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+ 2025-01-27 14:20:20,873 INFO ___FILE_ONLY___ ╠═ Downloading: Cloud Storage Command Line Tool ═╣
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377
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381
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383
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385
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389
+
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+ 2025-01-27 14:20:23,001 INFO ___FILE_ONLY___ ╠═ Downloading: Google Cloud CLI Core Libraries (Platfor... ═╣
391
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393
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394
+ 2025-01-27 14:20:24,083 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-core-nix-20240830134514.tar.gz HTTP/1.1" 200 2306
395
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396
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401
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+ 2025-01-27 14:20:24,344 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gcloud-crc32c-linux-x86_64-20250110133808.tar.gz HTTP/1.1" 200 1478989
413
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474
+
475
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476
+
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+ 2025-01-27 14:20:25,312 INFO ___FILE_ONLY___ ╠═ Downloading: gcloud cli dependencies (Platform Specific) ═╣
478
+
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480
+ 2025-01-27 14:20:25,315 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
481
+ 2025-01-27 14:20:25,559 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gcloud-deps-linux-x86_64-20210416153011.tar.gz HTTP/1.1" 200 104
482
+ 2025-01-27 14:20:25,560 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
483
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484
+
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486
+
487
+ 2025-01-27 14:20:25,562 INFO ___FILE_ONLY___ ╠═ Installing: BigQuery Command Line Tool ═╣
488
+
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490
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510
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533
+ 2025-01-27 14:20:25,777 INFO ___FILE_ONLY___ ═
534
+ 2025-01-27 14:20:25,779 INFO ___FILE_ONLY___ ═
535
+ 2025-01-27 14:20:25,781 INFO ___FILE_ONLY___ ═
536
+ 2025-01-27 14:20:25,783 INFO ___FILE_ONLY___ ═
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+ 2025-01-27 14:20:25,785 INFO ___FILE_ONLY___ ═
538
+ 2025-01-27 14:20:25,787 INFO ___FILE_ONLY___ ═
539
+ 2025-01-27 14:20:25,790 INFO ___FILE_ONLY___ ═
540
+ 2025-01-27 14:20:25,792 INFO ___FILE_ONLY___ ═
541
+ 2025-01-27 14:20:25,794 INFO ___FILE_ONLY___ ═
542
+ 2025-01-27 14:20:25,796 INFO ___FILE_ONLY___ ═
543
+ 2025-01-27 14:20:25,798 INFO ___FILE_ONLY___ ═
544
+ 2025-01-27 14:20:25,801 INFO ___FILE_ONLY___ ═
545
+ 2025-01-27 14:20:25,802 INFO ___FILE_ONLY___ ═
546
+ 2025-01-27 14:20:25,804 INFO ___FILE_ONLY___ ═
547
+ 2025-01-27 14:20:25,806 INFO ___FILE_ONLY___ ═
548
+ 2025-01-27 14:20:25,808 INFO ___FILE_ONLY___ ═
549
+ 2025-01-27 14:20:25,811 INFO ___FILE_ONLY___ ═
550
+ 2025-01-27 14:20:25,811 INFO ___FILE_ONLY___ ╝
551
+
552
+ 2025-01-27 14:20:25,819 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
553
+
554
+ 2025-01-27 14:20:25,819 INFO ___FILE_ONLY___ ╠═ Installing: BigQuery Command Line Tool (Platform Spec... ═╣
555
+
556
+ 2025-01-27 14:20:25,819 INFO ___FILE_ONLY___ ╚
557
+ 2025-01-27 14:20:25,819 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
558
+ 2025-01-27 14:20:25,820 INFO ___FILE_ONLY___ ╝
559
+
560
+ 2025-01-27 14:20:25,824 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
561
+
562
+ 2025-01-27 14:20:25,824 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.11 ═╣
563
+
564
+ 2025-01-27 14:20:25,824 INFO ___FILE_ONLY___ ╚
565
+ 2025-01-27 14:20:25,826 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
566
+ 2025-01-27 14:20:25,826 INFO ___FILE_ONLY___ ╝
567
+
568
+ 2025-01-27 14:20:25,828 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
569
+
570
+ 2025-01-27 14:20:25,828 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.11 (Platform Specific) ═╣
571
+
572
+ 2025-01-27 14:20:25,828 INFO ___FILE_ONLY___ ╚
573
+ 2025-01-27 14:20:27,817 INFO ___FILE_ONLY___ ═
574
+ 2025-01-27 14:20:27,832 INFO ___FILE_ONLY___ ═
575
+ 2025-01-27 14:20:27,846 INFO ___FILE_ONLY___ ═
576
+ 2025-01-27 14:20:27,858 INFO ___FILE_ONLY___ ═
577
+ 2025-01-27 14:20:27,871 INFO ___FILE_ONLY___ ═
578
+ 2025-01-27 14:20:27,884 INFO ___FILE_ONLY___ ═
579
+ 2025-01-27 14:20:27,897 INFO ___FILE_ONLY___ ═
580
+ 2025-01-27 14:20:27,910 INFO ___FILE_ONLY___ ═
581
+ 2025-01-27 14:20:27,923 INFO ___FILE_ONLY___ ═
582
+ 2025-01-27 14:20:27,936 INFO ___FILE_ONLY___ ═
583
+ 2025-01-27 14:20:27,948 INFO ___FILE_ONLY___ ═
584
+ 2025-01-27 14:20:27,961 INFO ___FILE_ONLY___ ═
585
+ 2025-01-27 14:20:27,973 INFO ___FILE_ONLY___ ═
586
+ 2025-01-27 14:20:27,986 INFO ___FILE_ONLY___ ═
587
+ 2025-01-27 14:20:27,999 INFO ___FILE_ONLY___ ═
588
+ 2025-01-27 14:20:28,012 INFO ___FILE_ONLY___ ═
589
+ 2025-01-27 14:20:28,026 INFO ___FILE_ONLY___ ═
590
+ 2025-01-27 14:20:28,039 INFO ___FILE_ONLY___ ═
591
+ 2025-01-27 14:20:28,052 INFO ___FILE_ONLY___ ═
592
+ 2025-01-27 14:20:28,065 INFO ___FILE_ONLY___ ═
593
+ 2025-01-27 14:20:28,078 INFO ___FILE_ONLY___ ═
594
+ 2025-01-27 14:20:28,091 INFO ___FILE_ONLY___ ═
595
+ 2025-01-27 14:20:28,104 INFO ___FILE_ONLY___ ═
596
+ 2025-01-27 14:20:28,117 INFO ___FILE_ONLY___ ═
597
+ 2025-01-27 14:20:28,133 INFO ___FILE_ONLY___ ═
598
+ 2025-01-27 14:20:28,148 INFO ___FILE_ONLY___ ═
599
+ 2025-01-27 14:20:28,162 INFO ___FILE_ONLY___ ═
600
+ 2025-01-27 14:20:28,176 INFO ___FILE_ONLY___ ═
601
+ 2025-01-27 14:20:28,191 INFO ___FILE_ONLY___ ═
602
+ 2025-01-27 14:20:28,205 INFO ___FILE_ONLY___ ═
603
+ 2025-01-27 14:20:28,219 INFO ___FILE_ONLY___ ═
604
+ 2025-01-27 14:20:28,234 INFO ___FILE_ONLY___ ═
605
+ 2025-01-27 14:20:28,248 INFO ___FILE_ONLY___ ═
606
+ 2025-01-27 14:20:28,262 INFO ___FILE_ONLY___ ═
607
+ 2025-01-27 14:20:28,278 INFO ___FILE_ONLY___ ═
608
+ 2025-01-27 14:20:28,293 INFO ___FILE_ONLY___ ═
609
+ 2025-01-27 14:20:29,070 INFO ___FILE_ONLY___ ═
610
+ 2025-01-27 14:20:29,097 INFO ___FILE_ONLY___ ═
611
+ 2025-01-27 14:20:29,121 INFO ___FILE_ONLY___ ═
612
+ 2025-01-27 14:20:29,143 INFO ___FILE_ONLY___ ═
613
+ 2025-01-27 14:20:29,164 INFO ___FILE_ONLY___ ═
614
+ 2025-01-27 14:20:29,302 INFO ___FILE_ONLY___ ═
615
+ 2025-01-27 14:20:29,332 INFO ___FILE_ONLY___ ═
616
+ 2025-01-27 14:20:29,352 INFO ___FILE_ONLY___ ═
617
+ 2025-01-27 14:20:29,369 INFO ___FILE_ONLY___ ═
618
+ 2025-01-27 14:20:29,388 INFO ___FILE_ONLY___ ═
619
+ 2025-01-27 14:20:29,419 INFO ___FILE_ONLY___ ═
620
+ 2025-01-27 14:20:29,442 INFO ___FILE_ONLY___ ═
621
+ 2025-01-27 14:20:29,461 INFO ___FILE_ONLY___ ═
622
+ 2025-01-27 14:20:29,476 INFO ___FILE_ONLY___ ═
623
+ 2025-01-27 14:20:29,570 INFO ___FILE_ONLY___ ═
624
+ 2025-01-27 14:20:29,591 INFO ___FILE_ONLY___ ═
625
+ 2025-01-27 14:20:29,611 INFO ___FILE_ONLY___ ═
626
+ 2025-01-27 14:20:29,630 INFO ___FILE_ONLY___ ═
627
+ 2025-01-27 14:20:29,675 INFO ___FILE_ONLY___ ═
628
+ 2025-01-27 14:20:30,043 INFO ___FILE_ONLY___ ═
629
+ 2025-01-27 14:20:30,061 INFO ___FILE_ONLY___ ═
630
+ 2025-01-27 14:20:30,083 INFO ___FILE_ONLY___ ═
631
+ 2025-01-27 14:20:30,103 INFO ___FILE_ONLY___ ═
632
+ 2025-01-27 14:20:30,470 INFO ___FILE_ONLY___ ═
633
+ 2025-01-27 14:20:30,471 INFO ___FILE_ONLY___ ╝
634
+
635
+ 2025-01-27 14:20:30,521 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
636
+
637
+ 2025-01-27 14:20:30,521 INFO ___FILE_ONLY___ ╠═ Installing: Cloud Storage Command Line Tool ═╣
638
+
639
+ 2025-01-27 14:20:30,521 INFO ___FILE_ONLY___ ╚
640
+ 2025-01-27 14:20:31,058 INFO ___FILE_ONLY___ ═
641
+ 2025-01-27 14:20:31,076 INFO ___FILE_ONLY___ ═
642
+ 2025-01-27 14:20:31,097 INFO ___FILE_ONLY___ ═
643
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644
+ 2025-01-27 14:20:31,131 INFO ___FILE_ONLY___ ═
645
+ 2025-01-27 14:20:31,144 INFO ___FILE_ONLY___ ═
646
+ 2025-01-27 14:20:31,159 INFO ___FILE_ONLY___ ═
647
+ 2025-01-27 14:20:31,172 INFO ___FILE_ONLY___ ═
648
+ 2025-01-27 14:20:31,187 INFO ___FILE_ONLY___ ═
649
+ 2025-01-27 14:20:31,201 INFO ___FILE_ONLY___ ═
650
+ 2025-01-27 14:20:31,215 INFO ___FILE_ONLY___ ═
651
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652
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653
+ 2025-01-27 14:20:31,247 INFO ___FILE_ONLY___ ═
654
+ 2025-01-27 14:20:31,257 INFO ___FILE_ONLY___ ═
655
+ 2025-01-27 14:20:31,267 INFO ___FILE_ONLY___ ═
656
+ 2025-01-27 14:20:31,279 INFO ___FILE_ONLY___ ═
657
+ 2025-01-27 14:20:31,290 INFO ___FILE_ONLY___ ═
658
+ 2025-01-27 14:20:31,301 INFO ___FILE_ONLY___ ═
659
+ 2025-01-27 14:20:31,312 INFO ___FILE_ONLY___ ═
660
+ 2025-01-27 14:20:31,325 INFO ___FILE_ONLY___ ═
661
+ 2025-01-27 14:20:31,340 INFO ___FILE_ONLY___ ═
662
+ 2025-01-27 14:20:31,355 INFO ___FILE_ONLY___ ═
663
+ 2025-01-27 14:20:31,365 INFO ___FILE_ONLY___ ═
664
+ 2025-01-27 14:20:31,379 INFO ___FILE_ONLY___ ═
665
+ 2025-01-27 14:20:31,395 INFO ___FILE_ONLY___ ═
666
+ 2025-01-27 14:20:31,413 INFO ___FILE_ONLY___ ═
667
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668
+ 2025-01-27 14:20:31,449 INFO ___FILE_ONLY___ ═
669
+ 2025-01-27 14:20:31,470 INFO ___FILE_ONLY___ ═
670
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671
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672
+ 2025-01-27 14:20:31,512 INFO ___FILE_ONLY___ ═
673
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674
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675
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676
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677
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678
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679
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680
+ 2025-01-27 14:20:31,602 INFO ___FILE_ONLY___ ═
681
+ 2025-01-27 14:20:31,617 INFO ___FILE_ONLY___ ═
682
+ 2025-01-27 14:20:31,627 INFO ___FILE_ONLY___ ═
683
+ 2025-01-27 14:20:31,641 INFO ___FILE_ONLY___ ═
684
+ 2025-01-27 14:20:31,657 INFO ___FILE_ONLY___ ═
685
+ 2025-01-27 14:20:31,669 INFO ___FILE_ONLY___ ═
686
+ 2025-01-27 14:20:31,678 INFO ___FILE_ONLY___ ═
687
+ 2025-01-27 14:20:31,689 INFO ___FILE_ONLY___ ═
688
+ 2025-01-27 14:20:31,700 INFO ___FILE_ONLY___ ═
689
+ 2025-01-27 14:20:31,713 INFO ___FILE_ONLY___ ═
690
+ 2025-01-27 14:20:31,730 INFO ___FILE_ONLY___ ═
691
+ 2025-01-27 14:20:31,742 INFO ___FILE_ONLY___ ═
692
+ 2025-01-27 14:20:31,758 INFO ___FILE_ONLY___ ═
693
+ 2025-01-27 14:20:31,772 INFO ___FILE_ONLY___ ═
694
+ 2025-01-27 14:20:31,809 INFO ___FILE_ONLY___ ═
695
+ 2025-01-27 14:20:31,819 INFO ___FILE_ONLY___ ═
696
+ 2025-01-27 14:20:31,830 INFO ___FILE_ONLY___ ═
697
+ 2025-01-27 14:20:31,841 INFO ___FILE_ONLY___ ═
698
+ 2025-01-27 14:20:31,853 INFO ___FILE_ONLY___ ═
699
+ 2025-01-27 14:20:31,868 INFO ___FILE_ONLY___ ═
700
+ 2025-01-27 14:20:31,868 INFO ___FILE_ONLY___ ╝
701
+
702
+ 2025-01-27 14:20:31,900 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
703
+
704
+ 2025-01-27 14:20:31,900 INFO ___FILE_ONLY___ ╠═ Installing: Cloud Storage Command Line Tool (Platform... ═╣
705
+
706
+ 2025-01-27 14:20:31,900 INFO ___FILE_ONLY___ ╚
707
+ 2025-01-27 14:20:31,901 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
708
+ 2025-01-27 14:20:31,901 INFO ___FILE_ONLY___ ╝
709
+
710
+ 2025-01-27 14:20:31,905 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
711
+
712
+ 2025-01-27 14:20:31,905 INFO ___FILE_ONLY___ ╠═ Installing: Default set of gcloud commands ═╣
713
+
714
+ 2025-01-27 14:20:31,905 INFO ___FILE_ONLY___ ╚
715
+ 2025-01-27 14:20:31,907 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
716
+ 2025-01-27 14:20:31,908 INFO ___FILE_ONLY___ ╝
717
+
718
+ 2025-01-27 14:20:31,909 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
719
+
720
+ 2025-01-27 14:20:31,909 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CLI Core Libraries (Platform... ═╣
721
+
722
+ 2025-01-27 14:20:31,909 INFO ___FILE_ONLY___ ╚
723
+ 2025-01-27 14:20:31,910 INFO ___FILE_ONLY___ ══════════════════════════════
724
+ 2025-01-27 14:20:31,911 INFO ___FILE_ONLY___ ══════════════════════════════
725
+ 2025-01-27 14:20:31,911 INFO ___FILE_ONLY___ ╝
726
+
727
+ 2025-01-27 14:20:31,915 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
728
+
729
+ 2025-01-27 14:20:31,915 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool ═╣
730
+
731
+ 2025-01-27 14:20:31,915 INFO ___FILE_ONLY___ ╚
732
+ 2025-01-27 14:20:31,917 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
733
+ 2025-01-27 14:20:31,918 INFO ___FILE_ONLY___ ╝
734
+
735
+ 2025-01-27 14:20:31,919 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
736
+
737
+ 2025-01-27 14:20:31,919 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool (Platform S... ═╣
738
+
739
+ 2025-01-27 14:20:31,919 INFO ___FILE_ONLY___ ╚
740
+ 2025-01-27 14:20:31,958 INFO ___FILE_ONLY___ ══════════════════════════════
741
+ 2025-01-27 14:20:31,958 INFO ___FILE_ONLY___ ══════════════════════════════
742
+ 2025-01-27 14:20:31,959 INFO ___FILE_ONLY___ ╝
743
+
744
+ 2025-01-27 14:20:31,963 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
745
+
746
+ 2025-01-27 14:20:31,964 INFO ___FILE_ONLY___ ╠═ Installing: gcloud cli dependencies (Platform Specific) ═╣
747
+
748
+ 2025-01-27 14:20:31,964 INFO ___FILE_ONLY___ ╚
749
+ 2025-01-27 14:20:31,964 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
750
+ 2025-01-27 14:20:31,964 INFO ___FILE_ONLY___ ╝
751
+
752
+ 2025-01-27 14:20:31,969 DEBUG root Updating notification cache...
753
+ 2025-01-27 14:20:31,969 INFO ___FILE_ONLY___
754
+
755
+ 2025-01-27 14:20:31,971 INFO ___FILE_ONLY___ Performing post processing steps...
756
+ 2025-01-27 14:20:31,972 DEBUG root Executing command: ['/tools/google-cloud-sdk/bin/gcloud', 'components', 'post-process']
757
+ 2025-01-27 14:20:40,196 DEBUG ___FILE_ONLY___
758
+ 2025-01-27 14:20:40,196 DEBUG ___FILE_ONLY___
759
+ 2025-01-27 14:20:40,250 INFO ___FILE_ONLY___
760
+ Update done!
761
+
762
+
763
+ 2025-01-27 14:20:40,253 DEBUG root Chosen display Format:none
764
+ 2025-01-27 14:20:40,253 INFO root Display format: "none"
.config/logs/2025.01.27/14.20.32.420325.log ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ 2025-01-27 14:20:32,421 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2025-01-27 14:20:32,423 DEBUG root Loaded Command Group: ['gcloud', 'components', 'post_process']
3
+ 2025-01-27 14:20:32,425 DEBUG root Running [gcloud.components.post-process] with arguments: []
4
+ 2025-01-27 14:20:40,081 DEBUG root Chosen display Format:none
5
+ 2025-01-27 14:20:40,082 INFO root Display format: "none"
.config/logs/2025.01.27/14.20.40.827364.log ADDED
@@ -0,0 +1,152 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2025-01-27 14:20:40,828 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2025-01-27 14:20:40,830 DEBUG root Loaded Command Group: ['gcloud', 'components', 'update']
3
+ 2025-01-27 14:20:40,832 DEBUG root Running [gcloud.components.update] with arguments: [--quiet: "True", COMPONENT-IDS:8: "['gcloud', 'core', 'bq', 'gsutil', 'compute', 'preview', 'alpha', 'beta']"]
4
+ 2025-01-27 14:20:40,833 INFO ___FILE_ONLY___ Beginning update. This process may take several minutes.
5
+
6
+ 2025-01-27 14:20:40,841 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
7
+ 2025-01-27 14:20:41,928 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components-2.json HTTP/1.1" 200 227832
8
+ 2025-01-27 14:20:41,943 WARNING root Component [compute] no longer exists.
9
+ 2025-01-27 14:20:41,944 INFO ___FILE_ONLY___
10
+
11
+ 2025-01-27 14:20:41,944 INFO ___FILE_ONLY___
12
+ Your current Google Cloud CLI version is: 507.0.0
13
+
14
+ 2025-01-27 14:20:41,944 INFO ___FILE_ONLY___ Installing components from version: 507.0.0
15
+
16
+ 2025-01-27 14:20:41,944 INFO ___FILE_ONLY___
17
+
18
+ 2025-01-27 14:20:41,944 DEBUG root Chosen display Format:table[box,title="These components will be removed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
19
+ 2025-01-27 14:20:41,945 DEBUG root Chosen display Format:table[box,title="These components will be updated."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
20
+ 2025-01-27 14:20:41,945 DEBUG root Chosen display Format:table[box,title="These components will be installed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
21
+ 2025-01-27 14:20:41,960 INFO ___FILE_ONLY___ ┌────────────────────────────────────────────────┐
22
+ 2025-01-27 14:20:41,960 INFO ___FILE_ONLY___
23
+
24
+ 2025-01-27 14:20:41,960 INFO ___FILE_ONLY___ │ These components will be installed. │
25
+ 2025-01-27 14:20:41,960 INFO ___FILE_ONLY___
26
+
27
+ 2025-01-27 14:20:41,960 INFO ___FILE_ONLY___ ├─────────────────────────┬────────────┬─────────┤
28
+ 2025-01-27 14:20:41,960 INFO ___FILE_ONLY___
29
+
30
+ 2025-01-27 14:20:41,960 INFO ___FILE_ONLY___ │ Name │ Version │ Size │
31
+ 2025-01-27 14:20:41,960 INFO ___FILE_ONLY___
32
+
33
+ 2025-01-27 14:20:41,961 INFO ___FILE_ONLY___ ├─────────────────────────┼────────────┼─────────┤
34
+ 2025-01-27 14:20:41,961 INFO ___FILE_ONLY___
35
+
36
+ 2025-01-27 14:20:41,961 INFO ___FILE_ONLY___ │
37
+ 2025-01-27 14:20:41,961 INFO ___FILE_ONLY___ gcloud Alpha Commands
38
+ 2025-01-27 14:20:41,961 INFO ___FILE_ONLY___
39
+ 2025-01-27 14:20:41,961 INFO ___FILE_ONLY___ │
40
+ 2025-01-27 14:20:41,961 INFO ___FILE_ONLY___ 2025.01.17
41
+ 2025-01-27 14:20:41,961 INFO ___FILE_ONLY___
42
+ 2025-01-27 14:20:41,961 INFO ___FILE_ONLY___ │
43
+ 2025-01-27 14:20:41,961 INFO ___FILE_ONLY___ < 1 MiB
44
+ 2025-01-27 14:20:41,961 INFO ___FILE_ONLY___
45
+ 2025-01-27 14:20:41,961 INFO ___FILE_ONLY___ │
46
+ 2025-01-27 14:20:41,961 INFO ___FILE_ONLY___
47
+
48
+ 2025-01-27 14:20:41,961 INFO ___FILE_ONLY___ │
49
+ 2025-01-27 14:20:41,961 INFO ___FILE_ONLY___ gcloud Beta Commands
50
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78
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80
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83
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.gitattributes CHANGED
@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
+ sample_data/mnist_test.csv filter=lfs diff=lfs merge=lfs -text
37
+ sample_data/mnist_train_small.csv filter=lfs diff=lfs merge=lfs -text
README.md CHANGED
@@ -1,12 +1,6 @@
1
  ---
2
- title: ITI110 Gradio
3
- emoji:
4
- colorFrom: yellow
5
- colorTo: purple
6
  sdk: gradio
7
  sdk_version: 5.16.2
8
- app_file: app.py
9
- pinned: false
10
  ---
11
-
12
- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
1
  ---
2
+ title: ITI110-Gradio
3
+ app_file: iti110_birds_cnn_resnet_gpt_4o.py
 
 
4
  sdk: gradio
5
  sdk_version: 5.16.2
 
 
6
  ---
 
 
iti110_birds_cnn_resnet_gpt_4o.py ADDED
@@ -0,0 +1,315 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -*- coding: utf-8 -*-
2
+ """ITI110-Birds-CNN-ResNet-GPT-4o.ipynb
3
+
4
+ Automatically generated by Colab.
5
+
6
+ Original file is located at
7
+ https://colab.research.google.com/drive/16uUvA-YukBceEUQd8qBCISg9K3raRmAh
8
+
9
+ Based on the above pipeline, write a python code to create a Gradio UI which performs the following: 1. An input text box for user to input the path to the dataset 2. An output text bx to display the distribution of samples across species 3. Automatically perform 1.2 Audio Processing 4. Put 3 checkboxes for enabling or disabling Pitch Shifting, Time Stretching, and Background Noise 5. Use bullet buttons to allow user to select using MFCC, Spectrograms or Chroma for Feature Extraction 6. Use bullet buttons to allow user to select building either RestNet50 or CNN model 7. Let the user press a button to train and save the model 8. Let the user press a button to evaluate the model, output the results 9. Let the user press a button to perform hyperparameter tuning and save the model file 10. Let the user press a button to perform Learning Rate Reduction Callback and save the model file 11. Let the user press a button to evaluate the two fine-tuned models, output the results
10
+
11
+ Below is the Python code to create a Gradio UI that performs the tasks as specified. This code assumes you have already installed Gradio (pip install gradio) and other necessary libraries.
12
+ """
13
+
14
+
15
+ import os
16
+ import numpy as np
17
+ import pandas as pd
18
+ import librosa
19
+ import soundfile as sf
20
+ from pydub import AudioSegment
21
+ import gradio as gr
22
+ from sklearn.model_selection import train_test_split
23
+ from sklearn.preprocessing import LabelEncoder
24
+ from tensorflow.keras.utils import to_categorical
25
+ from tensorflow.keras.models import Sequential, load_model, Model
26
+ from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, Input
27
+ from tensorflow.keras.applications import ResNet50
28
+ from tensorflow.keras.callbacks import ReduceLROnPlateau
29
+ import matplotlib.pyplot as plt
30
+ import seaborn as sns
31
+ from sklearn.metrics import classification_report, confusion_matrix
32
+
33
+ # Global variables
34
+ dataset_path = ''
35
+ species_folders = []
36
+ label_encoder = LabelEncoder()
37
+ X_train, X_test, y_train, y_test = None, None, None, None
38
+ history = None
39
+ model = None
40
+
41
+ def data_exploration(path):
42
+ global dataset_path, species_folders
43
+ dataset_path = path
44
+ species_folders = [f for f in os.listdir(dataset_path) if os.path.isdir(os.path.join(dataset_path, f))]
45
+ species_count = Counter()
46
+ for species in species_folders:
47
+ species_files = os.listdir(os.path.join(dataset_path, species))
48
+ species_count[species] = len(species_files)
49
+ species_df = pd.DataFrame(list(species_count.items()), columns=['Species', 'Count'])
50
+ species_df.to_csv('species_distribution.csv', index=False)
51
+ return species_df.to_string(index=False)
52
+
53
+ def audio_processing():
54
+ for species in species_folders:
55
+ species_folder = os.path.join(dataset_path, species)
56
+ for file_name in os.listdir(species_folder):
57
+ if file_name.endswith('.mp3'):
58
+ mp3_path = os.path.join(species_folder, file_name)
59
+ wav_path = os.path.join(species_folder, file_name.replace('.mp3', '.wav'))
60
+ convert_mp3_to_wav(mp3_path, wav_path)
61
+ os.remove(mp3_path)
62
+ for species in species_folders:
63
+ species_folder = os.path.join(dataset_path, species)
64
+ for file_name in os.listdir(species_folder):
65
+ if file_name.endswith('.wav'):
66
+ wav_path = os.path.join(species_folder, file_name)
67
+ preprocess_audio(wav_path, wav_path)
68
+ return "Audio processing completed."
69
+
70
+ def convert_mp3_to_wav(input_path, output_path):
71
+ audio = AudioSegment.from_mp3(input_path)
72
+ audio.export(output_path, format="wav")
73
+
74
+ def preprocess_audio(input_path, output_path, sample_rate=22050):
75
+ y, sr = librosa.load(input_path, sr=sample_rate)
76
+ y_trimmed, _ = librosa.effects.trim(y)
77
+ y_normalized = librosa.util.normalize(y_trimmed)
78
+ sf.write(output_path, y_normalized, sample_rate)
79
+
80
+ def data_augmentation(pitch_shifting, time_stretching, background_noise):
81
+ for species in species_folders:
82
+ species_folder = os.path.join(dataset_path, species)
83
+ if pitch_shifting:
84
+ apply_pitch_shift(species_folder)
85
+ if time_stretching:
86
+ apply_time_stretch(species_folder)
87
+ if background_noise:
88
+ apply_background_noise(species_folder)
89
+ return "Data augmentation completed."
90
+
91
+ def apply_pitch_shift(species_folder):
92
+ for file_name in os.listdir(species_folder):
93
+ if file_name.endswith('.wav'):
94
+ wav_path = os.path.join(species_folder, file_name)
95
+ y, sr = librosa.load(wav_path, sr=None)
96
+ y_shifted = librosa.effects.pitch_shift(y, sr, n_steps=2)
97
+ shifted_path = wav_path.replace('.wav', '_pitch_shifted.wav')
98
+ sf.write(shifted_path, y_shifted, sr)
99
+
100
+ def apply_time_stretch(species_folder):
101
+ for file_name in os.listdir(species_folder):
102
+ if file_name.endswith('.wav'):
103
+ wav_path = os.path.join(species_folder, file_name)
104
+ y, sr = librosa.load(wav_path, sr=None)
105
+ y_stretched = librosa.effects.time_stretch(y, rate=1.2)
106
+ stretched_path = wav_path.replace('.wav', '_time_stretched.wav')
107
+ sf.write(stretched_path, y_stretched, sr)
108
+
109
+ def apply_background_noise(species_folder):
110
+ for file_name in os.listdir(species_folder):
111
+ if file_name.endswith('.wav'):
112
+ wav_path = os.path.join(species_folder, file_name)
113
+ y, sr = librosa.load(wav_path, sr=None)
114
+ noise = np.random.randn(len(y))
115
+ y_noisy = y + 0.005 * noise
116
+ noisy_path = wav_path.replace('.wav', '_noisy.wav')
117
+ sf.write(noisy_path, y_noisy, sr)
118
+
119
+ def feature_extraction(feature_type):
120
+ output_folder = f'path_to_output_{feature_type.lower()}'
121
+ if feature_type == 'MFCC':
122
+ extract_features = extract_mfcc
123
+ elif feature_type == 'Spectrogram':
124
+ extract_features = extract_spectrogram
125
+ elif feature_type == 'Chroma':
126
+ extract_features = extract_chroma_features
127
+
128
+ for species in species_folders:
129
+ species_folder = os.path.join(dataset_path, species)
130
+ species_output_folder = os.path.join(output_folder, species)
131
+ extract_features(species_folder, species_output_folder)
132
+
133
+ return f"{feature_type} features extracted and saved."
134
+
135
+ def extract_mfcc(species_folder, output_folder):
136
+ if not os.path.exists(output_folder):
137
+ os.makedirs(output_folder)
138
+ for file_name in os.listdir(species_folder):
139
+ if file_name.endswith('.wav'):
140
+ wav_path = os.path.join(species_folder, file_name)
141
+ y, sr = librosa.load(wav_path, sr=None)
142
+ mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
143
+ feature_path = os.path.join(output_folder, file_name.replace('.wav', '.npy'))
144
+ np.save(feature_path, mfccs)
145
+
146
+ def extract_spectrogram(species_folder, output_folder):
147
+ if not os.path.exists(output_folder):
148
+ os.makedirs(output_folder)
149
+ for file_name in os.listdir(species_folder):
150
+ if file_name.endswith('.wav'):
151
+ wav_path = os.path.join(species_folder, file_name)
152
+ y, sr = librosa.load(wav_path, sr=None)
153
+ S = librosa.feature.melspectrogram(y=y, sr=sr)
154
+ S_DB = librosa.power_to_db(S, ref=np.max)
155
+ feature_path = os.path.join(output_folder, file_name.replace('.wav', '.npy'))
156
+ np.save(feature_path, S_DB)
157
+
158
+ def extract_chroma_features(species_folder, output_folder):
159
+ if not os.path.exists(output_folder):
160
+ os.makedirs(output_folder)
161
+ for file_name in os.listdir(species_folder):
162
+ if file_name.endswith('.wav'):
163
+ wav_path = os.path.join(species_folder, file_name)
164
+ y, sr = librosa.load(wav_path, sr=None)
165
+ chroma = librosa.feature.chroma_stft(y=y, sr=sr)
166
+ feature_path = os.path.join(output_folder, file_name.replace('.wav', '_chroma.npy'))
167
+ np.save(feature_path, chroma)
168
+
169
+ def load_features_and_labels(feature_folder):
170
+ X = []
171
+ y = []
172
+ species_list = os.listdir(feature_folder)
173
+ for species in species_list:
174
+ species_folder = os.path.join(feature_folder, species)
175
+ for file_name in os.listdir(species_folder):
176
+ if file_name.endswith('.npy'):
177
+ feature_path = os.path.join(species_folder, file_name)
178
+ features = np.load(feature_path)
179
+ X.append(features)
180
+ y.append(species)
181
+ return np.array(X), np.array(y)
182
+
183
+ def build_model(model_type, feature_type):
184
+ global X_train, X_test, y_train, y_test, label_encoder, model
185
+ feature_folder = f'path_to_output_{feature_type.lower()}'
186
+ X, y = load_features_and_labels(feature_folder)
187
+ X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
188
+ label_encoder = LabelEncoder()
189
+ y_train_encoded = label_encoder.fit_transform(y_train)
190
+ y_test_encoded = label_encoder.transform(y_test)
191
+ y_train_categorical = to_categorical(y_train_encoded)
192
+ y_test_categorical = to_categorical(y_test_encoded)
193
+ np.save('classes.npy', label_encoder.classes_)
194
+ X_train_reshaped = np.stack([np.repeat(x[..., np.newaxis], 3, axis=-1) for x in X_train])
195
+ X_test_reshaped = np.stack([np.repeat(x[..., np.newaxis], 3, axis=-1) for x in X_test])
196
+
197
+ if model_type == 'ResNet50':
198
+ input_tensor = Input(shape=(X_train.shape[1], X_train.shape[2], 3))
199
+ base_model = ResNet50(include_top=False, weights='imagenet', input_tensor=input_tensor)
200
+ x = Flatten()(base_model.output)
201
+ x = Dense(128, activation='relu')(x)
202
+ x = Dropout(0.3)(x)
203
+ output_tensor = Dense(len(label_encoder.classes_), activation='softmax')(x)
204
+ model = Model(inputs=input_tensor, outputs=output_tensor)
205
+ else:
206
+ model = Sequential([
207
+ Conv2D(32, (3, 3), activation='relu', input_shape=(X_train.shape[1], X_train.shape[2], 3)),
208
+ MaxPooling2D((2, 2)),
209
+ Dropout(0.3),
210
+ Conv2D(64, (3, 3), activation='relu'),
211
+ MaxPooling2D((2, 2)),
212
+ Dropout(0.3),
213
+ Flatten(),
214
+ Dense(128, activation='relu'),
215
+ Dropout(0.3),
216
+ Dense(len(label_encoder.classes_), activation='softmax')
217
+ ])
218
+
219
+ model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
220
+ model.summary()
221
+ return "Model built."
222
+
223
+ def train_model():
224
+ global history
225
+ history = model.fit(X_train_reshaped, y_train_categorical, epochs=30, batch_size=32, validation_data=(X_test_reshaped, y_test_categorical))
226
+ model.save('bird_species_model.h5')
227
+ return "Model training completed and saved as 'bird_species_model.h5'."
228
+
229
+ def evaluate_model():
230
+ test_loss, test_accuracy = model.evaluate(X_test_reshaped, y_test_categorical)
231
+ y_pred = model.predict(X_test_reshaped)
232
+ y_pred_classes = np.argmax(y_pred, axis=1)
233
+ y_true_classes = np.argmax(y_test_categorical, axis=1)
234
+ report = classification_report(y_true_classes, y_pred_classes, target_names=label_encoder.classes_)
235
+ conf_matrix = confusion_matrix(y_true_classes, y_pred_classes)
236
+ plt.figure(figsize=(12, 8))
237
+ sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=label_encoder.classes_, yticklabels=label_encoder.classes_)
238
+ plt.xlabel('Predicted Label')
239
+ plt.ylabel('True Label')
240
+ plt.title('Confusion Matrix')
241
+ plt.show()
242
+ return f"Test Loss: {test_loss}\nTest Accuracy: {test_accuracy}\n\nClassification Report:\n{report}"
243
+
244
+ def hyperparameter_tuning():
245
+ global history
246
+ reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.001)
247
+ history = model.fit(X_train_reshaped, y_train_categorical, epochs=50, batch_size=32, validation_data=(X_test_reshaped, y_test_categorical), callbacks=[reduce_lr])
248
+ model.save('bird_species_model_fine_tuned_v1.h5')
249
+ return "Model hyperparameter tuning completed and saved as 'bird_species_model_fine_tuned_v1.h5'."
250
+
251
+ def learning_rate_reduction():
252
+ global history
253
+ reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.001)
254
+ history = model.fit(X_train_reshaped, y_train_categorical, epochs=50, batch_size=32, validation_data=(X_test_reshaped, y_test_categorical), callbacks=[reduce_lr])
255
+ model.save('bird_species_model_fine_tuned_v2.h5')
256
+ return "Model learning rate reduction completed and saved as 'bird_species_model_fine_tuned_v2.h5'."
257
+
258
+ def evaluate_fine_tuned_models():
259
+ model_v1 = load_model('bird_species_model_fine_tuned_v1.h5')
260
+ model_v2 = load_model('bird_species_model_fine_tuned_v2.h5')
261
+
262
+ test_loss_v1, test_accuracy_v1 = model_v1.evaluate(X_test_reshaped, y_test_categorical)
263
+ y_pred_v1 = model_v1.predict(X_test_reshaped)
264
+ y_pred_classes_v1 = np.argmax(y_pred_v1, axis=1)
265
+ y_true_classes_v1 = np.argmax(y_test_categorical, axis=1)
266
+ report_v1 = classification_report(y_true_classes_v1, y_pred_classes_v1, target_names=label_encoder.classes_)
267
+
268
+ test_loss_v2, test_accuracy_v2 = model_v2.evaluate(X_test_reshaped, y_test_categorical)
269
+ y_pred_v2 = model_v2.predict(X_test_reshaped)
270
+ y_pred_classes_v2 = np.argmax(y_pred_v2, axis=1)
271
+ y_true_classes_v2 = np.argmax(y_test_categorical, axis=1)
272
+ report_v2 = classification_report(y_true_classes_v2, y_pred_classes_v2, target_names=label_encoder.classes_)
273
+
274
+ return f"Fine-Tuned Model V1:\nTest Loss: {test_loss_v1}\nTest Accuracy: {test_accuracy_v1}\n\nClassification Report:\n{report_v1}\n\n" \
275
+ f"Fine-Tuned Model V2:\nTest Loss: {test_loss_v2}\nTest Accuracy: {test_accuracy_v2}\n\nClassification Report:\n{report_v2}"
276
+
277
+ # Gradio UI
278
+ with gr.Blocks() as demo:
279
+ with gr.Row():
280
+ dataset_path_input = gr.Textbox(label="Dataset Path")
281
+ data_exploration_output = gr.Textbox(label="Data Exploration Output")
282
+ with gr.Row():
283
+ audio_processing_button = gr.Button("Perform Audio Processing")
284
+ with gr.Row():
285
+ pitch_shifting_checkbox = gr.Checkbox(label="Enable Pitch Shifting")
286
+ time_stretching_checkbox = gr.Checkbox(label="Enable Time Stretching")
287
+ background_noise_checkbox = gr.Checkbox(label="Enable Background Noise")
288
+ data_augmentation_button = gr.Button("Perform Data Augmentation")
289
+ with gr.Row():
290
+ feature_extraction_radio = gr.Radio(["MFCC", "Spectrogram", "Chroma"], label="Feature Extraction")
291
+ feature_extraction_button = gr.Button("Perform Feature Extraction")
292
+ with gr.Row():
293
+ model_type_radio = gr.Radio(["ResNet50", "CNN"], label="Model Type")
294
+ build_model_button = gr.Button("Build Model")
295
+ with gr.Row():
296
+ train_model_button = gr.Button("Train and Save Model")
297
+ evaluate_model_button = gr.Button("Evaluate Model")
298
+ with gr.Row():
299
+ hyperparameter_tuning_button = gr.Button("Hyperparameter Tuning")
300
+ learning_rate_reduction_button = gr.Button("Learning Rate Reduction")
301
+ with gr.Row():
302
+ evaluate_fine_tuned_models_button = gr.Button("Evaluate Fine-Tuned Models")
303
+
304
+ dataset_path_input.change(data_exploration, inputs=dataset_path_input, outputs=data_exploration_output)
305
+ audio_processing_button.click(audio_processing, outputs=None)
306
+ data_augmentation_button.click(data_augmentation, inputs=[pitch_shifting_checkbox, time_stretching_checkbox, background_noise_checkbox], outputs=None)
307
+ feature_extraction_button.click(feature_extraction, inputs=feature_extraction_radio, outputs=None)
308
+ build_model_button.click(build_model, inputs=[model_type_radio, feature_extraction_radio], outputs=None)
309
+ train_model_button.click(train_model, outputs=None)
310
+ evaluate_model_button.click(evaluate_model, outputs=None)
311
+ hyperparameter_tuning_button.click(hyperparameter_tuning, outputs=None)
312
+ learning_rate_reduction_button.click(learning_rate_reduction, outputs=None)
313
+ evaluate_fine_tuned_models_button.click(evaluate_fine_tuned_models, outputs=None)
314
+
315
+ demo.launch()
packages.txt ADDED
@@ -0,0 +1 @@
 
 
1
+ ffmpeg
requirements.txt ADDED
@@ -0,0 +1,548 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ absl-py==1.4.0
2
+ accelerate==1.2.1
3
+ aiohappyeyeballs==2.4.4
4
+ aiohttp==3.11.11
5
+ aiosignal==1.3.2
6
+ alabaster==1.0.0
7
+ albucore==0.0.19
8
+ albumentations==1.4.20
9
+ altair==5.5.0
10
+ annotated-types==0.7.0
11
+ anyio==3.7.1
12
+ argon2-cffi==23.1.0
13
+ argon2-cffi-bindings==21.2.0
14
+ array_record==0.6.0
15
+ arviz==0.20.0
16
+ astropy==6.1.7
17
+ astropy-iers-data==0.2025.1.20.0.32.27
18
+ astunparse==1.6.3
19
+ atpublic==4.1.0
20
+ attrs==24.3.0
21
+ audioread==3.0.1
22
+ autograd==1.7.0
23
+ babel==2.16.0
24
+ backcall==0.2.0
25
+ beautifulsoup4==4.12.3
26
+ bigframes==1.33.0
27
+ bigquery-magics==0.5.0
28
+ bleach==6.2.0
29
+ blinker==1.9.0
30
+ blis==0.7.11
31
+ blosc2==3.0.0
32
+ bokeh==3.6.2
33
+ Bottleneck==1.4.2
34
+ bqplot==0.12.44
35
+ branca==0.8.1
36
+ CacheControl==0.14.2
37
+ cachetools==5.5.1
38
+ catalogue==2.0.10
39
+ certifi==2024.12.14
40
+ cffi==1.17.1
41
+ chardet==5.2.0
42
+ charset-normalizer==3.4.1
43
+ chex==0.1.88
44
+ clarabel==0.9.0
45
+ click==8.1.8
46
+ cloudpathlib==0.20.0
47
+ cloudpickle==3.1.1
48
+ cmake==3.31.4
49
+ cmdstanpy==1.2.5
50
+ colorcet==3.1.0
51
+ colorlover==0.3.0
52
+ colour==0.1.5
53
+ community==1.0.0b1
54
+ confection==0.1.5
55
+ cons==0.4.6
56
+ contourpy==1.3.1
57
+ cryptography==43.0.3
58
+ cuda-python==12.2.1
59
+ cudf-cu12 @ https://pypi.nvidia.com/cudf-cu12/cudf_cu12-24.10.1-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl
60
+ cufflinks==0.17.3
61
+ cupy-cuda12x==12.2.0
62
+ cvxopt==1.3.2
63
+ cvxpy==1.6.0
64
+ cycler==0.12.1
65
+ cyipopt==1.5.0
66
+ cymem==2.0.11
67
+ Cython==3.0.11
68
+ dask==2024.10.0
69
+ datascience==0.17.6
70
+ db-dtypes==1.4.0
71
+ dbus-python==1.2.18
72
+ debugpy==1.8.0
73
+ decorator==4.4.2
74
+ defusedxml==0.7.1
75
+ Deprecated==1.2.17
76
+ diffusers==0.32.2
77
+ distro==1.9.0
78
+ dlib==19.24.2
79
+ dm-tree==0.1.8
80
+ docker-pycreds==0.4.0
81
+ docstring_parser==0.16
82
+ docutils==0.21.2
83
+ dopamine_rl==4.1.0
84
+ duckdb==1.1.3
85
+ earthengine-api==1.4.6
86
+ easydict==1.13
87
+ editdistance==0.8.1
88
+ eerepr==0.1.0
89
+ einops==0.8.0
90
+ en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl#sha256=86cc141f63942d4b2c5fcee06630fd6f904788d2f0ab005cce45aadb8fb73889
91
+ entrypoints==0.4
92
+ et_xmlfile==2.0.0
93
+ etils==1.11.0
94
+ etuples==0.3.9
95
+ eval_type_backport==0.2.2
96
+ fastai==2.7.18
97
+ fastcore==1.7.28
98
+ fastdownload==0.0.7
99
+ fastjsonschema==2.21.1
100
+ fastprogress==1.0.3
101
+ fastrlock==0.8.3
102
+ filelock==3.17.0
103
+ firebase-admin==6.6.0
104
+ Flask==3.1.0
105
+ flatbuffers==25.1.21
106
+ flax==0.10.2
107
+ folium==0.19.4
108
+ fonttools==4.55.6
109
+ frozendict==2.4.6
110
+ frozenlist==1.5.0
111
+ fsspec==2024.10.0
112
+ future==1.0.0
113
+ gast==0.6.0
114
+ gcsfs==2024.10.0
115
+ GDAL==3.6.4
116
+ gdown==5.2.0
117
+ geemap==0.35.1
118
+ gensim==4.3.3
119
+ geocoder==1.38.1
120
+ geographiclib==2.0
121
+ geopandas==1.0.1
122
+ geopy==2.4.1
123
+ gin-config==0.5.0
124
+ gitdb==4.0.12
125
+ GitPython==3.1.44
126
+ glob2==0.7
127
+ google==2.0.3
128
+ google-ai-generativelanguage==0.6.15
129
+ google-api-core==2.19.2
130
+ google-api-python-client==2.155.0
131
+ google-auth==2.27.0
132
+ google-auth-httplib2==0.2.0
133
+ google-auth-oauthlib==1.2.1
134
+ google-cloud-aiplatform==1.74.0
135
+ google-cloud-bigquery==3.25.0
136
+ google-cloud-bigquery-connection==1.17.0
137
+ google-cloud-bigquery-storage==2.27.0
138
+ google-cloud-bigtable==2.28.1
139
+ google-cloud-core==2.4.1
140
+ google-cloud-datastore==2.20.2
141
+ google-cloud-firestore==2.19.0
142
+ google-cloud-functions==1.19.0
143
+ google-cloud-iam==2.17.0
144
+ google-cloud-language==2.16.0
145
+ google-cloud-pubsub==2.25.0
146
+ google-cloud-resource-manager==1.14.0
147
+ google-cloud-spanner==3.51.0
148
+ google-cloud-storage==2.19.0
149
+ google-cloud-translate==3.19.0
150
+ google-colab @ file:///colabtools/dist/google_colab-1.0.0.tar.gz
151
+ google-crc32c==1.6.0
152
+ google-genai==0.3.0
153
+ google-generativeai==0.8.4
154
+ google-pasta==0.2.0
155
+ google-resumable-media==2.7.2
156
+ googleapis-common-protos==1.66.0
157
+ googledrivedownloader==0.4
158
+ graphviz==0.20.3
159
+ greenlet==3.1.1
160
+ grpc-google-iam-v1==0.14.0
161
+ grpc-interceptor==0.15.4
162
+ grpcio==1.70.0
163
+ grpcio-status==1.62.3
164
+ gspread==6.1.4
165
+ gspread-dataframe==4.0.0
166
+ gym==0.25.2
167
+ gym-notices==0.0.8
168
+ h11==0.14.0
169
+ h5netcdf==1.4.1
170
+ h5py==3.12.1
171
+ highspy==1.9.0
172
+ holidays==0.65
173
+ holoviews==1.20.0
174
+ html5lib==1.1
175
+ httpcore==1.0.7
176
+ httpimport==1.4.0
177
+ httplib2==0.22.0
178
+ httpx==0.28.1
179
+ huggingface-hub==0.27.1
180
+ humanize==4.11.0
181
+ hyperopt==0.2.7
182
+ ibis-framework==9.2.0
183
+ idna==3.10
184
+ imageio==2.36.1
185
+ imageio-ffmpeg==0.6.0
186
+ imagesize==1.4.1
187
+ imbalanced-learn==0.13.0
188
+ imgaug==0.4.0
189
+ immutabledict==4.2.1
190
+ importlib_metadata==8.6.1
191
+ importlib_resources==6.5.2
192
+ imutils==0.5.4
193
+ inflect==7.5.0
194
+ iniconfig==2.0.0
195
+ intel-cmplr-lib-ur==2025.0.4
196
+ intel-openmp==2025.0.4
197
+ ipyevents==2.0.2
198
+ ipyfilechooser==0.6.0
199
+ ipykernel==5.5.6
200
+ ipyleaflet==0.19.2
201
+ ipyparallel==8.8.0
202
+ ipython==7.34.0
203
+ ipython-genutils==0.2.0
204
+ ipython-sql==0.5.0
205
+ ipytree==0.2.2
206
+ ipywidgets==7.7.1
207
+ itsdangerous==2.2.0
208
+ jax==0.4.33
209
+ jax-cuda12-pjrt==0.4.33
210
+ jax-cuda12-plugin==0.4.33
211
+ jaxlib==0.4.33
212
+ jeepney==0.7.1
213
+ jellyfish==1.1.0
214
+ jieba==0.42.1
215
+ Jinja2==3.1.5
216
+ jiter==0.8.2
217
+ joblib==1.4.2
218
+ jsonpatch==1.33
219
+ jsonpickle==4.0.1
220
+ jsonpointer==3.0.0
221
+ jsonschema==4.23.0
222
+ jsonschema-specifications==2024.10.1
223
+ jupyter-client==6.1.12
224
+ jupyter-console==6.1.0
225
+ jupyter-leaflet==0.19.2
226
+ jupyter-server==1.24.0
227
+ jupyter_core==5.7.2
228
+ jupyterlab_pygments==0.3.0
229
+ jupyterlab_widgets==3.0.13
230
+ kaggle==1.6.17
231
+ kagglehub==0.3.6
232
+ keras==3.5.0
233
+ keyring==23.5.0
234
+ kiwisolver==1.4.8
235
+ langchain==0.3.15
236
+ langchain-core==0.3.31
237
+ langchain-text-splitters==0.3.5
238
+ langcodes==3.5.0
239
+ langsmith==0.3.1
240
+ language_data==1.3.0
241
+ launchpadlib==1.10.16
242
+ lazr.restfulclient==0.14.4
243
+ lazr.uri==1.0.6
244
+ lazy_loader==0.4
245
+ libclang==18.1.1
246
+ libcudf-cu12 @ https://pypi.nvidia.com/libcudf-cu12/libcudf_cu12-24.10.1-py3-none-manylinux_2_28_x86_64.whl
247
+ librosa==0.10.2.post1
248
+ lightgbm==4.5.0
249
+ linkify-it-py==2.0.3
250
+ llvmlite==0.43.0
251
+ locket==1.0.0
252
+ logical-unification==0.4.6
253
+ lxml==5.3.0
254
+ marisa-trie==1.2.1
255
+ Markdown==3.7
256
+ markdown-it-py==3.0.0
257
+ MarkupSafe==3.0.2
258
+ matplotlib==3.10.0
259
+ matplotlib-inline==0.1.7
260
+ matplotlib-venn==1.1.1
261
+ mdit-py-plugins==0.4.2
262
+ mdurl==0.1.2
263
+ miniKanren==1.0.3
264
+ missingno==0.5.2
265
+ mistune==3.1.0
266
+ mizani==0.13.1
267
+ mkl==2025.0.1
268
+ ml-dtypes==0.4.1
269
+ mlxtend==0.23.3
270
+ more-itertools==10.5.0
271
+ moviepy==1.0.3
272
+ mpmath==1.3.0
273
+ msgpack==1.1.0
274
+ multidict==6.1.0
275
+ multipledispatch==1.0.0
276
+ multitasking==0.0.11
277
+ murmurhash==1.0.12
278
+ music21==9.3.0
279
+ namex==0.0.8
280
+ narwhals==1.23.0
281
+ natsort==8.4.0
282
+ nbclassic==1.2.0
283
+ nbclient==0.10.2
284
+ nbconvert==7.16.5
285
+ nbformat==5.10.4
286
+ ndindex==1.9.2
287
+ nest-asyncio==1.6.0
288
+ networkx==3.4.2
289
+ nibabel==5.3.2
290
+ nltk==3.9.1
291
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292
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293
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294
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295
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296
+ nvidia-cublas-cu12==12.1.3.1
297
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298
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299
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300
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301
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302
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303
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304
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305
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308
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309
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310
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311
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312
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313
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314
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322
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323
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324
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331
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344
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345
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359
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sample_data/README.md ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ This directory includes a few sample datasets to get you started.
2
+
3
+ * `california_housing_data*.csv` is California housing data from the 1990 US
4
+ Census; more information is available at:
5
+ https://docs.google.com/document/d/e/2PACX-1vRhYtsvc5eOR2FWNCwaBiKL6suIOrxJig8LcSBbmCbyYsayia_DvPOOBlXZ4CAlQ5nlDD8kTaIDRwrN/pub
6
+
7
+ * `mnist_*.csv` is a small sample of the
8
+ [MNIST database](https://en.wikipedia.org/wiki/MNIST_database), which is
9
+ described at: http://yann.lecun.com/exdb/mnist/
10
+
11
+ * `anscombe.json` contains a copy of
12
+ [Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it
13
+ was originally described in
14
+
15
+ Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American
16
+ Statistician. 27 (1): 17-21. JSTOR 2682899.
17
+
18
+ and our copy was prepared by the
19
+ [vega_datasets library](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json).
sample_data/anscombe.json ADDED
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+ ]
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