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+ 2025-02-18 14:19:08,884 INFO ___FILE_ONLY___ ╝
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+ 2025-02-18 14:19:08,886 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
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+ 2025-02-18 14:19:08,886 INFO ___FILE_ONLY___ ╠═ Downloading: Cloud Storage Command Line Tool ═╣
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+ 2025-02-18 14:19:08,887 INFO ___FILE_ONLY___ ╚
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+ 2025-02-18 14:19:08,890 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
307
+ 2025-02-18 14:19:10,007 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gsutil-20241213184646.tar.gz HTTP/11" 200 12368759
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+ 2025-02-18 14:19:10,050 INFO ___FILE_ONLY___ ═
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+ 2025-02-18 14:19:10,067 INFO ___FILE_ONLY___ ═
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+ 2025-02-18 14:19:10,067 INFO ___FILE_ONLY___ ═
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+ 2025-02-18 14:19:10,068 INFO ___FILE_ONLY___ ═
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+ 2025-02-18 14:19:10,069 INFO ___FILE_ONLY___ ═
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+ 2025-02-18 14:19:10,069 INFO ___FILE_ONLY___ ═
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+ 2025-02-18 14:19:10,070 INFO ___FILE_ONLY___ ═
368
+ 2025-02-18 14:19:10,070 INFO ___FILE_ONLY___ ╝
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+
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+ 2025-02-18 14:19:10,072 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
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+
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+ 2025-02-18 14:19:10,072 INFO ___FILE_ONLY___ ╠═ Downloading: Cloud Storage Command Line Tool (Platfor... ═╣
373
+
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+ 2025-02-18 14:19:10,072 INFO ___FILE_ONLY___ ╚
375
+ 2025-02-18 14:19:10,075 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
376
+ 2025-02-18 14:19:11,190 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gsutil-nix-20240830134514.tar.gz HTTP/11" 200 1928
377
+ 2025-02-18 14:19:11,190 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
378
+ 2025-02-18 14:19:11,191 INFO ___FILE_ONLY___ ╝
379
+
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+ 2025-02-18 14:19:11,193 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
381
+
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+ 2025-02-18 14:19:11,193 INFO ___FILE_ONLY___ ╠═ Downloading: Default set of gcloud commands ═╣
383
+
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+ 2025-02-18 14:19:11,193 INFO ___FILE_ONLY___ ╚
385
+ 2025-02-18 14:19:11,193 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
386
+ 2025-02-18 14:19:11,193 INFO ___FILE_ONLY___ ╝
387
+
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+ 2025-02-18 14:19:11,195 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
389
+
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+ 2025-02-18 14:19:11,195 INFO ___FILE_ONLY___ ╠═ Downloading: Google Cloud CLI Core Libraries (Platfor... ═╣
391
+
392
+ 2025-02-18 14:19:11,195 INFO ___FILE_ONLY___ ╚
393
+ 2025-02-18 14:19:11,198 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
394
+ 2025-02-18 14:19:12,267 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-core-nix-20240830134514.tar.gz HTTP/11" 200 2306
395
+ 2025-02-18 14:19:12,268 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
396
+ 2025-02-18 14:19:12,268 INFO ___FILE_ONLY___ ╝
397
+
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+ 2025-02-18 14:19:12,270 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
399
+
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+ 2025-02-18 14:19:12,270 INFO ___FILE_ONLY___ ╠═ Downloading: Google Cloud CRC32C Hash Tool ═╣
401
+
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+ 2025-02-18 14:19:12,270 INFO ___FILE_ONLY___ ╚
403
+ 2025-02-18 14:19:12,270 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
404
+ 2025-02-18 14:19:12,270 INFO ___FILE_ONLY___ ╝
405
+
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+ 2025-02-18 14:19:12,272 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
407
+
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+ 2025-02-18 14:19:12,272 INFO ___FILE_ONLY___ ╠═ Downloading: Google Cloud CRC32C Hash Tool (Platform ... ═╣
409
+
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+ 2025-02-18 14:19:12,272 INFO ___FILE_ONLY___ ╚
411
+ 2025-02-18 14:19:12,275 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
412
+ 2025-02-18 14:19:13,296 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/11" 200 1478989
413
+ 2025-02-18 14:19:13,307 INFO ___FILE_ONLY___ ═
414
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415
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416
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417
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418
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419
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420
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421
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429
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430
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431
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446
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+ 2025-02-18 14:19:13,311 INFO ___FILE_ONLY___ ═
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+ 2025-02-18 14:19:13,311 INFO ___FILE_ONLY___ ═
451
+ 2025-02-18 14:19:13,311 INFO ___FILE_ONLY___ ═
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+ 2025-02-18 14:19:13,311 INFO ___FILE_ONLY___ ═
453
+ 2025-02-18 14:19:13,311 INFO ___FILE_ONLY___ ═
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+ 2025-02-18 14:19:13,311 INFO ___FILE_ONLY___ ═
455
+ 2025-02-18 14:19:13,312 INFO ___FILE_ONLY___ ═
456
+ 2025-02-18 14:19:13,312 INFO ___FILE_ONLY___ ═
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+ 2025-02-18 14:19:13,312 INFO ___FILE_ONLY___ ═
458
+ 2025-02-18 14:19:13,312 INFO ___FILE_ONLY___ ═
459
+ 2025-02-18 14:19:13,312 INFO ___FILE_ONLY___ ═
460
+ 2025-02-18 14:19:13,312 INFO ___FILE_ONLY___ ═
461
+ 2025-02-18 14:19:13,312 INFO ___FILE_ONLY___ ═
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+ 2025-02-18 14:19:13,312 INFO ___FILE_ONLY___ ═
463
+ 2025-02-18 14:19:13,312 INFO ___FILE_ONLY___ ═
464
+ 2025-02-18 14:19:13,313 INFO ___FILE_ONLY___ ═
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+ 2025-02-18 14:19:13,313 INFO ___FILE_ONLY___ ═
466
+ 2025-02-18 14:19:13,313 INFO ___FILE_ONLY___ ═
467
+ 2025-02-18 14:19:13,313 INFO ___FILE_ONLY___ ═
468
+ 2025-02-18 14:19:13,313 INFO ___FILE_ONLY___ ═
469
+ 2025-02-18 14:19:13,313 INFO ___FILE_ONLY___ ═
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+ 2025-02-18 14:19:13,313 INFO ___FILE_ONLY___ ═
471
+ 2025-02-18 14:19:13,313 INFO ___FILE_ONLY___ ═
472
+ 2025-02-18 14:19:13,313 INFO ___FILE_ONLY___ ═
473
+ 2025-02-18 14:19:13,314 INFO ___FILE_ONLY___ ╝
474
+
475
+ 2025-02-18 14:19:13,316 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
476
+
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+ 2025-02-18 14:19:13,316 INFO ___FILE_ONLY___ ╠═ Downloading: gcloud cli dependencies (Platform Specific) ═╣
478
+
479
+ 2025-02-18 14:19:13,316 INFO ___FILE_ONLY___ ╚
480
+ 2025-02-18 14:19:13,320 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
481
+ 2025-02-18 14:19:14,360 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/11" 200 104
482
+ 2025-02-18 14:19:14,361 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
483
+ 2025-02-18 14:19:14,361 INFO ___FILE_ONLY___ ╝
484
+
485
+ 2025-02-18 14:19:14,363 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
486
+
487
+ 2025-02-18 14:19:14,363 INFO ___FILE_ONLY___ ╠═ Installing: BigQuery Command Line Tool ═╣
488
+
489
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490
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491
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492
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493
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494
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495
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496
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497
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498
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499
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500
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501
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503
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504
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507
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508
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509
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510
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511
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512
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513
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527
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529
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530
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531
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532
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534
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535
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536
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537
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538
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539
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540
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541
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542
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543
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544
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545
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546
+ 2025-02-18 14:19:14,624 INFO ___FILE_ONLY___ ═
547
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548
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549
+ 2025-02-18 14:19:14,631 INFO ___FILE_ONLY___ ═
550
+ 2025-02-18 14:19:14,631 INFO ___FILE_ONLY___ ╝
551
+
552
+ 2025-02-18 14:19:14,639 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
553
+
554
+ 2025-02-18 14:19:14,639 INFO ___FILE_ONLY___ ╠═ Installing: BigQuery Command Line Tool (Platform Spec... ═╣
555
+
556
+ 2025-02-18 14:19:14,639 INFO ___FILE_ONLY___ ╚
557
+ 2025-02-18 14:19:14,639 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
558
+ 2025-02-18 14:19:14,640 INFO ___FILE_ONLY___ ╝
559
+
560
+ 2025-02-18 14:19:14,644 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
561
+
562
+ 2025-02-18 14:19:14,644 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.12 ═╣
563
+
564
+ 2025-02-18 14:19:14,644 INFO ___FILE_ONLY___ ╚
565
+ 2025-02-18 14:19:14,647 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
566
+ 2025-02-18 14:19:14,647 INFO ___FILE_ONLY___ ╝
567
+
568
+ 2025-02-18 14:19:14,648 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
569
+
570
+ 2025-02-18 14:19:14,648 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.12 (Platform Specific) ═╣
571
+
572
+ 2025-02-18 14:19:14,649 INFO ___FILE_ONLY___ ╚
573
+ 2025-02-18 14:19:17,787 INFO ___FILE_ONLY___ ═
574
+ 2025-02-18 14:19:18,270 INFO ___FILE_ONLY___ ═
575
+ 2025-02-18 14:19:18,290 INFO ___FILE_ONLY___ ═
576
+ 2025-02-18 14:19:18,324 INFO ___FILE_ONLY___ ═
577
+ 2025-02-18 14:19:18,345 INFO ___FILE_ONLY___ ═
578
+ 2025-02-18 14:19:18,360 INFO ___FILE_ONLY___ ═
579
+ 2025-02-18 14:19:18,389 INFO ___FILE_ONLY___ ═
580
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581
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582
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583
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584
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585
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586
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587
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588
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589
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590
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591
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592
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593
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594
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595
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596
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597
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598
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599
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600
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601
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602
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603
+ 2025-02-18 14:19:19,443 INFO ___FILE_ONLY___ ═
604
+ 2025-02-18 14:19:19,454 INFO ___FILE_ONLY___ ═
605
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606
+ 2025-02-18 14:19:19,478 INFO ___FILE_ONLY___ ═
607
+ 2025-02-18 14:19:19,490 INFO ___FILE_ONLY___ ═
608
+ 2025-02-18 14:19:19,502 INFO ___FILE_ONLY___ ═
609
+ 2025-02-18 14:19:19,515 INFO ___FILE_ONLY___ ═
610
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611
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612
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613
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614
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615
+ 2025-02-18 14:19:19,586 INFO ___FILE_ONLY___ ═
616
+ 2025-02-18 14:19:19,598 INFO ___FILE_ONLY___ ═
617
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618
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619
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620
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621
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622
+ 2025-02-18 14:19:19,673 INFO ___FILE_ONLY___ ═
623
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624
+ 2025-02-18 14:19:19,696 INFO ___FILE_ONLY___ ═
625
+ 2025-02-18 14:19:19,707 INFO ___FILE_ONLY___ ═
626
+ 2025-02-18 14:19:19,720 INFO ___FILE_ONLY___ ═
627
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628
+ 2025-02-18 14:19:19,744 INFO ___FILE_ONLY___ ═
629
+ 2025-02-18 14:19:19,756 INFO ___FILE_ONLY___ ═
630
+ 2025-02-18 14:19:19,768 INFO ___FILE_ONLY___ ═
631
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632
+ 2025-02-18 14:19:19,792 INFO ___FILE_ONLY___ ═
633
+ 2025-02-18 14:19:19,792 INFO ___FILE_ONLY___ ╝
634
+
635
+ 2025-02-18 14:19:19,844 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
636
+
637
+ 2025-02-18 14:19:19,844 INFO ___FILE_ONLY___ ╠═ Installing: Cloud Storage Command Line Tool ═╣
638
+
639
+ 2025-02-18 14:19:19,844 INFO ___FILE_ONLY___ ╚
640
+ 2025-02-18 14:19:20,377 INFO ___FILE_ONLY___ ═
641
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642
+ 2025-02-18 14:19:20,413 INFO ___FILE_ONLY___ ═
643
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644
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645
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647
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648
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649
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650
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651
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652
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653
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654
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655
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656
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657
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658
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659
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660
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661
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662
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663
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664
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665
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666
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667
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668
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669
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670
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671
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672
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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
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681
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682
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683
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684
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685
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686
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687
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688
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689
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690
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691
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692
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693
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694
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695
+ 2025-02-18 14:19:21,080 INFO ___FILE_ONLY___ ═
696
+ 2025-02-18 14:19:21,090 INFO ___FILE_ONLY___ ═
697
+ 2025-02-18 14:19:21,100 INFO ___FILE_ONLY___ ═
698
+ 2025-02-18 14:19:21,111 INFO ___FILE_ONLY___ ═
699
+ 2025-02-18 14:19:21,125 INFO ___FILE_ONLY___ ═
700
+ 2025-02-18 14:19:21,125 INFO ___FILE_ONLY___ ╝
701
+
702
+ 2025-02-18 14:19:21,156 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
703
+
704
+ 2025-02-18 14:19:21,156 INFO ___FILE_ONLY___ ╠═ Installing: Cloud Storage Command Line Tool (Platform... ═╣
705
+
706
+ 2025-02-18 14:19:21,156 INFO ___FILE_ONLY___ ╚
707
+ 2025-02-18 14:19:21,157 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
708
+ 2025-02-18 14:19:21,157 INFO ___FILE_ONLY___ ╝
709
+
710
+ 2025-02-18 14:19:21,161 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
711
+
712
+ 2025-02-18 14:19:21,161 INFO ___FILE_ONLY___ ╠═ Installing: Default set of gcloud commands ═╣
713
+
714
+ 2025-02-18 14:19:21,161 INFO ___FILE_ONLY___ ╚
715
+ 2025-02-18 14:19:21,164 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
716
+ 2025-02-18 14:19:21,164 INFO ___FILE_ONLY___ ╝
717
+
718
+ 2025-02-18 14:19:21,166 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
719
+
720
+ 2025-02-18 14:19:21,166 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CLI Core Libraries (Platform... ═╣
721
+
722
+ 2025-02-18 14:19:21,166 INFO ___FILE_ONLY___ ╚
723
+ 2025-02-18 14:19:21,167 INFO ___FILE_ONLY___ ══════════════════════════════
724
+ 2025-02-18 14:19:21,167 INFO ___FILE_ONLY___ ══════════════════════════════
725
+ 2025-02-18 14:19:21,167 INFO ___FILE_ONLY___ ╝
726
+
727
+ 2025-02-18 14:19:21,172 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
728
+
729
+ 2025-02-18 14:19:21,172 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool ═╣
730
+
731
+ 2025-02-18 14:19:21,172 INFO ___FILE_ONLY___ ╚
732
+ 2025-02-18 14:19:21,174 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
733
+ 2025-02-18 14:19:21,174 INFO ___FILE_ONLY___ ╝
734
+
735
+ 2025-02-18 14:19:21,176 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
736
+
737
+ 2025-02-18 14:19:21,176 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool (Platform S... ═╣
738
+
739
+ 2025-02-18 14:19:21,176 INFO ___FILE_ONLY___ ╚
740
+ 2025-02-18 14:19:21,214 INFO ___FILE_ONLY___ ══════════════════════════════
741
+ 2025-02-18 14:19:21,215 INFO ___FILE_ONLY___ ══════════════════════════════
742
+ 2025-02-18 14:19:21,215 INFO ___FILE_ONLY___ ╝
743
+
744
+ 2025-02-18 14:19:21,220 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
745
+
746
+ 2025-02-18 14:19:21,220 INFO ___FILE_ONLY___ ╠═ Installing: gcloud cli dependencies (Platform Specific) ═╣
747
+
748
+ 2025-02-18 14:19:21,220 INFO ___FILE_ONLY___ ╚
749
+ 2025-02-18 14:19:21,220 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
750
+ 2025-02-18 14:19:21,220 INFO ___FILE_ONLY___ ╝
751
+
752
+ 2025-02-18 14:19:21,225 DEBUG root Updating notification cache...
753
+ 2025-02-18 14:19:21,226 INFO ___FILE_ONLY___
754
+
755
+ 2025-02-18 14:19:21,228 INFO ___FILE_ONLY___ Performing post processing steps...
756
+ 2025-02-18 14:19:21,228 DEBUG root Executing command: ['/tools/google-cloud-sdk/bin/gcloud', 'components', 'post-process']
757
+ 2025-02-18 14:19:30,331 DEBUG ___FILE_ONLY___
758
+ 2025-02-18 14:19:30,331 DEBUG ___FILE_ONLY___
759
+ 2025-02-18 14:19:30,502 INFO ___FILE_ONLY___
760
+ Update done!
761
+
762
+
763
+ 2025-02-18 14:19:30,505 DEBUG root Chosen display Format:none
764
+ 2025-02-18 14:19:30,505 INFO root Display format: "none"
.config/logs/2025.02.18/14.19.21.783246.log ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ 2025-02-18 14:19:21,784 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2025-02-18 14:19:21,786 DEBUG root Loaded Command Group: ['gcloud', 'components', 'post_process']
3
+ 2025-02-18 14:19:21,787 DEBUG root Running [gcloud.components.post-process] with arguments: []
4
+ 2025-02-18 14:19:30,183 DEBUG root Chosen display Format:none
5
+ 2025-02-18 14:19:30,184 INFO root Display format: "none"
.config/logs/2025.02.18/14.19.31.209860.log ADDED
@@ -0,0 +1,152 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2025-02-18 14:19:31,210 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2025-02-18 14:19:31,212 DEBUG root Loaded Command Group: ['gcloud', 'components', 'update']
3
+ 2025-02-18 14:19:31,214 DEBUG root Running [gcloud.components.update] with arguments: [--quiet: "True", COMPONENT-IDS:8: "['gcloud', 'core', 'bq', 'gsutil', 'compute', 'preview', 'alpha', 'beta']"]
4
+ 2025-02-18 14:19:31,215 INFO ___FILE_ONLY___ Beginning update. This process may take several minutes.
5
+
6
+ 2025-02-18 14:19:31,224 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
7
+ 2025-02-18 14:19:32,265 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components-2.json HTTP/11" 200 227832
8
+ 2025-02-18 14:19:32,278 WARNING root Component [compute] no longer exists.
9
+ 2025-02-18 14:19:32,279 INFO ___FILE_ONLY___
10
+
11
+ 2025-02-18 14:19:32,280 INFO ___FILE_ONLY___
12
+ Your current Google Cloud CLI version is: 510.0.0
13
+
14
+ 2025-02-18 14:19:32,280 INFO ___FILE_ONLY___ Installing components from version: 510.0.0
15
+
16
+ 2025-02-18 14:19:32,280 INFO ___FILE_ONLY___
17
+
18
+ 2025-02-18 14:19:32,280 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
+ 2025-02-18 14:19:32,281 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
+ 2025-02-18 14:19:32,281 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
+ 2025-02-18 14:19:32,296 INFO ___FILE_ONLY___ ┌────────────────────────────────────────────────┐
22
+ 2025-02-18 14:19:32,296 INFO ___FILE_ONLY___
23
+
24
+ 2025-02-18 14:19:32,296 INFO ___FILE_ONLY___ │ These components will be installed. │
25
+ 2025-02-18 14:19:32,296 INFO ___FILE_ONLY___
26
+
27
+ 2025-02-18 14:19:32,296 INFO ___FILE_ONLY___ ├─────────────────────────┬────────────┬─────────┤
28
+ 2025-02-18 14:19:32,296 INFO ___FILE_ONLY___
29
+
30
+ 2025-02-18 14:19:32,296 INFO ___FILE_ONLY___ │ Name │ Version │ Size │
31
+ 2025-02-18 14:19:32,296 INFO ___FILE_ONLY___
32
+
33
+ 2025-02-18 14:19:32,296 INFO ___FILE_ONLY___ ├─────────────────────────┼────────────┼─────────┤
34
+ 2025-02-18 14:19:32,297 INFO ___FILE_ONLY___
35
+
36
+ 2025-02-18 14:19:32,297 INFO ___FILE_ONLY___ │
37
+ 2025-02-18 14:19:32,297 INFO ___FILE_ONLY___ gcloud Alpha Commands
38
+ 2025-02-18 14:19:32,297 INFO ___FILE_ONLY___
39
+ 2025-02-18 14:19:32,297 INFO ___FILE_ONLY___ │
40
+ 2025-02-18 14:19:32,297 INFO ___FILE_ONLY___ 2025.02.10
41
+ 2025-02-18 14:19:32,297 INFO ___FILE_ONLY___
42
+ 2025-02-18 14:19:32,297 INFO ___FILE_ONLY___ │
43
+ 2025-02-18 14:19:32,297 INFO ___FILE_ONLY___ < 1 MiB
44
+ 2025-02-18 14:19:32,297 INFO ___FILE_ONLY___
45
+ 2025-02-18 14:19:32,297 INFO ___FILE_ONLY___ │
46
+ 2025-02-18 14:19:32,297 INFO ___FILE_ONLY___
47
+
48
+ 2025-02-18 14:19:32,297 INFO ___FILE_ONLY___ │
49
+ 2025-02-18 14:19:32,297 INFO ___FILE_ONLY___ gcloud Beta Commands
50
+ 2025-02-18 14:19:32,298 INFO ___FILE_ONLY___
51
+ 2025-02-18 14:19:32,298 INFO ___FILE_ONLY___ │
52
+ 2025-02-18 14:19:32,298 INFO ___FILE_ONLY___ 2025.02.10
53
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README.md CHANGED
@@ -1,12 +1,6 @@
1
  ---
2
- title: Iti110 Birds Cnn Resnet Gpt 4o.py
3
- emoji: 🐢
4
- colorFrom: purple
5
- colorTo: gray
6
  sdk: gradio
7
  sdk_version: 5.16.2
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- app_file: app.py
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- pinned: false
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  ---
11
-
12
- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
1
  ---
2
+ title: iti110_birds_cnn_resnet_gpt_4o.py
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 +1,321 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ # Install pydub and ffmpeg
15
+ !pip install pydub
16
+ !apt-get install -y ffmpeg
17
+
18
+ # Install gradio
19
+ !pip install gradio
20
+
21
+ import os
22
+ import numpy as np
23
+ import pandas as pd
24
+ import librosa
25
+ import soundfile as sf
26
+ from pydub import AudioSegment
27
+ import gradio as gr
28
+ from sklearn.model_selection import train_test_split
29
+ from sklearn.preprocessing import LabelEncoder
30
+ from tensorflow.keras.utils import to_categorical
31
+ from tensorflow.keras.models import Sequential, load_model, Model
32
+ from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, Input
33
+ from tensorflow.keras.applications import ResNet50
34
+ from tensorflow.keras.callbacks import ReduceLROnPlateau
35
+ import matplotlib.pyplot as plt
36
+ import seaborn as sns
37
+ from sklearn.metrics import classification_report, confusion_matrix
38
+
39
+ # Global variables
40
+ dataset_path = ''
41
+ species_folders = []
42
+ label_encoder = LabelEncoder()
43
+ X_train, X_test, y_train, y_test = None, None, None, None
44
+ history = None
45
+ model = None
46
+
47
+ def data_exploration(path):
48
+ global dataset_path, species_folders
49
+ dataset_path = path
50
+ species_folders = [f for f in os.listdir(dataset_path) if os.path.isdir(os.path.join(dataset_path, f))]
51
+ species_count = Counter()
52
+ for species in species_folders:
53
+ species_files = os.listdir(os.path.join(dataset_path, species))
54
+ species_count[species] = len(species_files)
55
+ species_df = pd.DataFrame(list(species_count.items()), columns=['Species', 'Count'])
56
+ species_df.to_csv('species_distribution.csv', index=False)
57
+ return species_df.to_string(index=False)
58
+
59
+ def audio_processing():
60
+ for species in species_folders:
61
+ species_folder = os.path.join(dataset_path, species)
62
+ for file_name in os.listdir(species_folder):
63
+ if file_name.endswith('.mp3'):
64
+ mp3_path = os.path.join(species_folder, file_name)
65
+ wav_path = os.path.join(species_folder, file_name.replace('.mp3', '.wav'))
66
+ convert_mp3_to_wav(mp3_path, wav_path)
67
+ os.remove(mp3_path)
68
+ for species in species_folders:
69
+ species_folder = os.path.join(dataset_path, species)
70
+ for file_name in os.listdir(species_folder):
71
+ if file_name.endswith('.wav'):
72
+ wav_path = os.path.join(species_folder, file_name)
73
+ preprocess_audio(wav_path, wav_path)
74
+ return "Audio processing completed."
75
+
76
+ def convert_mp3_to_wav(input_path, output_path):
77
+ audio = AudioSegment.from_mp3(input_path)
78
+ audio.export(output_path, format="wav")
79
+
80
+ def preprocess_audio(input_path, output_path, sample_rate=22050):
81
+ y, sr = librosa.load(input_path, sr=sample_rate)
82
+ y_trimmed, _ = librosa.effects.trim(y)
83
+ y_normalized = librosa.util.normalize(y_trimmed)
84
+ sf.write(output_path, y_normalized, sample_rate)
85
+
86
+ def data_augmentation(pitch_shifting, time_stretching, background_noise):
87
+ for species in species_folders:
88
+ species_folder = os.path.join(dataset_path, species)
89
+ if pitch_shifting:
90
+ apply_pitch_shift(species_folder)
91
+ if time_stretching:
92
+ apply_time_stretch(species_folder)
93
+ if background_noise:
94
+ apply_background_noise(species_folder)
95
+ return "Data augmentation completed."
96
+
97
+ def apply_pitch_shift(species_folder):
98
+ for file_name in os.listdir(species_folder):
99
+ if file_name.endswith('.wav'):
100
+ wav_path = os.path.join(species_folder, file_name)
101
+ y, sr = librosa.load(wav_path, sr=None)
102
+ y_shifted = librosa.effects.pitch_shift(y, sr, n_steps=2)
103
+ shifted_path = wav_path.replace('.wav', '_pitch_shifted.wav')
104
+ sf.write(shifted_path, y_shifted, sr)
105
+
106
+ def apply_time_stretch(species_folder):
107
+ for file_name in os.listdir(species_folder):
108
+ if file_name.endswith('.wav'):
109
+ wav_path = os.path.join(species_folder, file_name)
110
+ y, sr = librosa.load(wav_path, sr=None)
111
+ y_stretched = librosa.effects.time_stretch(y, rate=1.2)
112
+ stretched_path = wav_path.replace('.wav', '_time_stretched.wav')
113
+ sf.write(stretched_path, y_stretched, sr)
114
+
115
+ def apply_background_noise(species_folder):
116
+ for file_name in os.listdir(species_folder):
117
+ if file_name.endswith('.wav'):
118
+ wav_path = os.path.join(species_folder, file_name)
119
+ y, sr = librosa.load(wav_path, sr=None)
120
+ noise = np.random.randn(len(y))
121
+ y_noisy = y + 0.005 * noise
122
+ noisy_path = wav_path.replace('.wav', '_noisy.wav')
123
+ sf.write(noisy_path, y_noisy, sr)
124
+
125
+ def feature_extraction(feature_type):
126
+ output_folder = f'path_to_output_{feature_type.lower()}'
127
+ if feature_type == 'MFCC':
128
+ extract_features = extract_mfcc
129
+ elif feature_type == 'Spectrogram':
130
+ extract_features = extract_spectrogram
131
+ elif feature_type == 'Chroma':
132
+ extract_features = extract_chroma_features
133
+
134
+ for species in species_folders:
135
+ species_folder = os.path.join(dataset_path, species)
136
+ species_output_folder = os.path.join(output_folder, species)
137
+ extract_features(species_folder, species_output_folder)
138
+
139
+ return f"{feature_type} features extracted and saved."
140
+
141
+ def extract_mfcc(species_folder, output_folder):
142
+ if not os.path.exists(output_folder):
143
+ os.makedirs(output_folder)
144
+ for file_name in os.listdir(species_folder):
145
+ if file_name.endswith('.wav'):
146
+ wav_path = os.path.join(species_folder, file_name)
147
+ y, sr = librosa.load(wav_path, sr=None)
148
+ mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
149
+ feature_path = os.path.join(output_folder, file_name.replace('.wav', '.npy'))
150
+ np.save(feature_path, mfccs)
151
+
152
+ def extract_spectrogram(species_folder, output_folder):
153
+ if not os.path.exists(output_folder):
154
+ os.makedirs(output_folder)
155
+ for file_name in os.listdir(species_folder):
156
+ if file_name.endswith('.wav'):
157
+ wav_path = os.path.join(species_folder, file_name)
158
+ y, sr = librosa.load(wav_path, sr=None)
159
+ S = librosa.feature.melspectrogram(y=y, sr=sr)
160
+ S_DB = librosa.power_to_db(S, ref=np.max)
161
+ feature_path = os.path.join(output_folder, file_name.replace('.wav', '.npy'))
162
+ np.save(feature_path, S_DB)
163
+
164
+ def extract_chroma_features(species_folder, output_folder):
165
+ if not os.path.exists(output_folder):
166
+ os.makedirs(output_folder)
167
+ for file_name in os.listdir(species_folder):
168
+ if file_name.endswith('.wav'):
169
+ wav_path = os.path.join(species_folder, file_name)
170
+ y, sr = librosa.load(wav_path, sr=None)
171
+ chroma = librosa.feature.chroma_stft(y=y, sr=sr)
172
+ feature_path = os.path.join(output_folder, file_name.replace('.wav', '_chroma.npy'))
173
+ np.save(feature_path, chroma)
174
+
175
+ def load_features_and_labels(feature_folder):
176
+ X = []
177
+ y = []
178
+ species_list = os.listdir(feature_folder)
179
+ for species in species_list:
180
+ species_folder = os.path.join(feature_folder, species)
181
+ for file_name in os.listdir(species_folder):
182
+ if file_name.endswith('.npy'):
183
+ feature_path = os.path.join(species_folder, file_name)
184
+ features = np.load(feature_path)
185
+ X.append(features)
186
+ y.append(species)
187
+ return np.array(X), np.array(y)
188
+
189
+ def build_model(model_type, feature_type):
190
+ global X_train, X_test, y_train, y_test, label_encoder, model
191
+ feature_folder = f'path_to_output_{feature_type.lower()}'
192
+ X, y = load_features_and_labels(feature_folder)
193
+ X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
194
+ label_encoder = LabelEncoder()
195
+ y_train_encoded = label_encoder.fit_transform(y_train)
196
+ y_test_encoded = label_encoder.transform(y_test)
197
+ y_train_categorical = to_categorical(y_train_encoded)
198
+ y_test_categorical = to_categorical(y_test_encoded)
199
+ np.save('classes.npy', label_encoder.classes_)
200
+ X_train_reshaped = np.stack([np.repeat(x[..., np.newaxis], 3, axis=-1) for x in X_train])
201
+ X_test_reshaped = np.stack([np.repeat(x[..., np.newaxis], 3, axis=-1) for x in X_test])
202
+
203
+ if model_type == 'ResNet50':
204
+ input_tensor = Input(shape=(X_train.shape[1], X_train.shape[2], 3))
205
+ base_model = ResNet50(include_top=False, weights='imagenet', input_tensor=input_tensor)
206
+ x = Flatten()(base_model.output)
207
+ x = Dense(128, activation='relu')(x)
208
+ x = Dropout(0.3)(x)
209
+ output_tensor = Dense(len(label_encoder.classes_), activation='softmax')(x)
210
+ model = Model(inputs=input_tensor, outputs=output_tensor)
211
+ else:
212
+ model = Sequential([
213
+ Conv2D(32, (3, 3), activation='relu', input_shape=(X_train.shape[1], X_train.shape[2], 3)),
214
+ MaxPooling2D((2, 2)),
215
+ Dropout(0.3),
216
+ Conv2D(64, (3, 3), activation='relu'),
217
+ MaxPooling2D((2, 2)),
218
+ Dropout(0.3),
219
+ Flatten(),
220
+ Dense(128, activation='relu'),
221
+ Dropout(0.3),
222
+ Dense(len(label_encoder.classes_), activation='softmax')
223
+ ])
224
+
225
+ model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
226
+ model.summary()
227
+ return "Model built."
228
+
229
+ def train_model():
230
+ global history
231
+ history = model.fit(X_train_reshaped, y_train_categorical, epochs=30, batch_size=32, validation_data=(X_test_reshaped, y_test_categorical))
232
+ model.save('bird_species_model.h5')
233
+ return "Model training completed and saved as 'bird_species_model.h5'."
234
+
235
+ def evaluate_model():
236
+ test_loss, test_accuracy = model.evaluate(X_test_reshaped, y_test_categorical)
237
+ y_pred = model.predict(X_test_reshaped)
238
+ y_pred_classes = np.argmax(y_pred, axis=1)
239
+ y_true_classes = np.argmax(y_test_categorical, axis=1)
240
+ report = classification_report(y_true_classes, y_pred_classes, target_names=label_encoder.classes_)
241
+ conf_matrix = confusion_matrix(y_true_classes, y_pred_classes)
242
+ plt.figure(figsize=(12, 8))
243
+ sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=label_encoder.classes_, yticklabels=label_encoder.classes_)
244
+ plt.xlabel('Predicted Label')
245
+ plt.ylabel('True Label')
246
+ plt.title('Confusion Matrix')
247
+ plt.show()
248
+ return f"Test Loss: {test_loss}\nTest Accuracy: {test_accuracy}\n\nClassification Report:\n{report}"
249
+
250
+ def hyperparameter_tuning():
251
+ global history
252
+ reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.001)
253
+ 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])
254
+ model.save('bird_species_model_fine_tuned_v1.h5')
255
+ return "Model hyperparameter tuning completed and saved as 'bird_species_model_fine_tuned_v1.h5'."
256
+
257
+ def learning_rate_reduction():
258
+ global history
259
+ reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_lr=0.001)
260
+ 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])
261
+ model.save('bird_species_model_fine_tuned_v2.h5')
262
+ return "Model learning rate reduction completed and saved as 'bird_species_model_fine_tuned_v2.h5'."
263
+
264
+ def evaluate_fine_tuned_models():
265
+ model_v1 = load_model('bird_species_model_fine_tuned_v1.h5')
266
+ model_v2 = load_model('bird_species_model_fine_tuned_v2.h5')
267
+
268
+ test_loss_v1, test_accuracy_v1 = model_v1.evaluate(X_test_reshaped, y_test_categorical)
269
+ y_pred_v1 = model_v1.predict(X_test_reshaped)
270
+ y_pred_classes_v1 = np.argmax(y_pred_v1, axis=1)
271
+ y_true_classes_v1 = np.argmax(y_test_categorical, axis=1)
272
+ report_v1 = classification_report(y_true_classes_v1, y_pred_classes_v1, target_names=label_encoder.classes_)
273
+
274
+ test_loss_v2, test_accuracy_v2 = model_v2.evaluate(X_test_reshaped, y_test_categorical)
275
+ y_pred_v2 = model_v2.predict(X_test_reshaped)
276
+ y_pred_classes_v2 = np.argmax(y_pred_v2, axis=1)
277
+ y_true_classes_v2 = np.argmax(y_test_categorical, axis=1)
278
+ report_v2 = classification_report(y_true_classes_v2, y_pred_classes_v2, target_names=label_encoder.classes_)
279
+
280
+ return f"Fine-Tuned Model V1:\nTest Loss: {test_loss_v1}\nTest Accuracy: {test_accuracy_v1}\n\nClassification Report:\n{report_v1}\n\n" \
281
+ f"Fine-Tuned Model V2:\nTest Loss: {test_loss_v2}\nTest Accuracy: {test_accuracy_v2}\n\nClassification Report:\n{report_v2}"
282
+
283
+ # Gradio UI
284
+ with gr.Blocks() as demo:
285
+ with gr.Row():
286
+ dataset_path_input = gr.Textbox(label="Dataset Path")
287
+ data_exploration_output = gr.Textbox(label="Data Exploration Output")
288
+ with gr.Row():
289
+ audio_processing_button = gr.Button("Perform Audio Processing")
290
+ with gr.Row():
291
+ pitch_shifting_checkbox = gr.Checkbox(label="Enable Pitch Shifting")
292
+ time_stretching_checkbox = gr.Checkbox(label="Enable Time Stretching")
293
+ background_noise_checkbox = gr.Checkbox(label="Enable Background Noise")
294
+ data_augmentation_button = gr.Button("Perform Data Augmentation")
295
+ with gr.Row():
296
+ feature_extraction_radio = gr.Radio(["MFCC", "Spectrogram", "Chroma"], label="Feature Extraction")
297
+ feature_extraction_button = gr.Button("Perform Feature Extraction")
298
+ with gr.Row():
299
+ model_type_radio = gr.Radio(["ResNet50", "CNN"], label="Model Type")
300
+ build_model_button = gr.Button("Build Model")
301
+ with gr.Row():
302
+ train_model_button = gr.Button("Train and Save Model")
303
+ evaluate_model_button = gr.Button("Evaluate Model")
304
+ with gr.Row():
305
+ hyperparameter_tuning_button = gr.Button("Hyperparameter Tuning")
306
+ learning_rate_reduction_button = gr.Button("Learning Rate Reduction")
307
+ with gr.Row():
308
+ evaluate_fine_tuned_models_button = gr.Button("Evaluate Fine-Tuned Models")
309
+
310
+ dataset_path_input.change(data_exploration, inputs=dataset_path_input, outputs=data_exploration_output)
311
+ audio_processing_button.click(audio_processing, outputs=None)
312
+ data_augmentation_button.click(data_augmentation, inputs=[pitch_shifting_checkbox, time_stretching_checkbox, background_noise_checkbox], outputs=None)
313
+ feature_extraction_button.click(feature_extraction, inputs=feature_extraction_radio, outputs=None)
314
+ build_model_button.click(build_model, inputs=[model_type_radio, feature_extraction_radio], outputs=None)
315
+ train_model_button.click(train_model, outputs=None)
316
+ evaluate_model_button.click(evaluate_model, outputs=None)
317
+ hyperparameter_tuning_button.click(hyperparameter_tuning, outputs=None)
318
+ learning_rate_reduction_button.click(learning_rate_reduction, outputs=None)
319
+ evaluate_fine_tuned_models_button.click(evaluate_fine_tuned_models, outputs=None)
320
+
321
+ demo.launch()
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
@@ -0,0 +1,49 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [
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+ {"Series":"I", "X":10.0, "Y":8.04},
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+ {"Series":"I", "X":8.0, "Y":6.95},
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+ {"Series":"I", "X":13.0, "Y":7.58},
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+ {"Series":"I", "X":11.0, "Y":8.33},
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+ {"Series":"I", "X":14.0, "Y":9.96},
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+ {"Series":"I", "X":6.0, "Y":7.24},
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+ {"Series":"I", "X":4.0, "Y":4.26},
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+ {"Series":"I", "X":12.0, "Y":10.84},
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+ {"Series":"I", "X":7.0, "Y":4.81},
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+ {"Series":"I", "X":5.0, "Y":5.68},
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+
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+ {"Series":"II", "X":10.0, "Y":9.14},
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+ {"Series":"II", "X":8.0, "Y":8.14},
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+ {"Series":"II", "X":13.0, "Y":8.74},
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+ {"Series":"II", "X":9.0, "Y":8.77},
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+ {"Series":"II", "X":11.0, "Y":9.26},
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+ {"Series":"II", "X":14.0, "Y":8.10},
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+ {"Series":"II", "X":6.0, "Y":6.13},
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+ {"Series":"II", "X":4.0, "Y":3.10},
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+ {"Series":"II", "X":12.0, "Y":9.13},
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+ {"Series":"II", "X":7.0, "Y":7.26},
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+ {"Series":"II", "X":5.0, "Y":4.74},
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+
26
+ {"Series":"III", "X":10.0, "Y":7.46},
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+ {"Series":"III", "X":8.0, "Y":6.77},
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+ {"Series":"III", "X":13.0, "Y":12.74},
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+ {"Series":"III", "X":9.0, "Y":7.11},
30
+ {"Series":"III", "X":11.0, "Y":7.81},
31
+ {"Series":"III", "X":14.0, "Y":8.84},
32
+ {"Series":"III", "X":6.0, "Y":6.08},
33
+ {"Series":"III", "X":4.0, "Y":5.39},
34
+ {"Series":"III", "X":12.0, "Y":8.15},
35
+ {"Series":"III", "X":7.0, "Y":6.42},
36
+ {"Series":"III", "X":5.0, "Y":5.73},
37
+
38
+ {"Series":"IV", "X":8.0, "Y":6.58},
39
+ {"Series":"IV", "X":8.0, "Y":5.76},
40
+ {"Series":"IV", "X":8.0, "Y":7.71},
41
+ {"Series":"IV", "X":8.0, "Y":8.84},
42
+ {"Series":"IV", "X":8.0, "Y":8.47},
43
+ {"Series":"IV", "X":8.0, "Y":7.04},
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+ {"Series":"IV", "X":8.0, "Y":5.25},
45
+ {"Series":"IV", "X":19.0, "Y":12.50},
46
+ {"Series":"IV", "X":8.0, "Y":5.56},
47
+ {"Series":"IV", "X":8.0, "Y":7.91},
48
+ {"Series":"IV", "X":8.0, "Y":6.89}
49
+ ]
sample_data/california_housing_test.csv ADDED
The diff for this file is too large to render. See raw diff
 
sample_data/california_housing_train.csv ADDED
The diff for this file is too large to render. See raw diff
 
sample_data/mnist_test.csv ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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