\n",
      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch<2.2,>=1.10->fastai) (12.1.105)\n",
      "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch<2.2,>=1.10->fastai) (3.2.1)\n",
      "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch<2.2,>=1.10->fastai) (12.1.0.106)\n",
      "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch<2.2,>=1.10->fastai) (4.9.0)\n",
      "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch<2.2,>=1.10->fastai) (12.3.101)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->fastai) (4.47.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/devcontainers/.local/lib/python3.10/site-packages (from matplotlib->fastai) (2.8.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->fastai) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->fastai) (0.12.1)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /usr/lib/python3/dist-packages (from matplotlib->fastai) (2.4.7)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->fastai) (1.4.5)\n",
      "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->fastai) (2023.4)\n",
      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->fastai) (2023.3.post1)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->fastai) (1.3.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->fastai) (3.2.0)\n",
      "Requirement already satisfied: pydantic-core==2.14.6 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<4->fastai) (2.14.6)\n",
      "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<4->fastai) (0.6.0)\n",
      "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib->fastai) (1.16.0)\n",
      "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from thinc<8.3.0,>=8.1.8->spacy<4->fastai) (0.1.4)\n",
      "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.10/dist-packages (from thinc<8.3.0,>=8.1.8->spacy<4->fastai) (0.7.11)\n",
      "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.10/dist-packages (from typer<0.10.0,>=0.3.0->spacy<4->fastai) (8.1.7)\n",
      "Requirement already satisfied: cloudpathlib<0.17.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from weasel<0.4.0,>=0.1.0->spacy<4->fastai) (0.16.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->spacy<4->fastai) (2.1.3)\n",
      "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch<2.2,>=1.10->fastai) (1.3.0)\n",
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: ipywidgets in /usr/local/lib/python3.10/dist-packages (8.0.4)\n",
      "Requirement already satisfied: ipykernel>=4.5.1 in /home/devcontainers/.local/lib/python3.10/site-packages (from ipywidgets) (6.28.0)\n",
      "Requirement already satisfied: traitlets>=4.3.1 in /home/devcontainers/.local/lib/python3.10/site-packages (from ipywidgets) (5.14.1)\n",
      "Requirement already satisfied: ipython>=6.1.0 in /home/devcontainers/.local/lib/python3.10/site-packages (from ipywidgets) (8.19.0)\n",
      "Requirement already satisfied: widgetsnbextension~=4.0 in /usr/local/lib/python3.10/dist-packages (from ipywidgets) (4.0.9)\n",
      "Requirement already satisfied: jupyterlab-widgets~=3.0 in /usr/local/lib/python3.10/dist-packages (from ipywidgets) (3.0.9)\n",
      "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from ipykernel>=4.5.1->ipywidgets) (5.9.7)\n",
      "Requirement already satisfied: jupyter-client>=6.1.12 in /home/devcontainers/.local/lib/python3.10/site-packages (from ipykernel>=4.5.1->ipywidgets) (8.6.0)\n",
      "Requirement already satisfied: comm>=0.1.1 in /home/devcontainers/.local/lib/python3.10/site-packages (from ipykernel>=4.5.1->ipywidgets) (0.2.1)\n",
      "Requirement already satisfied: jupyter-core!=5.0.*,>=4.12 in /home/devcontainers/.local/lib/python3.10/site-packages (from ipykernel>=4.5.1->ipywidgets) (5.7.0)\n",
      "Requirement already satisfied: pyzmq>=24 in /home/devcontainers/.local/lib/python3.10/site-packages (from ipykernel>=4.5.1->ipywidgets) (25.1.2)\n",
      "Requirement already satisfied: matplotlib-inline>=0.1 in /home/devcontainers/.local/lib/python3.10/site-packages (from ipykernel>=4.5.1->ipywidgets) (0.1.6)\n",
      "Requirement already satisfied: nest-asyncio in /usr/local/lib/python3.10/dist-packages (from ipykernel>=4.5.1->ipywidgets) (1.5.8)\n",
      "Requirement already satisfied: tornado>=6.1 in /home/devcontainers/.local/lib/python3.10/site-packages (from ipykernel>=4.5.1->ipywidgets) (6.4)\n",
      "Requirement already satisfied: debugpy>=1.6.5 in /home/devcontainers/.local/lib/python3.10/site-packages (from ipykernel>=4.5.1->ipywidgets) (1.8.0)\n",
      "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from ipykernel>=4.5.1->ipywidgets) (23.2)\n",
      "Requirement already satisfied: pygments>=2.4.0 in /usr/local/lib/python3.10/dist-packages (from ipython>=6.1.0->ipywidgets) (2.17.2)\n",
      "Requirement already satisfied: jedi>=0.16 in /home/devcontainers/.local/lib/python3.10/site-packages (from ipython>=6.1.0->ipywidgets) (0.19.1)\n",
      "Requirement already satisfied: stack-data in /home/devcontainers/.local/lib/python3.10/site-packages (from ipython>=6.1.0->ipywidgets) (0.6.3)\n",
      "Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in /usr/local/lib/python3.10/dist-packages (from ipython>=6.1.0->ipywidgets) (3.0.43)\n",
      "Requirement already satisfied: decorator in /home/devcontainers/.local/lib/python3.10/site-packages (from ipython>=6.1.0->ipywidgets) (5.1.1)\n",
      "Requirement already satisfied: pexpect>4.3 in /home/devcontainers/.local/lib/python3.10/site-packages (from ipython>=6.1.0->ipywidgets) (4.9.0)\n",
      "Requirement already satisfied: exceptiongroup in /home/devcontainers/.local/lib/python3.10/site-packages (from ipython>=6.1.0->ipywidgets) (1.2.0)\n",
      "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /home/devcontainers/.local/lib/python3.10/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets) (0.8.3)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /home/devcontainers/.local/lib/python3.10/site-packages (from jupyter-client>=6.1.12->ipykernel>=4.5.1->ipywidgets) (2.8.2)\n",
      "Requirement already satisfied: platformdirs>=2.5 in /home/devcontainers/.local/lib/python3.10/site-packages (from jupyter-core!=5.0.*,>=4.12->ipykernel>=4.5.1->ipywidgets) (4.1.0)\n",
      "Requirement already satisfied: ptyprocess>=0.5 in /home/devcontainers/.local/lib/python3.10/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets) (0.7.0)\n",
      "Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython>=6.1.0->ipywidgets) (0.2.12)\n",
      "Requirement already satisfied: pure-eval in /home/devcontainers/.local/lib/python3.10/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (0.2.2)\n",
      "Requirement already satisfied: executing>=1.2.0 in /home/devcontainers/.local/lib/python3.10/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.0.1)\n",
      "Requirement already satisfied: asttokens>=2.1.0 in /home/devcontainers/.local/lib/python3.10/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.4.1)\n",
      "Requirement already satisfied: six>=1.12.0 in /usr/lib/python3/dist-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "#|export\n",
    "import sys\n",
    "import subprocess\n",
    "\n",
    "subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'gradio==3.50'])\n",
    "subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'fastai'])\n",
    "subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'ipywidgets'])\n",
    "\n",
    "from fastai.vision.all import *\n",
    "import gradio as gr \n",
    "import timm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "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",
      "image/png": "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",
      "text/plain": [
       "PILImage mode=RGB size=224x179"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im = PILImage.create('basset.jpg')\n",
    "im.thumbnail((224,224))\n",
    "im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "learn = load_learner('model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/fastai/torch_core.py:263: UserWarning: 'has_mps' is deprecated, please use 'torch.backends.mps.is_built()'\n",
      "  return getattr(torch, 'has_mps', False)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "('basset_hound',\n",
       " tensor(14),\n",
       " tensor([1.1692e-09, 3.1612e-08, 1.8306e-09, 4.6919e-10, 1.3168e-10, 4.0575e-10,\n",
       "         1.4819e-10, 5.3437e-09, 1.2173e-10, 1.2675e-10, 1.6493e-10, 6.2697e-09,\n",
       "         4.2983e-10, 1.2519e-08, 1.0000e+00, 5.4720e-07, 2.2617e-09, 1.4778e-09,\n",
       "         3.3368e-06, 5.3226e-08, 1.4404e-08, 1.3074e-09, 1.5978e-09, 4.5295e-09,\n",
       "         1.6965e-09, 7.9832e-10, 6.1078e-09, 1.4449e-08, 4.2306e-09, 1.5306e-11,\n",
       "         3.0682e-09, 2.0838e-10, 1.5674e-09, 8.0081e-11, 1.3512e-09, 1.5832e-09,\n",
       "         2.2531e-08]))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.predict(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "categories = learn.dls.vocab\n",
    "\n",
    "def classify_image(img):\n",
    "    pred, idx, probs = learn.predict(img)\n",
    "    return dict(zip(categories, map(float, probs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/fastai/torch_core.py:263: UserWarning: 'has_mps' is deprecated, please use 'torch.backends.mps.is_built()'\n",
      "  return getattr(torch, 'has_mps', False)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='0' class='' max='1' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      0.00% [0/1 00:00&lt;?]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'Abyssinian': 1.169237706655224e-09,\n",
       " 'Bengal': 3.161239803262106e-08,\n",
       " 'Birman': 1.8306034466064602e-09,\n",
       " 'Bombay': 4.691882993235197e-10,\n",
       " 'British_Shorthair': 1.3168270640573354e-10,\n",
       " 'Egyptian_Mau': 4.0575368065454143e-10,\n",
       " 'Maine_Coon': 1.4818989091391899e-10,\n",
       " 'Persian': 5.343685938186127e-09,\n",
       " 'Ragdoll': 1.2172574059832186e-10,\n",
       " 'Russian_Blue': 1.2675280269824896e-10,\n",
       " 'Siamese': 1.649344688603449e-10,\n",
       " 'Sphynx': 6.269712748974143e-09,\n",
       " 'american_bulldog': 4.2982603587482515e-10,\n",
       " 'american_pit_bull_terrier': 1.251895120901736e-08,\n",
       " 'basset_hound': 0.9999959468841553,\n",
       " 'beagle': 5.472031148201495e-07,\n",
       " 'boxer': 2.2616624129057072e-09,\n",
       " 'chihuahua': 1.4778237522605764e-09,\n",
       " 'english_cocker_spaniel': 3.336789404784213e-06,\n",
       " 'english_setter': 5.322591434264723e-08,\n",
       " 'german_shorthaired': 1.440359120863377e-08,\n",
       " 'great_pyrenees': 1.3074151761216513e-09,\n",
       " 'havanese': 1.597841525757815e-09,\n",
       " 'japanese_chin': 4.529465247316011e-09,\n",
       " 'keeshond': 1.696519369431826e-09,\n",
       " 'leonberger': 7.983156358193355e-10,\n",
       " 'miniature_pinscher': 6.107816474809624e-09,\n",
       " 'newfoundland': 1.4448579221948421e-08,\n",
       " 'pomeranian': 4.230615413547412e-09,\n",
       " 'pug': 1.5305550229993692e-11,\n",
       " 'saint_bernard': 3.068181708698603e-09,\n",
       " 'samoyed': 2.0837782888083467e-10,\n",
       " 'scottish_terrier': 1.5673877751254395e-09,\n",
       " 'shiba_inu': 8.008132351688957e-11,\n",
       " 'staffordshire_bull_terrier': 1.3512396757064948e-09,\n",
       " 'wheaten_terrier': 1.583207009936416e-09,\n",
       " 'yorkshire_terrier': 2.253112540984148e-08}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classify_image(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4142/3104820256.py:2: GradioDeprecationWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
      "  image = gr.inputs.Image(shape=(192,192))\n",
      "/tmp/ipykernel_4142/3104820256.py:2: GradioDeprecationWarning: `optional` parameter is deprecated, and it has no effect\n",
      "  image = gr.inputs.Image(shape=(192,192))\n",
      "/tmp/ipykernel_4142/3104820256.py:3: GradioDeprecationWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
      "  label = gr.outputs.Label()\n",
      "/tmp/ipykernel_4142/3104820256.py:3: GradioUnusedKwargWarning: You have unused kwarg parameters in Label, please remove them: {'type': 'auto'}\n",
      "  label = gr.outputs.Label()\n"
     ]
    }
   ],
   "source": [
    "#|export\n",
    "image = gr.inputs.Image(shape=(192,192))\n",
    "label = gr.outputs.Label()\n",
    "examples = ['basset.jpg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7860\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#|export\n",
    "intf = gr.Interface(fn=classify_image, inputs = image, outputs=label, examples=examples)\n",
    "intf.launch(inline=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): TimmBody(\n",
       "    (model): EfficientNet(\n",
       "      (conv_stem): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNormAct2d(\n",
       "        32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "        (drop): Identity()\n",
       "        (act): ReLU(inplace=True)\n",
       "      )\n",
       "      (blocks): Sequential(\n",
       "        (0): Sequential(\n",
       "          (0): EdgeResidual(\n",
       "            (conv_exp): Conv2d(32, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn1): BatchNormAct2d(\n",
       "              96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (se): Identity()\n",
       "            (conv_pwl): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn2): BatchNormAct2d(\n",
       "              24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): Identity()\n",
       "            )\n",
       "            (drop_path): Identity()\n",
       "          )\n",
       "        )\n",
       "        (1): Sequential(\n",
       "          (0): EdgeResidual(\n",
       "            (conv_exp): Conv2d(24, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "            (bn1): BatchNormAct2d(\n",
       "              192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (se): Identity()\n",
       "            (conv_pwl): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn2): BatchNormAct2d(\n",
       "              32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): Identity()\n",
       "            )\n",
       "            (drop_path): Identity()\n",
       "          )\n",
       "          (1): EdgeResidual(\n",
       "            (conv_exp): Conv2d(32, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn1): BatchNormAct2d(\n",
       "              256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (se): Identity()\n",
       "            (conv_pwl): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn2): BatchNormAct2d(\n",
       "              32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): Identity()\n",
       "            )\n",
       "            (drop_path): Identity()\n",
       "          )\n",
       "        )\n",
       "        (2): Sequential(\n",
       "          (0): EdgeResidual(\n",
       "            (conv_exp): Conv2d(32, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "            (bn1): BatchNormAct2d(\n",
       "              256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (se): Identity()\n",
       "            (conv_pwl): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn2): BatchNormAct2d(\n",
       "              48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): Identity()\n",
       "            )\n",
       "            (drop_path): Identity()\n",
       "          )\n",
       "          (1): EdgeResidual(\n",
       "            (conv_exp): Conv2d(48, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn1): BatchNormAct2d(\n",
       "              384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (se): Identity()\n",
       "            (conv_pwl): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn2): BatchNormAct2d(\n",
       "              48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): Identity()\n",
       "            )\n",
       "            (drop_path): Identity()\n",
       "          )\n",
       "          (2): EdgeResidual(\n",
       "            (conv_exp): Conv2d(48, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn1): BatchNormAct2d(\n",
       "              384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (se): Identity()\n",
       "            (conv_pwl): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn2): BatchNormAct2d(\n",
       "              48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): Identity()\n",
       "            )\n",
       "            (drop_path): Identity()\n",
       "          )\n",
       "          (3): EdgeResidual(\n",
       "            (conv_exp): Conv2d(48, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn1): BatchNormAct2d(\n",
       "              384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (se): Identity()\n",
       "            (conv_pwl): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn2): BatchNormAct2d(\n",
       "              48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): Identity()\n",
       "            )\n",
       "            (drop_path): Identity()\n",
       "          )\n",
       "        )\n",
       "        (3): Sequential(\n",
       "          (0): InvertedResidual(\n",
       "            (conv_pw): Conv2d(48, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): BatchNormAct2d(\n",
       "              384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (conv_dw): Conv2d(384, 384, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=384, bias=False)\n",
       "            (bn2): BatchNormAct2d(\n",
       "              384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (se): Identity()\n",
       "            (conv_pwl): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): BatchNormAct2d(\n",
       "              96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): Identity()\n",
       "            )\n",
       "            (drop_path): Identity()\n",
       "          )\n",
       "          (1): InvertedResidual(\n",
       "            (conv_pw): Conv2d(96, 768, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): BatchNormAct2d(\n",
       "              768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (conv_dw): Conv2d(768, 768, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=768, bias=False)\n",
       "            (bn2): BatchNormAct2d(\n",
       "              768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (se): Identity()\n",
       "            (conv_pwl): Conv2d(768, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): BatchNormAct2d(\n",
       "              96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): Identity()\n",
       "            )\n",
       "            (drop_path): Identity()\n",
       "          )\n",
       "          (2): InvertedResidual(\n",
       "            (conv_pw): Conv2d(96, 768, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): BatchNormAct2d(\n",
       "              768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (conv_dw): Conv2d(768, 768, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=768, bias=False)\n",
       "            (bn2): BatchNormAct2d(\n",
       "              768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (se): Identity()\n",
       "            (conv_pwl): Conv2d(768, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): BatchNormAct2d(\n",
       "              96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): Identity()\n",
       "            )\n",
       "            (drop_path): Identity()\n",
       "          )\n",
       "          (3): InvertedResidual(\n",
       "            (conv_pw): Conv2d(96, 768, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): BatchNormAct2d(\n",
       "              768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (conv_dw): Conv2d(768, 768, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=768, bias=False)\n",
       "            (bn2): BatchNormAct2d(\n",
       "              768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (se): Identity()\n",
       "            (conv_pwl): Conv2d(768, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): BatchNormAct2d(\n",
       "              96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): Identity()\n",
       "            )\n",
       "            (drop_path): Identity()\n",
       "          )\n",
       "          (4): InvertedResidual(\n",
       "            (conv_pw): Conv2d(96, 768, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): BatchNormAct2d(\n",
       "              768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (conv_dw): Conv2d(768, 768, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=768, bias=False)\n",
       "            (bn2): BatchNormAct2d(\n",
       "              768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (se): Identity()\n",
       "            (conv_pwl): Conv2d(768, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): BatchNormAct2d(\n",
       "              96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): Identity()\n",
       "            )\n",
       "            (drop_path): Identity()\n",
       "          )\n",
       "        )\n",
       "        (4): Sequential(\n",
       "          (0): InvertedResidual(\n",
       "            (conv_pw): Conv2d(96, 768, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): BatchNormAct2d(\n",
       "              768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (conv_dw): Conv2d(768, 768, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=768, bias=False)\n",
       "            (bn2): BatchNormAct2d(\n",
       "              768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (se): Identity()\n",
       "            (conv_pwl): Conv2d(768, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): BatchNormAct2d(\n",
       "              144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): Identity()\n",
       "            )\n",
       "            (drop_path): Identity()\n",
       "          )\n",
       "          (1): InvertedResidual(\n",
       "            (conv_pw): Conv2d(144, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): BatchNormAct2d(\n",
       "              1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (conv_dw): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1152, bias=False)\n",
       "            (bn2): BatchNormAct2d(\n",
       "              1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (se): Identity()\n",
       "            (conv_pwl): Conv2d(1152, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): BatchNormAct2d(\n",
       "              144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): Identity()\n",
       "            )\n",
       "            (drop_path): Identity()\n",
       "          )\n",
       "          (2): InvertedResidual(\n",
       "            (conv_pw): Conv2d(144, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): BatchNormAct2d(\n",
       "              1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (conv_dw): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1152, bias=False)\n",
       "            (bn2): BatchNormAct2d(\n",
       "              1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (se): Identity()\n",
       "            (conv_pwl): Conv2d(1152, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): BatchNormAct2d(\n",
       "              144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): Identity()\n",
       "            )\n",
       "            (drop_path): Identity()\n",
       "          )\n",
       "          (3): InvertedResidual(\n",
       "            (conv_pw): Conv2d(144, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): BatchNormAct2d(\n",
       "              1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (conv_dw): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1152, bias=False)\n",
       "            (bn2): BatchNormAct2d(\n",
       "              1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (se): Identity()\n",
       "            (conv_pwl): Conv2d(1152, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): BatchNormAct2d(\n",
       "              144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): Identity()\n",
       "            )\n",
       "            (drop_path): Identity()\n",
       "          )\n",
       "        )\n",
       "        (5): Sequential(\n",
       "          (0): InvertedResidual(\n",
       "            (conv_pw): Conv2d(144, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): BatchNormAct2d(\n",
       "              1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (conv_dw): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=1152, bias=False)\n",
       "            (bn2): BatchNormAct2d(\n",
       "              1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (se): Identity()\n",
       "            (conv_pwl): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): BatchNormAct2d(\n",
       "              192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): Identity()\n",
       "            )\n",
       "            (drop_path): Identity()\n",
       "          )\n",
       "          (1): InvertedResidual(\n",
       "            (conv_pw): Conv2d(192, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): BatchNormAct2d(\n",
       "              1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (conv_dw): Conv2d(1536, 1536, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1536, bias=False)\n",
       "            (bn2): BatchNormAct2d(\n",
       "              1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): ReLU(inplace=True)\n",
       "            )\n",
       "            (se): Identity()\n",
       "            (conv_pwl): Conv2d(1536, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): BatchNormAct2d(\n",
       "              192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "              (drop): Identity()\n",
       "              (act): Identity()\n",
       "            )\n",
       "            (drop_path): Identity()\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (conv_head): Conv2d(192, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn2): BatchNormAct2d(\n",
       "        1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True\n",
       "        (drop): Identity()\n",
       "        (act): ReLU(inplace=True)\n",
       "      )\n",
       "      (global_pool): SelectAdaptivePool2d(pool_type=avg, flatten=Flatten(start_dim=1, end_dim=-1))\n",
       "      (classifier): Identity()\n",
       "    )\n",
       "  )\n",
       "  (1): Sequential(\n",
       "    (0): AdaptiveConcatPool2d(\n",
       "      (ap): AdaptiveAvgPool2d(output_size=1)\n",
       "      (mp): AdaptiveMaxPool2d(output_size=1)\n",
       "    )\n",
       "    (1): fastai.layers.Flatten(full=False)\n",
       "    (2): BatchNorm1d(2560, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (3): Dropout(p=0.25, inplace=False)\n",
       "    (4): Linear(in_features=2560, out_features=512, bias=False)\n",
       "    (5): ReLU(inplace=True)\n",
       "    (6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (7): Dropout(p=0.5, inplace=False)\n",
       "    (8): Linear(in_features=512, out_features=37, bias=False)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = learn.model\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/fastai/torch_core.py:263: UserWarning: 'has_mps' is deprecated, please use 'torch.backends.mps.is_built()'\n",
      "  return getattr(torch, 'has_mps', False)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/fastai/torch_core.py:263: UserWarning: 'has_mps' is deprecated, please use 'torch.backends.mps.is_built()'\n",
      "  return getattr(torch, 'has_mps', False)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/fastai/torch_core.py:263: UserWarning: 'has_mps' is deprecated, please use 'torch.backends.mps.is_built()'\n",
      "  return getattr(torch, 'has_mps', False)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/fastai/torch_core.py:263: UserWarning: 'has_mps' is deprecated, please use 'torch.backends.mps.is_built()'\n",
      "  return getattr(torch, 'has_mps', False)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/fastai/torch_core.py:263: UserWarning: 'has_mps' is deprecated, please use 'torch.backends.mps.is_built()'\n",
      "  return getattr(torch, 'has_mps', False)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/fastai/torch_core.py:263: UserWarning: 'has_mps' is deprecated, please use 'torch.backends.mps.is_built()'\n",
      "  return getattr(torch, 'has_mps', False)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/fastai/torch_core.py:263: UserWarning: 'has_mps' is deprecated, please use 'torch.backends.mps.is_built()'\n",
      "  return getattr(torch, 'has_mps', False)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nbdev.export import nb_export\n",
    "\n",
    "nb_export('app.ipynb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}