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.config/.last_opt_in_prompt.yaml ADDED
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+ 2025-07-29 13:36:06,013 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
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+ 2025-07-29 13:36:06,013 INFO ___FILE_ONLY___ ╠═ Downloading: Cloud Storage Command Line Tool ═╣
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+ 2025-07-29 13:36:06,013 INFO ___FILE_ONLY___ ╚
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+ 2025-07-29 13:36:06,016 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
307
+ 2025-07-29 13:36:06,126 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gsutil-20250627154417.tar.gz HTTP/11" 200 12962791
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+ 2025-07-29 13:36:06,779 INFO ___FILE_ONLY___ ═
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+ 2025-07-29 13:36:06,795 INFO ___FILE_ONLY___ ═
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+ 2025-07-29 13:36:06,795 INFO ___FILE_ONLY___ ═
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+ 2025-07-29 13:36:06,796 INFO ___FILE_ONLY___ ═
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+ 2025-07-29 13:36:06,796 INFO ___FILE_ONLY___ ═
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+ 2025-07-29 13:36:06,797 INFO ___FILE_ONLY___ ═
360
+ 2025-07-29 13:36:06,797 INFO ___FILE_ONLY___ ═
361
+ 2025-07-29 13:36:06,797 INFO ___FILE_ONLY___ ═
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+ 2025-07-29 13:36:06,798 INFO ___FILE_ONLY___ ═
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+ 2025-07-29 13:36:06,798 INFO ___FILE_ONLY___ ═
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+ 2025-07-29 13:36:06,798 INFO ___FILE_ONLY___ ═
365
+ 2025-07-29 13:36:06,799 INFO ___FILE_ONLY___ ═
366
+ 2025-07-29 13:36:06,799 INFO ___FILE_ONLY___ ═
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+ 2025-07-29 13:36:06,799 INFO ___FILE_ONLY___ ═
368
+ 2025-07-29 13:36:06,799 INFO ___FILE_ONLY___ ╝
369
+
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+ 2025-07-29 13:36:06,802 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
371
+
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+ 2025-07-29 13:36:06,802 INFO ___FILE_ONLY___ ╠═ Downloading: Cloud Storage Command Line Tool (Platfor... ═╣
373
+
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+ 2025-07-29 13:36:06,802 INFO ___FILE_ONLY___ ╚
375
+ 2025-07-29 13:36:06,805 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
376
+ 2025-07-29 13:36:06,874 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gsutil-nix-20250523104322.tar.gz HTTP/11" 200 1950
377
+ 2025-07-29 13:36:06,875 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
378
+ 2025-07-29 13:36:06,875 INFO ___FILE_ONLY___ ╝
379
+
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+ 2025-07-29 13:36:06,877 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
381
+
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+ 2025-07-29 13:36:06,877 INFO ___FILE_ONLY___ ╠═ Downloading: Default set of gcloud commands ═╣
383
+
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+ 2025-07-29 13:36:06,877 INFO ___FILE_ONLY___ ╚
385
+ 2025-07-29 13:36:06,877 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
386
+ 2025-07-29 13:36:06,877 INFO ___FILE_ONLY___ ╝
387
+
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+ 2025-07-29 13:36:06,879 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
389
+
390
+ 2025-07-29 13:36:06,879 INFO ___FILE_ONLY___ ╠═ Downloading: Google Cloud CLI Core Libraries (Platfor... ═╣
391
+
392
+ 2025-07-29 13:36:06,879 INFO ___FILE_ONLY___ ╚
393
+ 2025-07-29 13:36:06,883 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
394
+ 2025-07-29 13:36:07,225 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-core-nix-20250523104322.tar.gz HTTP/11" 200 2325
395
+ 2025-07-29 13:36:07,225 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
396
+ 2025-07-29 13:36:07,226 INFO ___FILE_ONLY___ ╝
397
+
398
+ 2025-07-29 13:36:07,228 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
399
+
400
+ 2025-07-29 13:36:07,228 INFO ___FILE_ONLY___ ╠═ Downloading: Google Cloud CRC32C Hash Tool ═╣
401
+
402
+ 2025-07-29 13:36:07,228 INFO ___FILE_ONLY___ ╚
403
+ 2025-07-29 13:36:07,228 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
404
+ 2025-07-29 13:36:07,228 INFO ___FILE_ONLY___ ╝
405
+
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+ 2025-07-29 13:36:07,230 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
407
+
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+ 2025-07-29 13:36:07,230 INFO ___FILE_ONLY___ ╠═ Downloading: Google Cloud CRC32C Hash Tool (Platform ... ═╣
409
+
410
+ 2025-07-29 13:36:07,230 INFO ___FILE_ONLY___ ╚
411
+ 2025-07-29 13:36:07,233 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
412
+ 2025-07-29 13:36:07,382 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gcloud-crc32c-linux-x86_64-20250613150750.tar.gz HTTP/11" 200 1525557
413
+ 2025-07-29 13:36:07,729 INFO ___FILE_ONLY___ ═
414
+ 2025-07-29 13:36:07,729 INFO ___FILE_ONLY___ ═
415
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416
+ 2025-07-29 13:36:07,729 INFO ___FILE_ONLY___ ═
417
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418
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419
+ 2025-07-29 13:36:07,729 INFO ___FILE_ONLY___ ═
420
+ 2025-07-29 13:36:07,729 INFO ___FILE_ONLY___ ═
421
+ 2025-07-29 13:36:07,729 INFO ___FILE_ONLY___ ═
422
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424
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426
+ 2025-07-29 13:36:07,730 INFO ___FILE_ONLY___ ═
427
+ 2025-07-29 13:36:07,730 INFO ___FILE_ONLY___ ═
428
+ 2025-07-29 13:36:07,730 INFO ___FILE_ONLY___ ═
429
+ 2025-07-29 13:36:07,730 INFO ___FILE_ONLY___ ═
430
+ 2025-07-29 13:36:07,730 INFO ___FILE_ONLY___ ═
431
+ 2025-07-29 13:36:07,730 INFO ___FILE_ONLY___ ═
432
+ 2025-07-29 13:36:07,730 INFO ___FILE_ONLY___ ═
433
+ 2025-07-29 13:36:07,730 INFO ___FILE_ONLY___ ═
434
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435
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436
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439
+ 2025-07-29 13:36:07,731 INFO ___FILE_ONLY___ ═
440
+ 2025-07-29 13:36:07,731 INFO ___FILE_ONLY___ ═
441
+ 2025-07-29 13:36:07,731 INFO ___FILE_ONLY___ ═
442
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443
+ 2025-07-29 13:36:07,731 INFO ___FILE_ONLY___ ═
444
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445
+ 2025-07-29 13:36:07,731 INFO ___FILE_ONLY___ ═
446
+ 2025-07-29 13:36:07,732 INFO ___FILE_ONLY___ ═
447
+ 2025-07-29 13:36:07,732 INFO ___FILE_ONLY___ ═
448
+ 2025-07-29 13:36:07,732 INFO ___FILE_ONLY___ ═
449
+ 2025-07-29 13:36:07,732 INFO ___FILE_ONLY___ ═
450
+ 2025-07-29 13:36:07,732 INFO ___FILE_ONLY___ ═
451
+ 2025-07-29 13:36:07,732 INFO ___FILE_ONLY___ ═
452
+ 2025-07-29 13:36:07,732 INFO ___FILE_ONLY___ ═
453
+ 2025-07-29 13:36:07,732 INFO ___FILE_ONLY___ ═
454
+ 2025-07-29 13:36:07,732 INFO ___FILE_ONLY___ ═
455
+ 2025-07-29 13:36:07,732 INFO ___FILE_ONLY___ ═
456
+ 2025-07-29 13:36:07,732 INFO ___FILE_ONLY___ ═
457
+ 2025-07-29 13:36:07,733 INFO ___FILE_ONLY___ ═
458
+ 2025-07-29 13:36:07,733 INFO ___FILE_ONLY___ ═
459
+ 2025-07-29 13:36:07,733 INFO ___FILE_ONLY___ ═
460
+ 2025-07-29 13:36:07,733 INFO ___FILE_ONLY___ ═
461
+ 2025-07-29 13:36:07,733 INFO ___FILE_ONLY___ ═
462
+ 2025-07-29 13:36:07,733 INFO ___FILE_ONLY___ ═
463
+ 2025-07-29 13:36:07,733 INFO ___FILE_ONLY___ ═
464
+ 2025-07-29 13:36:07,733 INFO ___FILE_ONLY___ ═
465
+ 2025-07-29 13:36:07,733 INFO ___FILE_ONLY___ ═
466
+ 2025-07-29 13:36:07,733 INFO ___FILE_ONLY___ ═
467
+ 2025-07-29 13:36:07,733 INFO ___FILE_ONLY___ ═
468
+ 2025-07-29 13:36:07,733 INFO ___FILE_ONLY___ ═
469
+ 2025-07-29 13:36:07,734 INFO ___FILE_ONLY___ ═
470
+ 2025-07-29 13:36:07,734 INFO ___FILE_ONLY___ ═
471
+ 2025-07-29 13:36:07,734 INFO ___FILE_ONLY___ ═
472
+ 2025-07-29 13:36:07,734 INFO ___FILE_ONLY___ ═
473
+ 2025-07-29 13:36:07,734 INFO ___FILE_ONLY___ ╝
474
+
475
+ 2025-07-29 13:36:07,736 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
476
+
477
+ 2025-07-29 13:36:07,736 INFO ___FILE_ONLY___ ╠═ Downloading: gcloud cli dependencies (Platform Specific) ═╣
478
+
479
+ 2025-07-29 13:36:07,736 INFO ___FILE_ONLY___ ╚
480
+ 2025-07-29 13:36:07,739 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
481
+ 2025-07-29 13:36:08,113 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
+ 2025-07-29 13:36:08,114 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
483
+ 2025-07-29 13:36:08,114 INFO ___FILE_ONLY___ ╝
484
+
485
+ 2025-07-29 13:36:08,116 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
486
+
487
+ 2025-07-29 13:36:08,116 INFO ___FILE_ONLY___ ╠═ Installing: BigQuery Command Line Tool ═╣
488
+
489
+ 2025-07-29 13:36:08,117 INFO ___FILE_ONLY___ ╚
490
+ 2025-07-29 13:36:08,213 INFO ___FILE_ONLY___ ═
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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502
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503
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504
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505
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506
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507
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508
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509
+ 2025-07-29 13:36:08,259 INFO ___FILE_ONLY___ ═
510
+ 2025-07-29 13:36:08,261 INFO ___FILE_ONLY___ ═
511
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512
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513
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514
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515
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516
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517
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518
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519
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520
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521
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522
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523
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524
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525
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526
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527
+ 2025-07-29 13:36:08,312 INFO ___FILE_ONLY___ ═
528
+ 2025-07-29 13:36:08,314 INFO ___FILE_ONLY___ ═
529
+ 2025-07-29 13:36:08,316 INFO ___FILE_ONLY___ ═
530
+ 2025-07-29 13:36:08,319 INFO ___FILE_ONLY___ ═
531
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532
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533
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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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540
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541
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542
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543
+ 2025-07-29 13:36:08,351 INFO ___FILE_ONLY___ ═
544
+ 2025-07-29 13:36:08,353 INFO ___FILE_ONLY___ ═
545
+ 2025-07-29 13:36:08,355 INFO ___FILE_ONLY___ ═
546
+ 2025-07-29 13:36:08,358 INFO ___FILE_ONLY___ ═
547
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548
+ 2025-07-29 13:36:08,361 INFO ___FILE_ONLY___ ═
549
+ 2025-07-29 13:36:08,364 INFO ___FILE_ONLY___ ═
550
+ 2025-07-29 13:36:08,364 INFO ___FILE_ONLY___ ╝
551
+
552
+ 2025-07-29 13:36:08,372 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
553
+
554
+ 2025-07-29 13:36:08,372 INFO ___FILE_ONLY___ ╠═ Installing: BigQuery Command Line Tool (Platform Spec... ═╣
555
+
556
+ 2025-07-29 13:36:08,372 INFO ___FILE_ONLY___ ╚
557
+ 2025-07-29 13:36:08,373 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
558
+ 2025-07-29 13:36:08,373 INFO ___FILE_ONLY___ ╝
559
+
560
+ 2025-07-29 13:36:08,380 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
561
+
562
+ 2025-07-29 13:36:08,380 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.12 ═╣
563
+
564
+ 2025-07-29 13:36:08,380 INFO ___FILE_ONLY___ ╚
565
+ 2025-07-29 13:36:08,382 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
566
+ 2025-07-29 13:36:08,383 INFO ___FILE_ONLY___ ╝
567
+
568
+ 2025-07-29 13:36:08,384 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
569
+
570
+ 2025-07-29 13:36:08,384 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.12 (Platform Specific) ═╣
571
+
572
+ 2025-07-29 13:36:08,384 INFO ___FILE_ONLY___ ╚
573
+ 2025-07-29 13:36:10,620 INFO ___FILE_ONLY___ ═
574
+ 2025-07-29 13:36:10,633 INFO ___FILE_ONLY___ ═
575
+ 2025-07-29 13:36:10,645 INFO ___FILE_ONLY___ ═
576
+ 2025-07-29 13:36:10,658 INFO ___FILE_ONLY___ ═
577
+ 2025-07-29 13:36:10,671 INFO ___FILE_ONLY___ ═
578
+ 2025-07-29 13:36:10,685 INFO ___FILE_ONLY___ ═
579
+ 2025-07-29 13:36:10,699 INFO ___FILE_ONLY___ ═
580
+ 2025-07-29 13:36:10,711 INFO ___FILE_ONLY___ ═
581
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582
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583
+ 2025-07-29 13:36:10,751 INFO ___FILE_ONLY___ ═
584
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585
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586
+ 2025-07-29 13:36:10,790 INFO ___FILE_ONLY___ ═
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
+ 2025-07-29 13:36:10,879 INFO ___FILE_ONLY___ ═
593
+ 2025-07-29 13:36:10,893 INFO ___FILE_ONLY___ ═
594
+ 2025-07-29 13:36:10,907 INFO ___FILE_ONLY___ ═
595
+ 2025-07-29 13:36:10,920 INFO ___FILE_ONLY___ ═
596
+ 2025-07-29 13:36:10,933 INFO ___FILE_ONLY___ ═
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
+ 2025-07-29 13:36:11,013 INFO ___FILE_ONLY___ ═
603
+ 2025-07-29 13:36:11,027 INFO ___FILE_ONLY___ ═
604
+ 2025-07-29 13:36:11,040 INFO ___FILE_ONLY___ ═
605
+ 2025-07-29 13:36:11,054 INFO ___FILE_ONLY___ ═
606
+ 2025-07-29 13:36:11,068 INFO ___FILE_ONLY___ ═
607
+ 2025-07-29 13:36:11,086 INFO ___FILE_ONLY___ ═
608
+ 2025-07-29 13:36:11,102 INFO ___FILE_ONLY___ ═
609
+ 2025-07-29 13:36:12,007 INFO ___FILE_ONLY___ ═
610
+ 2025-07-29 13:36:12,035 INFO ___FILE_ONLY___ ═
611
+ 2025-07-29 13:36:12,559 INFO ___FILE_ONLY___ ═
612
+ 2025-07-29 13:36:12,579 INFO ___FILE_ONLY___ ═
613
+ 2025-07-29 13:36:12,604 INFO ___FILE_ONLY___ ═
614
+ 2025-07-29 13:36:12,634 INFO ___FILE_ONLY___ ═
615
+ 2025-07-29 13:36:12,656 INFO ___FILE_ONLY___ ═
616
+ 2025-07-29 13:36:12,686 INFO ___FILE_ONLY___ ═
617
+ 2025-07-29 13:36:12,707 INFO ___FILE_ONLY___ ═
618
+ 2025-07-29 13:36:12,727 INFO ___FILE_ONLY___ ═
619
+ 2025-07-29 13:36:12,746 INFO ___FILE_ONLY___ ═
620
+ 2025-07-29 13:36:12,766 INFO ___FILE_ONLY___ ═
621
+ 2025-07-29 13:36:12,858 INFO ___FILE_ONLY___ ═
622
+ 2025-07-29 13:36:12,877 INFO ___FILE_ONLY___ ═
623
+ 2025-07-29 13:36:13,020 INFO ___FILE_ONLY___ ═
624
+ 2025-07-29 13:36:13,037 INFO ___FILE_ONLY___ ═
625
+ 2025-07-29 13:36:13,055 INFO ___FILE_ONLY___ ═
626
+ 2025-07-29 13:36:13,073 INFO ___FILE_ONLY___ ═
627
+ 2025-07-29 13:36:13,089 INFO ___FILE_ONLY___ ═
628
+ 2025-07-29 13:36:13,108 INFO ___FILE_ONLY___ ═
629
+ 2025-07-29 13:36:13,126 INFO ___FILE_ONLY___ ═
630
+ 2025-07-29 13:36:13,144 INFO ___FILE_ONLY___ ═
631
+ 2025-07-29 13:36:13,173 INFO ___FILE_ONLY___ ═
632
+ 2025-07-29 13:36:13,682 INFO ___FILE_ONLY___ ═
633
+ 2025-07-29 13:36:13,682 INFO ___FILE_ONLY___ ╝
634
+
635
+ 2025-07-29 13:36:13,738 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
636
+
637
+ 2025-07-29 13:36:13,739 INFO ___FILE_ONLY___ ╠═ Installing: Cloud Storage Command Line Tool ═╣
638
+
639
+ 2025-07-29 13:36:13,739 INFO ___FILE_ONLY___ ╚
640
+ 2025-07-29 13:36:14,290 INFO ___FILE_ONLY___ ═
641
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642
+ 2025-07-29 13:36:14,314 INFO ___FILE_ONLY___ ═
643
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644
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645
+ 2025-07-29 13:36:14,371 INFO ___FILE_ONLY___ ═
646
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647
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648
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649
+ 2025-07-29 13:36:14,435 INFO ___FILE_ONLY___ ═
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
+ 2025-07-29 13:36:14,567 INFO ___FILE_ONLY___ ═
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
+ 2025-07-29 13:36:14,928 INFO ___FILE_ONLY___ ═
685
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686
+ 2025-07-29 13:36:14,949 INFO ___FILE_ONLY___ ═
687
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688
+ 2025-07-29 13:36:14,970 INFO ___FILE_ONLY___ ═
689
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690
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691
+ 2025-07-29 13:36:15,012 INFO ___FILE_ONLY___ ═
692
+ 2025-07-29 13:36:15,026 INFO ___FILE_ONLY___ ═
693
+ 2025-07-29 13:36:15,041 INFO ___FILE_ONLY___ ═
694
+ 2025-07-29 13:36:15,054 INFO ___FILE_ONLY___ ═
695
+ 2025-07-29 13:36:15,070 INFO ___FILE_ONLY___ ═
696
+ 2025-07-29 13:36:15,086 INFO ___FILE_ONLY___ ═
697
+ 2025-07-29 13:36:15,108 INFO ___FILE_ONLY___ ═
698
+ 2025-07-29 13:36:15,126 INFO ___FILE_ONLY___ ═
699
+ 2025-07-29 13:36:15,145 INFO ___FILE_ONLY___ ═
700
+ 2025-07-29 13:36:15,145 INFO ___FILE_ONLY___ ╝
701
+
702
+ 2025-07-29 13:36:15,177 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
703
+
704
+ 2025-07-29 13:36:15,178 INFO ___FILE_ONLY___ ╠═ Installing: Cloud Storage Command Line Tool (Platform... ═╣
705
+
706
+ 2025-07-29 13:36:15,178 INFO ___FILE_ONLY___ ╚
707
+ 2025-07-29 13:36:15,179 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
708
+ 2025-07-29 13:36:15,179 INFO ___FILE_ONLY___ ╝
709
+
710
+ 2025-07-29 13:36:15,183 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
711
+
712
+ 2025-07-29 13:36:15,183 INFO ___FILE_ONLY___ ╠═ Installing: Default set of gcloud commands ═╣
713
+
714
+ 2025-07-29 13:36:15,183 INFO ___FILE_ONLY___ ╚
715
+ 2025-07-29 13:36:15,186 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
716
+ 2025-07-29 13:36:15,186 INFO ___FILE_ONLY___ ╝
717
+
718
+ 2025-07-29 13:36:15,188 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
719
+
720
+ 2025-07-29 13:36:15,188 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CLI Core Libraries (Platform... ═╣
721
+
722
+ 2025-07-29 13:36:15,188 INFO ___FILE_ONLY___ ╚
723
+ 2025-07-29 13:36:15,189 INFO ___FILE_ONLY___ ══════════════════════════════
724
+ 2025-07-29 13:36:15,190 INFO ___FILE_ONLY___ ══════════════════════════════
725
+ 2025-07-29 13:36:15,190 INFO ___FILE_ONLY___ ╝
726
+
727
+ 2025-07-29 13:36:15,194 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
728
+
729
+ 2025-07-29 13:36:15,194 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool ═╣
730
+
731
+ 2025-07-29 13:36:15,194 INFO ___FILE_ONLY___ ╚
732
+ 2025-07-29 13:36:15,197 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
733
+ 2025-07-29 13:36:15,197 INFO ___FILE_ONLY___ ╝
734
+
735
+ 2025-07-29 13:36:15,199 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
736
+
737
+ 2025-07-29 13:36:15,199 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool (Platform S... ═╣
738
+
739
+ 2025-07-29 13:36:15,199 INFO ___FILE_ONLY___ ╚
740
+ 2025-07-29 13:36:15,239 INFO ___FILE_ONLY___ ══════════════════════════════
741
+ 2025-07-29 13:36:15,240 INFO ___FILE_ONLY___ ══════════════════════════════
742
+ 2025-07-29 13:36:15,240 INFO ___FILE_ONLY___ ╝
743
+
744
+ 2025-07-29 13:36:15,245 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
745
+
746
+ 2025-07-29 13:36:15,245 INFO ___FILE_ONLY___ ╠═ Installing: gcloud cli dependencies (Platform Specific) ═╣
747
+
748
+ 2025-07-29 13:36:15,245 INFO ___FILE_ONLY___ ╚
749
+ 2025-07-29 13:36:15,246 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
750
+ 2025-07-29 13:36:15,246 INFO ___FILE_ONLY___ ╝
751
+
752
+ 2025-07-29 13:36:15,251 DEBUG root Updating notification cache...
753
+ 2025-07-29 13:36:15,252 INFO ___FILE_ONLY___
754
+
755
+ 2025-07-29 13:36:15,254 INFO ___FILE_ONLY___ Performing post processing steps...
756
+ 2025-07-29 13:36:15,254 DEBUG root Executing command: ['/tools/google-cloud-sdk/bin/gcloud', 'components', 'post-process']
757
+ 2025-07-29 13:36:24,170 DEBUG ___FILE_ONLY___
758
+ 2025-07-29 13:36:24,170 DEBUG ___FILE_ONLY___
759
+ 2025-07-29 13:36:24,310 INFO root descriptor_list: [{'universeDomain': 'googleapis.com', 'universeShortName': '', 'authenticationDomain': 'auth.cloud.google.com', 'projectPrefix': '', 'cloudWebDomain': 'cloud.google.com', 'documentationDomain': 'cloud.google.com', 'version': '1.0.0', 'state': 'primary', 'artifactRegistryDomain': 'pkg.dev'}]
760
+ 2025-07-29 13:36:24,311 INFO ___FILE_ONLY___
761
+ Update done!
762
+
763
+
764
+ 2025-07-29 13:36:24,314 DEBUG root Chosen display Format:none
765
+ 2025-07-29 13:36:24,314 INFO root Display format: "none"
.config/logs/2025.07.29/13.36.15.785307.log ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ 2025-07-29 13:36:15,786 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2025-07-29 13:36:15,788 DEBUG root Loaded Command Group: ['gcloud', 'components', 'post_process']
3
+ 2025-07-29 13:36:15,790 DEBUG root Running [gcloud.components.post-process] with arguments: []
4
+ 2025-07-29 13:36:24,039 DEBUG root Chosen display Format:none
5
+ 2025-07-29 13:36:24,040 INFO root Display format: "none"
.config/logs/2025.07.29/13.36.25.001588.log ADDED
@@ -0,0 +1,153 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2025-07-29 13:36:25,002 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2025-07-29 13:36:25,004 DEBUG root Loaded Command Group: ['gcloud', 'components', 'update']
3
+ 2025-07-29 13:36:25,006 DEBUG root Running [gcloud.components.update] with arguments: [--quiet: "True", COMPONENT-IDS:8: "['gcloud', 'core', 'bq', 'gsutil', 'compute', 'preview', 'alpha', 'beta']"]
4
+ 2025-07-29 13:36:25,007 INFO ___FILE_ONLY___ Beginning update. This process may take several minutes.
5
+
6
+ 2025-07-29 13:36:25,016 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
7
+ 2025-07-29 13:36:25,361 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components-2.json HTTP/11" 200 239806
8
+ 2025-07-29 13:36:25,376 WARNING root Component [compute] no longer exists.
9
+ 2025-07-29 13:36:25,377 INFO ___FILE_ONLY___
10
+
11
+ 2025-07-29 13:36:25,378 INFO ___FILE_ONLY___
12
+ Your current Google Cloud CLI version is: 532.0.0
13
+
14
+ 2025-07-29 13:36:25,378 INFO ___FILE_ONLY___ Installing components from version: 532.0.0
15
+
16
+ 2025-07-29 13:36:25,378 INFO ___FILE_ONLY___
17
+
18
+ 2025-07-29 13:36:25,378 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
+ 2025-07-29 13:36:25,378 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
+ 2025-07-29 13:36:25,379 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
+ 2025-07-29 13:36:25,393 INFO ___FILE_ONLY___ ┌────────────────────────────────────────────────┐
22
+ 2025-07-29 13:36:25,393 INFO ___FILE_ONLY___
23
+
24
+ 2025-07-29 13:36:25,393 INFO ___FILE_ONLY___ │ These components will be installed. │
25
+ 2025-07-29 13:36:25,393 INFO ___FILE_ONLY___
26
+
27
+ 2025-07-29 13:36:25,393 INFO ___FILE_ONLY___ ├─────────────────────────┬────────────┬─────────┤
28
+ 2025-07-29 13:36:25,394 INFO ___FILE_ONLY___
29
+
30
+ 2025-07-29 13:36:25,394 INFO ___FILE_ONLY___ │ Name │ Version │ Size │
31
+ 2025-07-29 13:36:25,394 INFO ___FILE_ONLY___
32
+
33
+ 2025-07-29 13:36:25,394 INFO ___FILE_ONLY___ ├─────────────────────────┼────────────┼─────────┤
34
+ 2025-07-29 13:36:25,394 INFO ___FILE_ONLY___
35
+
36
+ 2025-07-29 13:36:25,394 INFO ___FILE_ONLY___ │
37
+ 2025-07-29 13:36:25,394 INFO ___FILE_ONLY___ gcloud Alpha Commands
38
+ 2025-07-29 13:36:25,394 INFO ___FILE_ONLY___
39
+ 2025-07-29 13:36:25,394 INFO ___FILE_ONLY___ │
40
+ 2025-07-29 13:36:25,394 INFO ___FILE_ONLY___ 2025.07.25
41
+ 2025-07-29 13:36:25,394 INFO ___FILE_ONLY___
42
+ 2025-07-29 13:36:25,394 INFO ___FILE_ONLY___ │
43
+ 2025-07-29 13:36:25,394 INFO ___FILE_ONLY___ < 1 MiB
44
+ 2025-07-29 13:36:25,394 INFO ___FILE_ONLY___
45
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1
+ # -*- coding: utf-8 -*-
2
+ """chem-sim.ipynb
3
+
4
+ Automatically generated by Colab.
5
+
6
+ Original file is located at
7
+ https://colab.research.google.com/drive/1GgGC-fVnA0fSxU859NjSi_jH8yiWUKOu
8
+ """
9
+
10
+ !pip install tensorflow==2.15
11
+
12
+ """# Chem simulation using scipy"""
13
+
14
+ import numpy as np
15
+ import matplotlib.pyplot as plt
16
+ import pandas as pd
17
+ from scipy.integrate import solve_ivp
18
+ import random
19
+ import tensorflow as tf
20
+
21
+ """# Dataset
22
+
23
+ $$
24
+ \displaystyle
25
+ k = A \cdot e^{-\frac{E_a}{RT}}
26
+ $$
27
+
28
+
29
+ k : Rate constant (what we’re solving for)
30
+
31
+ A : Pre-exponential factor (frequency factor)
32
+
33
+ Ea : Activation energy (J/mol)
34
+
35
+ R : Gas constant 8.314 J/mol·K
36
+
37
+ T : Temperature (in Kelvin)
38
+
39
+ | temp: Kelvin | pH: 0–14 scale | Ea: in kJ/mol | A_factor: 1/s |
40
+
41
+ ## Zero order
42
+ """
43
+
44
+ def zero(t, y, k):
45
+ A, B, C = y
46
+ dA_dt = -k
47
+ dB_dt = 0
48
+ dC_dt = k
49
+ return [dA_dt, dB_dt, dC_dt]
50
+
51
+ """## First Order"""
52
+
53
+ def first(t, y, k):
54
+ A, B, C = y
55
+ dA_dt = -k * A
56
+ dB_dt = 0
57
+ dC_dt = +k * A
58
+ return [dA_dt, dB_dt, dC_dt]
59
+
60
+ def decay_first(t, y, k):
61
+ A, B, C = y
62
+ dA_dt = -k * A
63
+ dB_dt = 0
64
+ dC_dt = 0
65
+ return [dA_dt, dB_dt, dC_dt]
66
+
67
+ def reversible_first(t, y, k, k_1):
68
+ A, B, C = y
69
+ dA_dt = -k * A + k_1 * C
70
+ dB_dt = 0
71
+ dC_dt = k * A - k_1 * C
72
+ return [dA_dt, dB_dt, dC_dt]
73
+
74
+ """## Second Order"""
75
+
76
+ def second1(t, y, k):
77
+ A, B, C = y
78
+ dA_dt = -k * A * B
79
+ dB_dt = -k * A * B
80
+ dC_dt = +k * A * B
81
+ return [dA_dt, dB_dt, dC_dt]
82
+
83
+ def second2(t, y, k):
84
+ A, B, C = y
85
+ dA_dt = -2 * k * A**2
86
+ dB_dt = 0
87
+ dC_dt = +k * A**2
88
+ return [dA_dt, dB_dt, dC_dt]
89
+
90
+ def reversible_second1(t, y, k, k_1):
91
+ A, B, C = y
92
+ dA_dt = -k * A * B + k_1 * C
93
+ dB_dt = -k * A * B + k_1 * C
94
+ dC_dt = +k * A * B - k_1 * C
95
+ return [dA_dt, dB_dt, dC_dt]
96
+
97
+ def reversible_second2(t, y, k, k_1):
98
+ A, B, C = y
99
+ dA_dt = -2 * k * A**2 + 2 * k_1 * C
100
+ dB_dt = 0
101
+ dC_dt = +k * A**2 - k_1 * C
102
+ return [dA_dt, dB_dt, dC_dt]
103
+
104
+ """## Third order"""
105
+
106
+ def third1(t, y, k):
107
+ A, B, C = y
108
+ dA_dt = -3 * k * A**3
109
+ dB_dt = 0
110
+ dC_dt = +k * A**3
111
+ return [dA_dt, dB_dt, dC_dt]
112
+
113
+ def third2(t, y, k):
114
+ A, B, C = y
115
+ dA_dt = -2 * k * A**2 * B
116
+ dB_dt = -1 * k * A**2 * B
117
+ dC_dt = +k * A**2 * B
118
+ return [dA_dt, dB_dt, dC_dt]
119
+
120
+ def reversible_third1(t, y, k, k_1):
121
+ A, B, C = y
122
+ dA_dt = -3 * k * A**3 + 3 * k_1 * C
123
+ dB_dt = 0
124
+ dC_dt = +k * A**3 - k_1 * C
125
+ return [dA_dt, dB_dt, dC_dt]
126
+
127
+ def reversible_third2(t, y, k, k_1):
128
+ A, B, C = y
129
+ dA_dt = -2 * k * A**2 * B + 2 * k_1 * C
130
+ dB_dt = -1 * k * A**2 * B + 1 * k_1 * C
131
+ dC_dt = +k * A**2 * B - k_1 * C
132
+ return [dA_dt, dB_dt, dC_dt]
133
+
134
+ """## functions"""
135
+
136
+ def compute_k(temp, Ea, A_factor):
137
+ R = 8.314
138
+ Ea_J = Ea * 1000 # Convert Ea from kJ/mol to J/mol
139
+ k = A_factor * np.exp(-Ea_J / (R * temp))
140
+ return k
141
+
142
+ def ode1(A0, B0, C0, temp, Ea, A_factor):
143
+ y0 = [A0, B0, C0]
144
+ k = compute_k(temp, Ea, A_factor)
145
+ k_1 = k * random.uniform(0.5, 0.9)
146
+
147
+ t_span = (0, 8) # From time 0 to 10 seconds
148
+ t_eval = np.linspace(0, 8, 11) # 11 points where you want the solution
149
+
150
+ num = random.randint(0, 11) # For choosing between first or decay if not reversible
151
+
152
+ match num:
153
+ case 0:
154
+ func_name = zero
155
+ is_reversible = 0
156
+ order = 'zero'
157
+ case 1:
158
+ func_name = first
159
+ is_reversible = 0
160
+ order = 'first'
161
+ case 2:
162
+ func_name = decay_first
163
+ is_reversible = 0
164
+ order = 'first'
165
+ case 3:
166
+ func_name = reversible_first
167
+ is_reversible = 1
168
+ order = 'first'
169
+ case 4:
170
+ func_name = second1
171
+ is_reversible = 0
172
+ order = 'second'
173
+ case 5:
174
+ func_name = second2
175
+ is_reversible = 0
176
+ order = 'second'
177
+ case 6:
178
+ func_name = reversible_second1
179
+ is_reversible = 1
180
+ order = 'second'
181
+ case 7:
182
+ func_name = reversible_second2
183
+ is_reversible = 1
184
+ order = 'second'
185
+ case 8:
186
+ func_name = third1
187
+ is_reversible = 0
188
+ order = 'third'
189
+ case 9:
190
+ func_name = third2
191
+ is_reversible = 0
192
+ order = 'third'
193
+ case 10:
194
+ func_name = reversible_third1
195
+ is_reversible = 1
196
+ order = 'third'
197
+ case 11:
198
+ func_name = reversible_third2
199
+ is_reversible = 1
200
+ order = 'third'
201
+
202
+
203
+ if is_reversible == 1:
204
+ solution = solve_ivp(
205
+ func_name,
206
+ t_span,
207
+ y0,
208
+ args=(k, k_1),
209
+ t_eval=t_eval
210
+ )
211
+ elif is_reversible == 0:
212
+ solution = solve_ivp(
213
+ func_name,
214
+ t_span,
215
+ y0,
216
+ args=(k,),
217
+ t_eval=t_eval
218
+ )
219
+
220
+
221
+ return solution.t, solution.y[0], solution.y[1], solution.y[2], k, k_1, is_reversible, order
222
+
223
+ """## dataframe"""
224
+
225
+ results = []
226
+
227
+ counter = 0
228
+ while counter < 6000:
229
+ counter += 1
230
+
231
+ A0 = round(random.uniform(1.0, 10.0), 2)
232
+ B0 = round(random.uniform(0.0, 5.0), 2)
233
+ C0 = round(random.uniform(0.0, 5.0), 2)
234
+ temp = random.randint(270, 280)
235
+ pH = round(random.uniform(1.0, 14.0), 2)
236
+ Ea = random.randint(90, 100)
237
+ A_factor = round(random.uniform(2e16, 5e17), 2)
238
+ pressure = round(random.uniform(0.5, 5.0), 2)
239
+ weight = round(random.uniform(20, 200), 1)
240
+ structure = random.choice(['Linear', 'Ring', 'Branched', 'Unknown'])
241
+ catalyst = random.choice(['None', 'Enzyme', 'Acid', 'Base'])
242
+ time, A, B, C, k, k_1, is_reversible, order = ode1(A0, B0, C0, temp, Ea, A_factor)
243
+
244
+ row = {
245
+ 'order' : order,
246
+ 'temp': temp,
247
+ 'pH': pH,
248
+ 'Ea': Ea,
249
+ 'A_factor': A_factor,
250
+ 'pressure': pressure,
251
+ 'log_pressure' : np.log(pressure),
252
+ 'weight': weight,
253
+ 'structure': structure,
254
+ 'catalyst': catalyst,
255
+ 'is_reversible': is_reversible,
256
+ 'k' : k,
257
+ 'k_1' : k_1,
258
+ 'A0': A[0], 'A1': A[1], 'A2': A[2], 'A3': A[3], 'A4': A[4],
259
+ 'A5': A[5], 'A6': A[6], 'A7': A[7], 'A8': A[8], 'A9': A[9], 'A10': A[10],
260
+ 'B0': B[0], 'B1': B[1], 'B2': B[2], 'B3': B[3], 'B4': B[4],
261
+ 'B5': B[5], 'B6': B[6], 'B7': B[7], 'B8': B[8], 'B9': B[9], 'B10': B[10],
262
+ 'C0': C[0], 'C1': C[1], 'C2': C[2], 'C3': C[3], 'C4': C[4],
263
+ 'C5': C[5], 'C6': C[6], 'C7': C[7], 'C8': C[8], 'C9': C[9], 'C10': C[10]
264
+ }
265
+ results.append(row)
266
+
267
+ df = pd.DataFrame(results)
268
+ df_original = df.copy()
269
+ # df
270
+
271
+ """# Machine learning
272
+
273
+ ## Data preparation
274
+
275
+ - removing 'structure' and 'catalyst' from dataframe
276
+ - mapping 0 to zero , 1 to first, 2 to second and 3 to third in order column
277
+ - mapping structure and catalyst
278
+ """
279
+
280
+ structure_map = {'Linear': 0, 'Ring': 1, 'Branched': 2, 'Unknown': 3}
281
+ catalyst_map = {'None': 0, 'Enzyme': 1, 'Acid': 2, 'Base': 3}
282
+ order_map = {'zero': 0, 'first': 1, 'second': 2, 'third' : 3}
283
+ df['structure'] = df['structure'].map(structure_map)
284
+ df['catalyst'] = df['catalyst'].map(catalyst_map)
285
+ df['order'] = df['order'].map(order_map)
286
+ # df
287
+
288
+ """## Models
289
+
290
+ ## DNN (Deep Neural Networks)
291
+
292
+ - saving file as csv
293
+ """
294
+
295
+ df_X = df_original.drop(['order'], axis=1)
296
+ df_y = df_original['order']
297
+
298
+ train_df = df_X.copy()
299
+ train_df['order'] = df_y
300
+
301
+ train_df.to_csv('chem_data_train.csv', index=False)
302
+ train_df.to_csv('chem_data_test.csv', index=False)
303
+
304
+ """- DNNs"""
305
+
306
+ csv_columns = ['temp', 'pH', 'Ea', 'A_factor', 'pressure', 'log_pressure', 'weight', 'structure', 'catalyst', 'is_reversible', 'k', 'k_1']
307
+ classes = ['First_Order','Second_Order','Third_Order']
308
+
309
+ train_path = './chem_data_train.csv'
310
+ test_path = './chem_data_train.csv'
311
+
312
+ train = pd.read_csv(train_path)
313
+ test = pd.read_csv(test_path)
314
+
315
+ # train.head()
316
+
317
+ if 'order' in train.columns:
318
+ train_y = train.pop('order')
319
+ if 'order' in test.columns:
320
+ test_y = test.pop('order')
321
+
322
+ # Fill missing values in the 'catalyst' column
323
+ train['catalyst'] = train['catalyst'].fillna('None') #NaN values arenot accepted by classifier thats why convert every Nan values to none
324
+ test['catalyst'] = test['catalyst'].fillna('None')
325
+
326
+
327
+ # train.head() #the species column is now gone
328
+
329
+ # Define categorical and numerical feature columns
330
+ CATEGORICAL_COLUMNS = ['structure', 'catalyst'] #columns that have strings
331
+ NUMERIC_COLUMNS = ['temp', 'pH', 'Ea', 'A_factor', 'pressure', 'log_pressure', 'weight',
332
+ 'is_reversible', 'k', 'k_1', 'A0', 'A1', 'A2', 'A3', 'A4', 'A5', 'A6', 'A7', 'A8', 'A9', 'A10',
333
+ 'B0', 'B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B9', 'B10',
334
+ 'C0', 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9', 'C10'] #columns that have numerical values
335
+
336
+ feature_columns = []
337
+ for feature_name in CATEGORICAL_COLUMNS:
338
+ vocabulary = train[feature_name].unique() #Assining each string a numerical uinque value because our dumb ahh model canot understand english
339
+ cat_column = tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary)
340
+ indicator_column = tf.feature_column.indicator_column(cat_column) #it creates binary coolumns that will be mapped in to feature columns and it will be steamlined to our DNN model
341
+ feature_columns.append(indicator_column)
342
+
343
+ for feature_name in NUMERIC_COLUMNS:
344
+ feature_columns.append(tf.feature_column.numeric_column(feature_name, dtype=tf.float32))
345
+
346
+ print(feature_columns)
347
+
348
+ import logging
349
+ tf.get_logger().setLevel(logging.INFO)
350
+
351
+ #setting up input function
352
+
353
+ def input_fn(features,labels,training=True,batch_size=500):
354
+ #convert the inputs to a dataset
355
+ dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels)) #this cnonverts the dataset into tensorflow object
356
+
357
+ if training:
358
+ dataset = dataset.shuffle(1000).repeat()
359
+
360
+ return dataset.batch(batch_size)
361
+
362
+ from sklearn.preprocessing import StandardScaler
363
+
364
+ # Normalize the numerical features in the training data
365
+ scaler = StandardScaler()
366
+ train_normalized = train.copy()
367
+ train_normalized[NUMERIC_COLUMNS] = scaler.fit_transform(train[NUMERIC_COLUMNS])
368
+
369
+ test_normalized = test.copy()
370
+ test_normalized[NUMERIC_COLUMNS] = scaler.transform(test[NUMERIC_COLUMNS])
371
+
372
+ from sklearn.preprocessing import LabelEncoder
373
+
374
+ # Convert the 'order' labels to numerical values
375
+ le = LabelEncoder()
376
+ train_y_encoded = le.fit_transform(train_y) #we used sckit label encoder to encode the values
377
+
378
+ classifier = tf.estimator.DNNClassifier(
379
+ feature_columns=feature_columns,
380
+ hidden_units=[50, 40],
381
+ n_classes=4, # We have 4 classes: zero, first, second, third
382
+ optimizer=tf.keras.optimizers.legacy.RMSprop(learning_rate=0.001))
383
+
384
+ classifier.train(
385
+ input_fn=lambda: input_fn(train_normalized, train_y_encoded, training=True),
386
+ steps=600
387
+ )
388
+
389
+ test_y_encoded = le.fit_transform(test_y) #we used sckit label encoder to encode the values better than 1 2 3 4 5 blah blah
390
+
391
+ classifier.evaluate(input_fn=lambda: input_fn(test_normalized,test_y_encoded,training=False))
392
+
393
+ """- accuracy = 0.99983335
394
+
395
+ # Interactive/sliders
396
+
397
+ - TODO: be able to change chemical-initial-conc, temp, ea, A_factor, pH, molecular-weight using sliders/input
398
+ - best ml model predicts the order of the differential equation from that
399
+ """
400
+
401
+ def predict_order(inputs):
402
+
403
+ try:
404
+ # Create a pandas DataFrame from the input dictionary
405
+ input_df = pd.DataFrame(inputs, index=[0])
406
+
407
+ # Normalize the numerical features
408
+ input_df[NUMERIC_COLUMNS] = scaler.transform(input_df[NUMERIC_COLUMNS])
409
+
410
+ # Make a prediction
411
+ predictions = classifier.predict(input_fn=lambda: input_fn(input_df, labels=None, training=False))
412
+
413
+ # Get the predicted class and probability
414
+ for pred_dict in predictions:
415
+ class_id = pred_dict['class_ids'][0]
416
+ probability = pred_dict['probabilities'][class_id]
417
+ # Get the class name from the label encoder
418
+ class_name = le.inverse_transform([class_id])[0]
419
+ print('Order is "{}" ({:.1f}%)'.format(class_name, 100 * probability))
420
+ return class_name
421
+ except Exception as e:
422
+ print(f"An error occurred: {e}")
423
+ return None
424
+
425
+ def ode2(A0, B0, C0, temp, Ea, A_factor, is_reversible, order):
426
+ y0 = [A0, B0, C0]
427
+
428
+ k = compute_k(temp, Ea, A_factor)
429
+ k_1 = k * 0.7
430
+
431
+ t_span = (0, 8)
432
+ t_eval = np.linspace(0, 8, 11)
433
+
434
+ if order == 'zero':
435
+ solution = solve_ivp(zero, t_span, y0, args=(k,) ,t_eval=t_eval)
436
+ elif is_reversible == 0 and order == 'first':
437
+ solution = solve_ivp(first, t_span, y0, args=(k,) ,t_eval=t_eval)
438
+ elif is_reversible == 1 and order == 'first':
439
+ solution = solve_ivp(reversible_first, t_span, y0, args=(k, k_1) ,t_eval=t_eval)
440
+ elif is_reversible == 0 and order == 'second':
441
+ solution = solve_ivp(second1, t_span, y0, args=(k,) ,t_eval=t_eval)
442
+ elif is_reversible == 1 and order == 'second':
443
+ solution = solve_ivp(reversible_second1, t_span, y0, args=(k, k_1) ,t_eval=t_eval)
444
+ elif is_reversible == 0 and order == 'third':
445
+ solution = solve_ivp(third2, t_span, y0, args=(k,) ,t_eval=t_eval)
446
+ elif is_reversible == 1 and order == 'third':
447
+ solution = solve_ivp(reversible_third2, t_span, y0, args=(k, k_1) ,t_eval=t_eval)
448
+
449
+ return solution.t, solution.y[0], solution.y[1], solution.y[2], k, k_1
450
+
451
+ """## gradio"""
452
+
453
+ !pip install gradio
454
+
455
+ import gradio as gr
456
+ import pandas as pd
457
+ import numpy as np
458
+ import matplotlib.pyplot as plt
459
+
460
+ def run_simulation_and_plot(temp, Ea, A_factor, pH, pressure, is_reversible, structure, catalyst, A0, B0, C0):
461
+ #Data Preparation for Predictio
462
+ # Simullatqae the reaction using ode1 to get concentrations over time for prediction features
463
+ time_pred, A_pred, B_pred, C_pred, k_pred, k_1_pred, is_reversible_simulated, order_simulated = ode1(A0, B0, C0, temp, Ea, A_factor)
464
+
465
+ # Create a dictionary with all the necessary inputs for the model
466
+ inputs = {
467
+ 'temp': temp,
468
+ 'pH': pH,
469
+ 'Ea': Ea,
470
+ 'A_factor': A_factor,
471
+ 'pressure': pressure,
472
+ 'log_pressure': np.log(pressure),
473
+ 'weight': 150, # Using a placeholder value as it's not a user input
474
+ 'structure': structure,
475
+ 'catalyst': catalyst,
476
+ 'is_reversible': int(is_reversible),
477
+ 'k': k_pred, # Use simulated k
478
+ 'k_1': k_1_pred, # Use simulated k_1
479
+ 'A0': A_pred[0], 'A1': A_pred[1], 'A2': A_pred[2], 'A3': A_pred[3], 'A4': A_pred[4],
480
+ 'A5': A_pred[5], 'A6': A_pred[6], 'A7': A_pred[7], 'A8': A_pred[8], 'A9': A_pred[9], 'A10': A_pred[10],
481
+ 'B0': B_pred[0], 'B1': B_pred[1], 'B2': B_pred[2], 'B3': B_pred[3], 'B4': B_pred[4],
482
+ 'B5': B_pred[5], 'B6': B_pred[6], 'B7': B_pred[7], 'B8': B_pred[8], 'B9': B_pred[9], 'B10': B_pred[10],
483
+ 'C0': C_pred[0], 'C1': C_pred[1], 'C2': C_pred[2], 'C3': C_pred[3], 'C4': C_pred[4],
484
+ 'C5': C_pred[5], 'C6': C_pred[6], 'C7': C_pred[7], 'C8': C_pred[8], 'C9': C_pred[9], 'C10': C_pred[10]
485
+ }
486
+
487
+ # --- 2. Prediction ---
488
+ predicted_order = predict_order(inputs)
489
+
490
+ # --- 3. Simulation with ode2 and Predicted Order ---
491
+ # Use ode2 for the final simulation and plotting
492
+ time_sim, A_sim, B_sim, C_sim, k_sim, k_1_sim = ode2(A0, B0, C0, temp, Ea, A_factor, int(is_reversible), predicted_order)
493
+
494
+
495
+ # --- 4. Plotting ---
496
+ plt.figure()
497
+ plt.plot(time_sim, A_sim, label='A')
498
+ plt.plot(time_sim, B_sim, label='B')
499
+ plt.plot(time_sim, C_sim, label='C')
500
+ plt.xlabel('Time')
501
+ plt.ylabel('Concentration')
502
+ plt.title(f'Concentration vs. Time (Predicted Order: {predicted_order})')
503
+ plt.legend()
504
+ plt.grid(True)
505
+
506
+
507
+ return predicted_order, plt
508
+
509
+ # --- 5. Gradio Interface ---
510
+ with gr.Blocks() as iface:
511
+ gr.Markdown("# Chemical Reaction Order Prediction and Simulation")
512
+ gr.Markdown("Use the sliders and options to see the predicted reaction order and a plot of the concentrations over time.")
513
+ with gr.Row():
514
+ with gr.Column():
515
+ gr.Markdown("### Reaction Conditions")
516
+ temp = gr.Slider(270, 280, value=277, label="Temperature (K)")
517
+ Ea = gr.Slider(90, 100, value=93, label="Activation Energy (Ea, kJ/mol)")
518
+ A_factor = gr.Slider(2e16, 5e17, value=4.2e17, label="Pre-exponential Factor (A_factor)")
519
+ pH = gr.Slider(1.0, 14.0, value=6.5, label="pH")
520
+ pressure = gr.Slider(0.5, 5.0, value=3.0, label="Pressure")
521
+ is_reversible = gr.Checkbox(label="Is Reversible?")
522
+ structure = gr.Dropdown(['Linear', 'Ring', 'Branched', 'Unknown'], label="Structure")
523
+ catalyst = gr.Dropdown(['None', 'Enzyme', 'Acid', 'Base'], label="Catalyst")
524
+ with gr.Column():
525
+ gr.Markdown("### Initial Concentrations")
526
+ A0 = gr.Slider(0.0, 10.0, value=5.0, label="A0")
527
+ B0 = gr.Slider(0.0, 10.0, value=2.0, label="B0")
528
+ C0 = gr.Slider(0.0, 10.0, value=1.0, label="C0")
529
+
530
+ with gr.Row():
531
+ predict_button = gr.Button("Predict and Plot")
532
+ with gr.Row():
533
+ with gr.Column():
534
+ order_output = gr.Textbox(label="Predicted Order")
535
+ with gr.Column():
536
+ plot_output = gr.Plot()
537
+
538
+ predict_button.click(
539
+ fn=run_simulation_and_plot,
540
+ inputs=[temp, Ea, A_factor, pH, pressure, is_reversible, structure, catalyst, A0, B0, C0],
541
+ outputs=[order_output, plot_output]
542
+ )
543
+
544
+
545
+ iface.launch()
546
+
547
+ !gradio deploy
548
+
549
+ """## Streamlit Stuff"""
550
+
551
+ !pip install -q streamlit
552
+
553
+ import streamlit as st
554
+ import pandas as pd
555
+ import numpy as np
556
+ import matplotlib.pyplot as plt
557
+
558
+ # Assuming the functions compute_k, ode1, ode2, predict_order, and the classifier, scaler, and le objects are already defined and available in the notebook's global scope from previous cells.
559
+
560
+ st.set_page_config(layout="wide", page_title="Chemical Reaction Simulator") # Set page layout to wide and add a page title
561
+
562
+ st.title("🧪 Chemical Reaction Order Prediction and Simulation ✨")
563
+ st.markdown("Adjust the parameters below to predict the reaction order and visualize the concentration changes over time. 👇")
564
+
565
+ # Use columns for a better layout of inputs
566
+ col1, col2 = st.columns(2)
567
+
568
+ with col1:
569
+ st.header("⚙️ Reaction Conditions")
570
+ temp = st.slider("Temperature (K) 🌡️", 270.0, 280.0, value=277.0)
571
+ Ea = st.slider("Activation Energy (Ea, kJ/mol) 🔥", 90.0, 100.0, value=93.0)
572
+ A_factor = st.slider("Pre-exponential Factor (A_factor) 📈", 2e16, 5e17, value=4.2e17, format="%e") # Use scientific notation format
573
+ pH = st.slider("pH 🧪", 1.0, 14.0, value=6.5)
574
+ pressure = st.slider("Pressure 🌫️", 0.5, 5.0, value=3.0)
575
+ is_reversible = st.checkbox("Is Reversible? 🔄", value=False)
576
+ structure = st.selectbox("Structure ⚛️", ['Linear', 'Ring', 'Branched', 'Unknown'], index=1)
577
+ catalyst = st.selectbox("Catalyst ✨", ['None', 'Enzyme', 'Acid', 'Base'], index=2)
578
+
579
+ with col2:
580
+ st.header("📈 Initial Concentrations")
581
+ A0 = st.slider("Initial Concentration of A (A₀)", 0.0, 10.0, value=5.0)
582
+ B0 = st.slider("Initial Concentration of B (B₀)", 0.0, 10.0, value=2.0)
583
+ C0 = st.slider("Initial Concentration of C (C₀)", 0.0, 10.0, value=1.0)
584
+
585
+ st.markdown("---") # Add a horizontal rule for separation
586
+
587
+ if st.button("🚀 Predict and Plot Reaction"):
588
+ # Data Preparation for Prediction
589
+ # Simulate the reaction using ode1 to get concentrations over time for prediction features
590
+ time_pred, A_pred, B_pred, C_pred, k_pred, k_1_pred, is_reversible_simulated, order_simulated = ode1(A0, B0, C0, temp, Ea, A_factor)
591
+
592
+ # Create a dictionary with all the necessary inputs for the model
593
+ inputs = {
594
+ 'temp': temp,
595
+ 'pH': pH,
596
+ 'Ea': Ea,
597
+ 'A_factor': A_factor,
598
+ 'pressure': pressure,
599
+ 'log_pressure': np.log(pressure),
600
+ 'weight': 150, # Using a placeholder value as it's not a user input
601
+ 'structure': structure,
602
+ 'catalyst': catalyst,
603
+ 'is_reversible': int(is_reversible),
604
+ 'k': k_pred, # Use simulated k
605
+ 'k_1': k_1_pred, # Use simulated k_1
606
+ 'A0': A_pred[0], 'A1': A_pred[1], 'A2': A_pred[2], 'A3': A_pred[3], 'A4': A_pred[4],
607
+ 'A5': A_pred[5], 'A6': A_pred[6], 'A7': A_pred[7], 'A8': A_pred[8], 'A9': A_pred[9], 'A10': A_pred[10],
608
+ 'B0': B_pred[0], 'B1': B_pred[1], 'B2': B_pred[2], 'B3': B_pred[3], 'B4': B_pred[4],
609
+ 'B5': B_pred[5], 'B6': B_pred[6], 'B7': B_pred[7], 'B8': B_pred[8], 'B9': B_pred[9], 'B10': B_pred[10],
610
+ 'C0': C_pred[0], 'C1': C_pred[1], 'C2': C_pred[2], 'C3': C_pred[3], 'C4': C_pred[4],
611
+ 'C5': C_pred[5], 'C6': C_pred[6], 'C7': C_pred[7], 'C8': C_pred[8], 'C9': C_pred[9], 'C10': C_pred[10]
612
+ }
613
+
614
+ # --- 2. Prediction ---
615
+ with st.spinner('Predicting reaction order...'):
616
+ predicted_order = predict_order(inputs)
617
+ st.success(f"✅ Predicted Order: **{predicted_order}**")
618
+
619
+ # --- 3. Simulation with ode2 and Predicted Order ---
620
+ with st.spinner('Simulating reaction...'):
621
+ time_sim, A_sim, B_sim, C_sim, k_sim, k_1_sim = ode2(A0, B0, C0, temp, Ea, A_factor, int(is_reversible), predicted_order)
622
+
623
+ # --- 4. Plotting ---
624
+ st.header("📊 Concentration vs. Time Plot")
625
+ fig, ax = plt.subplots()
626
+ ax.plot(time_sim, A_sim, label='A', marker='o') # Add markers to plot points
627
+ ax.plot(time_sim, B_sim, label='B', marker='x')
628
+ ax.plot(time_sim, C_sim, label='C', marker='s')
629
+ ax.set_xlabel('Time')
630
+ ax.set_ylabel('Concentration')
631
+ ax.set_title(f'Concentration vs. Time (Predicted Order: {predicted_order})')
632
+ ax.legend()
633
+ ax.grid(True)
634
+
635
+ st.pyplot(fig)
636
+
637
+ st.markdown("---")
638
+ st.markdown("App created with ❤️ using Streamlit")
639
+
640
+
641
+
642
+ !npm install -g localtunnel
643
+
644
+ """Main code for Steamlit pipeline
645
+
646
+ first copy this code and then create a file named app.py and save it
647
+ """
648
+
649
+ # import numpy as np
650
+ # import matplotlib.pyplot as plt
651
+ # import pandas as pd
652
+ # from scipy.integrate import solve_ivp
653
+ # import random
654
+ # import tensorflow as tf
655
+
656
+ # def compute_k(temp, Ea, A_factor):
657
+ # R = 8.314
658
+ # Ea_J = Ea * 1000 # Convert Ea from kJ/mol to J/mol
659
+ # k = A_factor * np.exp(-Ea_J / (R * temp))
660
+ # return k
661
+
662
+ # def zero(t, y, k):
663
+ # A, B, C = y
664
+ # dA_dt = -k
665
+ # dB_dt = 0
666
+ # dC_dt = k
667
+ # return [dA_dt, dB_dt, dC_dt]
668
+
669
+ # def first(t, y, k):
670
+ # A, B, C = y
671
+ # dA_dt = -k * A
672
+ # dB_dt = 0
673
+ # dC_dt = +k * A
674
+ # return [dA_dt, dB_dt, dC_dt]
675
+
676
+ # def decay_first(t, y, k):
677
+ # A, B, C = y
678
+ # dA_dt = -k * A
679
+ # dB_dt = 0
680
+ # dC_dt = 0
681
+ # return [dA_dt, dB_dt, dC_dt]
682
+
683
+ # def reversible_first(t, y, k, k_1):
684
+ # A, B, C = y
685
+ # dA_dt = -k * A + k_1 * C
686
+ # dB_dt = 0
687
+ # dC_dt = k * A - k_1 * C
688
+ # return [dA_dt, dB_dt, dC_dt]
689
+
690
+ # def second1(t, y, k):
691
+ # A, B, C = y
692
+ # dA_dt = -k * A * B
693
+ # dB_dt = -k * A * B
694
+ # dC_dt = +k * A * B
695
+ # return [dA_dt, dB_dt, dC_dt]
696
+
697
+ # def second2(t, y, k):
698
+ # A, B, C = y
699
+ # dA_dt = -2 * k * A**2
700
+ # dB_dt = 0
701
+ # dC_dt = +k * A**2
702
+ # return [dA_dt, dB_dt, dC_dt]
703
+
704
+ # def reversible_second1(t, y, k, k_1):
705
+ # A, B, C = y
706
+ # dA_dt = -k * A * B + k_1 * C
707
+ # dB_dt = -k * A * B + k_1 * C
708
+ # dC_dt = +k * A * B - k_1 * C
709
+ # return [dA_dt, dB_dt, dC_dt]
710
+
711
+ # def reversible_second2(t, y, k, k_1):
712
+ # A, B, C = y
713
+ # dA_dt = -2 * k * A**2 + 2 * k_1 * C
714
+ # dB_dt = 0
715
+ # dC_dt = +k * A**2 - k_1 * C
716
+ # return [dA_dt, dB_dt, dC_dt]
717
+
718
+ # def third1(t, y, k):
719
+ # A, B, C = y
720
+ # dA_dt = -3 * k * A**3
721
+ # dB_dt = 0
722
+ # dC_dt = +k * A**3
723
+ # return [dA_dt, dB_dt, dC_dt]
724
+
725
+ # def third2(t, y, k):
726
+ # A, B, C = y
727
+ # dA_dt = -2 * k * A**2 * B
728
+ # dB_dt = -1 * k * A**2 * B
729
+ # dC_dt = +k * A**2 * B
730
+ # return [dA_dt, dB_dt, dC_dt]
731
+
732
+ # def reversible_third1(t, y, k, k_1):
733
+ # A, B, C = y
734
+ # dA_dt = -3 * k * A**3 + 3 * k_1 * C
735
+ # dB_dt = 0
736
+ # dC_dt = +k * A**3 - k_1 * C
737
+ # return [dA_dt, dB_dt, dC_dt]
738
+
739
+ # def reversible_third2(t, y, k, k_1):
740
+ # A, B, C = y
741
+ # dA_dt = -2 * k * A**2 * B + 2 * k_1 * C
742
+ # dB_dt = -1 * k * A**2 * B + 1 * k_1 * C
743
+ # dC_dt = +k * A**2 * B - k_1 * C
744
+ # return [dA_dt, dB_dt, dC_dt]
745
+
746
+
747
+ # def ode1(A0, B0, C0, temp, Ea, A_factor):
748
+ # y0 = [A0, B0, C0]
749
+ # k = compute_k(temp, Ea, A_factor)
750
+ # k_1 = k * random.uniform(0.5, 0.9)
751
+
752
+ # t_span = (0, 8) # From time 0 to 10 seconds
753
+ # t_eval = np.linspace(0, 8, 11) # 11 points where you want the solution
754
+
755
+ # num = random.randint(0, 11) # For choosing between first or decay if not reversible
756
+
757
+ # match num:
758
+ # case 0:
759
+ # func_name = zero
760
+ # is_reversible = 0
761
+ # order = 'zero'
762
+ # case 1:
763
+ # func_name = first
764
+ # is_reversible = 0
765
+ # order = 'first'
766
+ # case 2:
767
+ # func_name = decay_first
768
+ # is_reversible = 0
769
+ # order = 'first'
770
+ # case 3:
771
+ # func_name = reversible_first
772
+ # is_reversible = 1
773
+ # order = 'first'
774
+ # case 4:
775
+ # func_name = second1
776
+ # is_reversible = 0
777
+ # order = 'second'
778
+ # case 5:
779
+ # func_name = second2
780
+ # is_reversible = 0
781
+ # order = 'second'
782
+ # case 6:
783
+ # func_name = reversible_second1
784
+ # is_reversible = 1
785
+ # order = 'second'
786
+ # case 7:
787
+ # func_name = reversible_second2
788
+ # is_reversible = 1
789
+ # order = 'second'
790
+ # case 8:
791
+ # func_name = third1
792
+ # is_reversible = 0
793
+ # order = 'third'
794
+ # case 9:
795
+ # func_name = third2
796
+ # is_reversible = 0
797
+ # order = 'third'
798
+ # case 10:
799
+ # func_name = reversible_third1
800
+ # is_reversible = 1
801
+ # order = 'third'
802
+ # case 11:
803
+ # func_name = reversible_third2
804
+ # is_reversible = 1
805
+ # order = 'third'
806
+
807
+
808
+ # if is_reversible == 1:
809
+ # solution = solve_ivp(
810
+ # func_name,
811
+ # t_span,
812
+ # y0,
813
+ # args=(k, k_1),
814
+ # t_eval=t_eval
815
+ # )
816
+ # elif is_reversible == 0:
817
+ # solution = solve_ivp(
818
+ # func_name,
819
+ # t_span,
820
+ # y0,
821
+ # args=(k,),
822
+ # t_eval=t_eval
823
+ # )
824
+
825
+
826
+ # return solution.t, solution.y[0], solution.y[1], solution.y[2], k, k_1, is_reversible, order
827
+
828
+ # results = []
829
+
830
+ # counter = 0
831
+ # while counter < 6000:
832
+ # counter += 1
833
+
834
+ # A0 = round(random.uniform(1.0, 10.0), 2)
835
+ # B0 = round(random.uniform(0.0, 5.0), 2)
836
+ # C0 = round(random.uniform(0.0, 5.0), 2)
837
+ # temp = random.randint(270, 280)
838
+ # pH = round(random.uniform(1.0, 14.0), 2)
839
+ # Ea = random.randint(90, 100)
840
+ # A_factor = round(random.uniform(2e16, 5e17), 2)
841
+ # pressure = round(random.uniform(0.5, 5.0), 2)
842
+ # weight = round(random.uniform(20, 200), 1)
843
+ # structure = random.choice(['Linear', 'Ring', 'Branched', 'Unknown'])
844
+ # catalyst = random.choice(['None', 'Enzyme', 'Acid', 'Base'])
845
+ # time, A, B, C, k, k_1, is_reversible, order = ode1(A0, B0, C0, temp, Ea, A_factor)
846
+
847
+ # row = {
848
+ # 'order' : order,
849
+ # 'temp': temp,
850
+ # 'pH': pH,
851
+ # 'Ea': Ea,
852
+ # 'A_factor': A_factor,
853
+ # 'pressure': pressure,
854
+ # 'log_pressure' : np.log(pressure),
855
+ # 'weight': weight,
856
+ # 'structure': structure,
857
+ # 'catalyst': catalyst,
858
+ # 'is_reversible': is_reversible,
859
+ # 'k' : k,
860
+ # 'k_1' : k_1,
861
+ # 'A0': A[0], 'A1': A[1], 'A2': A[2], 'A3': A[3], 'A4': A[4],
862
+ # 'A5': A[5], 'A6': A[6], 'A7': A[7], 'A8': A[8], 'A9': A[9], 'A10': A[10],
863
+ # 'B0': B[0], 'B1': B[1], 'B2': B[2], 'B3': B[3], 'B4': B[4],
864
+ # 'B5': B[5], 'B6': B[6], 'B7': B[7], 'B8': B[8], 'B9': B[9], 'B10': B[10],
865
+ # 'C0': C[0], 'C1': C[1], 'C2': C[2], 'C3': C[3], 'C4': C[4],
866
+ # 'C5': C[5], 'C6': C[6], 'C7': C[7], 'C8': C[8], 'C9': C[9], 'C10': C[10]
867
+ # }
868
+ # results.append(row)
869
+
870
+ # df = pd.DataFrame(results)
871
+ # df_original = df.copy()
872
+ # # display(df)
873
+
874
+ # structure_map = {'Linear': 0, 'Ring': 1, 'Branched': 2, 'Unknown': 3}
875
+ # catalyst_map = {'None': 0, 'Enzyme': 1, 'Acid': 2, 'Base': 3}
876
+ # order_map = {'zero': 0, 'first': 1, 'second': 2, 'third' : 3}
877
+ # df['structure'] = df['structure'].map(structure_map)
878
+ # df['catalyst'] = df['catalyst'].map(catalyst_map)
879
+ # df['order'] = df['order'].map(order_map)
880
+ # # display(df)
881
+
882
+
883
+ # csv_columns = ['temp', 'pH', 'Ea', 'A_factor', 'pressure', 'log_pressure', 'weight', 'structure', 'catalyst', 'is_reversible', 'k', 'k_1']
884
+ # classes = ['First_Order','Second_Order','Third_Order']
885
+
886
+ # train_path = './chem_data_train.csv'
887
+ # test_path = './chem_data_train.csv'
888
+
889
+ # train = pd.read_csv(train_path)
890
+ # test = pd.read_csv(test_path)
891
+
892
+ # # display(train.head())
893
+
894
+ # if 'order' in train.columns:
895
+ # train_y = train.pop('order')
896
+ # if 'order' in test.columns:
897
+ # test_y = test.pop('order')
898
+
899
+ # # Fill missing values in the 'catalyst' column
900
+ # train['catalyst'] = train['catalyst'].fillna('None') #NaN values arenot accepted by classifier thats why convert every Nan values to none
901
+ # test['catalyst'] = test['catalyst'].fillna('None')
902
+
903
+
904
+ # # display(train.head()) #the species column is now gone
905
+
906
+ # # Define categorical and numerical feature columns
907
+ # CATEGORICAL_COLUMNS = ['structure', 'catalyst'] #columns that have strings
908
+ # NUMERIC_COLUMNS = ['temp', 'pH', 'Ea', 'A_factor', 'pressure', 'log_pressure', 'weight',
909
+ # 'is_reversible', 'k', 'k_1', 'A0', 'A1', 'A2', 'A3', 'A4', 'A5', 'A6', 'A7', 'A8', 'A9', 'A10',
910
+ # 'B0', 'B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B9', 'B10',
911
+ # 'C0', 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9', 'C10'] #columns that have numerical values
912
+
913
+ # feature_columns = []
914
+ # for feature_name in CATEGORICAL_COLUMNS:
915
+ # vocabulary = train[feature_name].unique() #Assining each string a numerical uinque value because our dumb ahh model canot understand english
916
+ # cat_column = tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary)
917
+ # indicator_column = tf.feature_column.indicator_column(cat_column) #it creates binary coolumns that will be mapped in to feature columns and it will be steamlined to our DNN model
918
+ # feature_columns.append(indicator_column)
919
+
920
+ # for feature_name in NUMERIC_COLUMNS:
921
+ # feature_columns.append(tf.feature_column.numeric_column(feature_name, dtype=tf.float32))
922
+
923
+ # # print(feature_columns)
924
+
925
+ # import logging
926
+ # tf.get_logger().setLevel(logging.INFO)
927
+
928
+ # #setting up input function
929
+
930
+ # def input_fn(features,labels,training=True,batch_size=500):
931
+ # #convert the inputs to a dataset
932
+ # dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels)) #this cnonverts the dataset into tensorflow object
933
+
934
+ # if training:
935
+ # dataset = dataset.shuffle(1000).repeat()
936
+
937
+ # return dataset.batch(batch_size)
938
+
939
+ # from sklearn.preprocessing import StandardScaler
940
+
941
+ # # Normalize the numerical features in the training data
942
+ # scaler = StandardScaler()
943
+ # train_normalized = train.copy()
944
+ # train_normalized[NUMERIC_COLUMNS] = scaler.fit_transform(train[NUMERIC_COLUMNS])
945
+
946
+ # test_normalized = test.copy()
947
+ # test_normalized[NUMERIC_COLUMNS] = scaler.transform(test[NUMERIC_COLUMNS])
948
+
949
+ # from sklearn.preprocessing import LabelEncoder
950
+
951
+ # # Convert the 'order' labels to numerical values
952
+ # le = LabelEncoder()
953
+ # train_y_encoded = le.fit_transform(train_y) #we used sckit label encoder to encode the values
954
+
955
+ # classifier = tf.estimator.DNNClassifier(
956
+ # feature_columns=feature_columns,
957
+ # hidden_units=[50, 40],
958
+ # n_classes=4, # We have 4 classes: zero, first, second, third
959
+ # optimizer=tf.keras.optimizers.legacy.RMSprop(learning_rate=0.001))
960
+
961
+ # classifier.train(
962
+ # input_fn=lambda: input_fn(train_normalized, train_y_encoded, training=True),
963
+ # steps=300
964
+ # )
965
+
966
+ # test_y_encoded = le.fit_transform(test_y) #we used sckit label encoder to encode the values better than 1 2 3 4 5 blah blah
967
+
968
+ # classifier.evaluate(input_fn=lambda: input_fn(test_normalized,test_y_encoded,training=False))
969
+
970
+
971
+ # def predict_order(inputs):
972
+
973
+ # try:
974
+ # # Create a pandas DataFrame from the input dictionary
975
+ # input_df = pd.DataFrame(inputs, index=[0])
976
+
977
+ # # Normalize the numerical features
978
+ # input_df[NUMERIC_COLUMNS] = scaler.transform(input_df[NUMERIC_COLUMNS])
979
+
980
+ # # Make a prediction
981
+ # predictions = classifier.predict(input_fn=lambda: input_fn(input_df, labels=None, training=False))
982
+
983
+ # # Get the predicted class and probability
984
+ # for pred_dict in predictions:
985
+ # class_id = pred_dict['class_ids'][0]
986
+ # probability = pred_dict['probabilities'][class_id]
987
+ # # Get the class name from the label encoder
988
+ # class_name = le.inverse_transform([class_id])[0]
989
+ # print('Order is "{}" ({:.1f}%)'.format(class_name, 100 * probability))
990
+ # return class_name
991
+ # except Exception as e:
992
+ # print(f"An error occurred: {e}")
993
+ # return None
994
+
995
+ # def ode2(A0, B0, C0, temp, Ea, A_factor, is_reversible, order):
996
+ # y0 = [A0, B0, C0]
997
+
998
+ # k = compute_k(temp, Ea, A_factor)
999
+ # k_1 = k * 0.7
1000
+
1001
+ # t_span = (0, 8)
1002
+ # t_eval = np.linspace(0, 8, 11)
1003
+
1004
+ # if order == 'zero':
1005
+ # solution = solve_ivp(zero, t_span, y0, args=(k,) ,t_eval=t_eval)
1006
+ # elif is_reversible == 0 and order == 'first':
1007
+ # solution = solve_ivp(first, t_span, y0, args=(k,) ,t_eval=t_eval)
1008
+ # elif is_reversible == 1 and order == 'first':
1009
+ # solution = solve_ivp(reversible_first, t_span, y0, args=(k, k_1) ,t_eval=t_eval)
1010
+ # elif is_reversible == 0 and order == 'second':
1011
+ # solution = solve_ivp(second1, t_span, y0, args=(k,) ,t_eval=t_eval)
1012
+ # elif is_reversible == 1 and order == 'second':
1013
+ # solution = solve_ivp(reversible_second1, t_span, y0, args=(k, k_1) ,t_eval=t_eval)
1014
+ # elif is_reversible == 0 and order == 'third':
1015
+ # solution = solve_ivp(third2, t_span, y0, args=(k,) ,t_eval=t_eval)
1016
+ # elif is_reversible == 1 and order == 'third':
1017
+ # solution = solve_ivp(reversible_third2, t_span, y0, args=(k, k_1) ,t_eval=t_eval)
1018
+
1019
+ # return solution.t, solution.y[0], solution.y[1], solution.y[2], k, k_1
1020
+
1021
+
1022
+
1023
+ # import streamlit as st
1024
+ # import pandas as pd
1025
+ # import numpy as np
1026
+ # import matplotlib.pyplot as plt
1027
+
1028
+ # # Assuming the functions compute_k, ode1, ode2, predict_order, and the classifier, scaler, and le objects are already defined and available in the notebook's global scope from previous cells.
1029
+
1030
+ # st.set_page_config(layout="wide", page_title="Chemical Reaction Simulator") # Set page layout to wide and add a page title
1031
+
1032
+ # st.title("🧪 Project E-11")
1033
+ # st.markdown("🧪 Chemical Reaction Order Prediction and Simulation ✨")
1034
+ # st.markdown("Adjust the parameters below to predict the reaction order and visualize the concentration changes over time. 👇")
1035
+
1036
+ # # Use columns for a better layout of inputs
1037
+ # col1, col2 = st.columns(2)
1038
+
1039
+ # with col1:
1040
+ # st.header("⚙️ Reaction Conditions")
1041
+ # temp = st.slider("Temperature (K) 🌡️", 270.0, 280.0, value=277.0)
1042
+ # Ea = st.slider("Activation Energy (Ea, kJ/mol) 🔥", 90.0, 100.0, value=93.0)
1043
+ # A_factor = st.slider("Pre-exponential Factor (A_factor) 📈", 2e16, 5e17, value=4.2e17, format="%e") # Use scientific notation format
1044
+ # pH = st.slider("pH 🧪", 1.0, 14.0, value=6.5)
1045
+ # pressure = st.slider("Pressure 🌫️", 0.5, 5.0, value=3.0)
1046
+ # is_reversible = st.checkbox("Is Reversible? 🔄", value=False)
1047
+ # structure = st.selectbox("Structure ⚛️", ['Linear', 'Ring', 'Branched', 'Unknown'], index=1)
1048
+ # catalyst = st.selectbox("Catalyst ✨", ['None', 'Enzyme', 'Acid', 'Base'], index=2)
1049
+
1050
+ # with col2:
1051
+ # st.header("📈 Initial Concentrations")
1052
+ # A0 = st.slider("Initial Concentration of A (A₀)", 0.0, 10.0, value=5.0)
1053
+ # B0 = st.slider("Initial Concentration of B (B₀)", 0.0, 10.0, value=2.0)
1054
+ # C0 = st.slider("Initial Concentration of C (C₀)", 0.0, 10.0, value=1.0)
1055
+
1056
+ # st.markdown("---") # Add a horizontal rule for separation
1057
+
1058
+ # if st.button("🚀 Predict and Plot Reaction"):
1059
+ # # Data Preparation for Prediction
1060
+ # # Simulate the reaction using ode1 to get concentrations over time for prediction features
1061
+ # time_pred, A_pred, B_pred, C_pred, k_pred, k_1_pred, is_reversible_simulated, order_simulated = ode1(A0, B0, C0, temp, Ea, A_factor)
1062
+
1063
+ # # Create a dictionary with all the necessary inputs for the model
1064
+ # inputs = {
1065
+ # 'temp': temp,
1066
+ # 'pH': pH,
1067
+ # 'Ea': Ea,
1068
+ # 'A_factor': A_factor,
1069
+ # 'pressure': pressure,
1070
+ # 'log_pressure': np.log(pressure),
1071
+ # 'weight': 150, # Using a placeholder value as it's not a user input
1072
+ # 'structure': structure,
1073
+ # 'catalyst': catalyst,
1074
+ # 'is_reversible': int(is_reversible),
1075
+ # 'k': k_pred, # Use simulated k
1076
+ # 'k_1': k_1_pred, # Use simulated k_1
1077
+ # 'A0': A_pred[0], 'A1': A_pred[1], 'A2': A_pred[2], 'A3': A_pred[3], 'A4': A_pred[4],
1078
+ # 'A5': A_pred[5], 'A6': A_pred[6], 'A7': A_pred[7], 'A8': A_pred[8], 'A9': A_pred[9], 'A10': A_pred[10],
1079
+ # 'B0': B_pred[0], 'B1': B_pred[1], 'B2': B_pred[2], 'B3': B_pred[3], 'B4': B_pred[4],
1080
+ # 'B5': B_pred[5], 'B6': B_pred[6], 'B7': B_pred[7], 'B8': B_pred[8], 'B9': B_pred[9], 'B10': B_pred[10],
1081
+ # 'C0': C_pred[0], 'C1': C_pred[1], 'C2': C_pred[2], 'C3': C_pred[3], 'C4': C_pred[4],
1082
+ # 'C5': C_pred[5], 'C6': C_pred[6], 'C7': C_pred[7], 'C8': C_pred[8], 'C9': C_pred[9], 'C10': C_pred[10]
1083
+ # }
1084
+
1085
+ # # --- 2. Prediction ---
1086
+ # with st.spinner('Predicting reaction order...'):
1087
+ # predicted_order = predict_order(inputs)
1088
+ # st.success(f"✅ Predicted Order: **{predicted_order}**")
1089
+
1090
+ # # --- 3. Simulation with ode2 and Predicted Order ---
1091
+ # with st.spinner('Simulating reaction...'):
1092
+ # time_sim, A_sim, B_sim, C_sim, k_sim, k_1_sim = ode2(A0, B0, C0, temp, Ea, A_factor, int(is_reversible), predicted_order)
1093
+
1094
+ # # --- 4. Plotting ---
1095
+ # st.header("📊 Concentration vs. Time Plot")
1096
+ # fig, ax = plt.subplots()
1097
+ # ax.plot(time_sim, A_sim, label='A', marker='o') # Add markers to plot points
1098
+ # ax.plot(time_sim, B_sim, label='B', marker='x')
1099
+ # ax.plot(time_sim, C_sim, label='C', marker='s')
1100
+ # ax.set_xlabel('Time')
1101
+ # ax.set_ylabel('Concentration')
1102
+ # ax.set_title(f'Concentration vs. Time (Predicted Order: {predicted_order})')
1103
+ # ax.legend()
1104
+ # ax.grid(True)
1105
+
1106
+ # st.pyplot(fig)
1107
+
1108
+ # st.markdown("---")
1109
+ # st.markdown("App created with ❤️ by Mujtaba , Muzammil , Taha and Ali Zain.")
1110
+
1111
+ # !streamlit run /content/app.py &>/content/logs.txt & #this starts the loca server
1112
+
1113
+ # !npx localtunnel --port 8501 #the tunnel
1114
+
1115
+ # get_ipython().run_line_magic('shell', 'curl https://loca.lt/mytunnelpassword') #getting ur home ip adress :cold:
1116
+
1117
+ # %%writefile requirements.txt
1118
+ # gradio
1119
+ # pandas
1120
+ # numpy
1121
+ # matplotlib
1122
+ # scipy
1123
+ # tensorflow==2.15
1124
+ # scikit-learn
chem_data_test.csv ADDED
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chem_data_train.csv ADDED
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requirements.txt ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ streamlit
2
+ pandas
3
+ numpy
4
+ matplotlib
5
+ scipy
6
+ tensorflow==2.15
7
+ scikit-learn
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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+ {"Series":"IV", "X":8.0, "Y":6.89}
49
+ ]
sample_data/california_housing_test.csv ADDED
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sample_data/california_housing_train.csv ADDED
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sample_data/mnist_test.csv ADDED
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