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  1. .config/.last_opt_in_prompt.yaml +1 -0
  2. .config/.last_survey_prompt.yaml +1 -0
  3. .config/.last_update_check.json +1 -0
  4. .config/active_config +1 -0
  5. .config/config_sentinel +0 -0
  6. .config/configurations/config_default +6 -0
  7. .config/default_configs.db +0 -0
  8. .config/gce +1 -0
  9. .config/hidden_gcloud_config_universe_descriptor_data_cache_configs.db +0 -0
  10. .config/logs/2025.09.03/13.35.10.219821.log +765 -0
  11. .config/logs/2025.09.03/13.35.31.467623.log +5 -0
  12. .config/logs/2025.09.03/13.35.39.928561.log +153 -0
  13. .config/logs/2025.09.03/13.35.41.650830.log +5 -0
  14. .config/logs/2025.09.03/13.35.50.564372.log +8 -0
  15. .config/logs/2025.09.03/13.35.51.306217.log +8 -0
  16. .gitattributes +2 -0
  17. README.md +2 -8
  18. app.py +685 -0
  19. requirements.txt +7 -0
  20. sample_data/README.md +19 -0
  21. sample_data/anscombe.json +49 -0
  22. sample_data/california_housing_test.csv +0 -0
  23. sample_data/california_housing_train.csv +0 -0
  24. sample_data/mnist_test.csv +3 -0
  25. sample_data/mnist_train_small.csv +3 -0
.config/.last_opt_in_prompt.yaml ADDED
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.config/.last_survey_prompt.yaml ADDED
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+ last_prompt_time: 1756906539.1872025
.config/.last_update_check.json ADDED
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+ {"last_update_check_time": 1756906541.050925, "last_update_check_revision": 20250829143545, "notifications": [], "last_nag_times": {}}
.config/active_config ADDED
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+ default
.config/config_sentinel ADDED
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.config/configurations/config_default ADDED
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+ [component_manager]
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+ disable_update_check = true
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+
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+ [compute]
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+ gce_metadata_read_timeout_sec = 0
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.config/default_configs.db ADDED
Binary file (12.3 kB). View file
 
.config/gce ADDED
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.config/logs/2025.09.03/13.35.10.219821.log ADDED
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+ 2025-09-03 13:35:23,072 INFO ___FILE_ONLY___ For the latest full release notes, please visit:
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482
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531
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534
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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
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547
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548
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549
+ 2025-09-03 13:35:24,561 INFO ___FILE_ONLY___ ═
550
+ 2025-09-03 13:35:24,561 INFO ___FILE_ONLY___ ╝
551
+
552
+ 2025-09-03 13:35:24,571 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
553
+
554
+ 2025-09-03 13:35:24,571 INFO ___FILE_ONLY___ ╠═ Installing: BigQuery Command Line Tool (Platform Spec... ═╣
555
+
556
+ 2025-09-03 13:35:24,571 INFO ___FILE_ONLY___ ╚
557
+ 2025-09-03 13:35:24,572 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
558
+ 2025-09-03 13:35:24,572 INFO ___FILE_ONLY___ ╝
559
+
560
+ 2025-09-03 13:35:24,576 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
561
+
562
+ 2025-09-03 13:35:24,576 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.12 ═╣
563
+
564
+ 2025-09-03 13:35:24,577 INFO ___FILE_ONLY___ ╚
565
+ 2025-09-03 13:35:24,579 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
566
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567
+
568
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569
+
570
+ 2025-09-03 13:35:24,581 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.12 (Platform Specific) ═╣
571
+
572
+ 2025-09-03 13:35:24,581 INFO ___FILE_ONLY___ ╚
573
+ 2025-09-03 13:35:26,703 INFO ___FILE_ONLY___ ═
574
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575
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576
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577
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579
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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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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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600
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601
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602
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603
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604
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605
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606
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607
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608
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609
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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
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616
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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
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623
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624
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625
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626
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627
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628
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629
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630
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631
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632
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633
+ 2025-09-03 13:35:29,432 INFO ___FILE_ONLY___ ╝
634
+
635
+ 2025-09-03 13:35:29,483 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
636
+
637
+ 2025-09-03 13:35:29,484 INFO ___FILE_ONLY___ ╠═ Installing: Cloud Storage Command Line Tool ═╣
638
+
639
+ 2025-09-03 13:35:29,484 INFO ___FILE_ONLY___ ╚
640
+ 2025-09-03 13:35:30,013 INFO ___FILE_ONLY___ ═
641
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642
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645
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646
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647
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648
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649
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650
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654
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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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663
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664
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665
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669
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670
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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
+ 2025-09-03 13:35:30,699 INFO ___FILE_ONLY___ ═
695
+ 2025-09-03 13:35:30,713 INFO ___FILE_ONLY___ ═
696
+ 2025-09-03 13:35:30,729 INFO ___FILE_ONLY___ ═
697
+ 2025-09-03 13:35:30,748 INFO ___FILE_ONLY___ ═
698
+ 2025-09-03 13:35:30,764 INFO ___FILE_ONLY___ ═
699
+ 2025-09-03 13:35:30,782 INFO ___FILE_ONLY___ ═
700
+ 2025-09-03 13:35:30,782 INFO ___FILE_ONLY___ ╝
701
+
702
+ 2025-09-03 13:35:30,813 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
703
+
704
+ 2025-09-03 13:35:30,813 INFO ___FILE_ONLY___ ╠═ Installing: Cloud Storage Command Line Tool (Platform... ═╣
705
+
706
+ 2025-09-03 13:35:30,813 INFO ___FILE_ONLY___ ╚
707
+ 2025-09-03 13:35:30,814 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
708
+ 2025-09-03 13:35:30,814 INFO ___FILE_ONLY___ ╝
709
+
710
+ 2025-09-03 13:35:30,818 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
711
+
712
+ 2025-09-03 13:35:30,818 INFO ___FILE_ONLY___ ╠═ Installing: Default set of gcloud commands ═╣
713
+
714
+ 2025-09-03 13:35:30,818 INFO ___FILE_ONLY___ ╚
715
+ 2025-09-03 13:35:30,821 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
716
+ 2025-09-03 13:35:30,821 INFO ___FILE_ONLY___ ╝
717
+
718
+ 2025-09-03 13:35:30,822 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
719
+
720
+ 2025-09-03 13:35:30,822 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CLI Core Libraries (Platform... ═╣
721
+
722
+ 2025-09-03 13:35:30,822 INFO ___FILE_ONLY___ ╚
723
+ 2025-09-03 13:35:30,823 INFO ___FILE_ONLY___ ══════════════════════════════
724
+ 2025-09-03 13:35:30,824 INFO ___FILE_ONLY___ ══════════════════════════════
725
+ 2025-09-03 13:35:30,824 INFO ___FILE_ONLY___ ╝
726
+
727
+ 2025-09-03 13:35:30,828 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
728
+
729
+ 2025-09-03 13:35:30,828 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool ═╣
730
+
731
+ 2025-09-03 13:35:30,828 INFO ___FILE_ONLY___ ╚
732
+ 2025-09-03 13:35:30,830 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
733
+ 2025-09-03 13:35:30,830 INFO ___FILE_ONLY___ ╝
734
+
735
+ 2025-09-03 13:35:30,832 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
736
+
737
+ 2025-09-03 13:35:30,832 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool (Platform S... ═╣
738
+
739
+ 2025-09-03 13:35:30,832 INFO ___FILE_ONLY___ ╚
740
+ 2025-09-03 13:35:30,868 INFO ___FILE_ONLY___ ══════════════════════════════
741
+ 2025-09-03 13:35:30,869 INFO ___FILE_ONLY___ ══════════════════════════════
742
+ 2025-09-03 13:35:30,869 INFO ___FILE_ONLY___ ╝
743
+
744
+ 2025-09-03 13:35:30,873 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
745
+
746
+ 2025-09-03 13:35:30,874 INFO ___FILE_ONLY___ ╠═ Installing: gcloud cli dependencies (Platform Specific) ═╣
747
+
748
+ 2025-09-03 13:35:30,874 INFO ___FILE_ONLY___ ╚
749
+ 2025-09-03 13:35:30,874 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
750
+ 2025-09-03 13:35:30,874 INFO ___FILE_ONLY___ ╝
751
+
752
+ 2025-09-03 13:35:30,879 DEBUG root Updating notification cache...
753
+ 2025-09-03 13:35:30,879 INFO ___FILE_ONLY___
754
+
755
+ 2025-09-03 13:35:30,881 INFO ___FILE_ONLY___ Performing post processing steps...
756
+ 2025-09-03 13:35:30,881 DEBUG root Executing command: ['/tools/google-cloud-sdk/bin/gcloud', 'components', 'post-process']
757
+ 2025-09-03 13:35:39,104 DEBUG ___FILE_ONLY___
758
+ 2025-09-03 13:35:39,104 DEBUG ___FILE_ONLY___
759
+ 2025-09-03 13:35:39,183 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
+ 2025-09-03 13:35:39,183 INFO ___FILE_ONLY___
761
+ Update done!
762
+
763
+
764
+ 2025-09-03 13:35:39,186 DEBUG root Chosen display Format:none
765
+ 2025-09-03 13:35:39,186 INFO root Display format: "none"
.config/logs/2025.09.03/13.35.31.467623.log ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ 2025-09-03 13:35:31,468 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2025-09-03 13:35:31,470 DEBUG root Loaded Command Group: ['gcloud', 'components', 'post_process']
3
+ 2025-09-03 13:35:31,472 DEBUG root Running [gcloud.components.post-process] with arguments: []
4
+ 2025-09-03 13:35:38,975 DEBUG root Chosen display Format:none
5
+ 2025-09-03 13:35:38,976 INFO root Display format: "none"
.config/logs/2025.09.03/13.35.39.928561.log ADDED
@@ -0,0 +1,153 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2025-09-03 13:35:39,929 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2025-09-03 13:35:39,931 DEBUG root Loaded Command Group: ['gcloud', 'components', 'update']
3
+ 2025-09-03 13:35:39,933 DEBUG root Running [gcloud.components.update] with arguments: [--quiet: "True", COMPONENT-IDS:8: "['gcloud', 'core', 'bq', 'gsutil', 'compute', 'preview', 'alpha', 'beta']"]
4
+ 2025-09-03 13:35:39,934 INFO ___FILE_ONLY___ Beginning update. This process may take several minutes.
5
+
6
+ 2025-09-03 13:35:39,942 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
7
+ 2025-09-03 13:35:40,028 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components-2.json HTTP/11" 200 233150
8
+ 2025-09-03 13:35:40,039 WARNING root Component [compute] no longer exists.
9
+ 2025-09-03 13:35:40,040 INFO ___FILE_ONLY___
10
+
11
+ 2025-09-03 13:35:40,041 INFO ___FILE_ONLY___
12
+ Your current Google Cloud CLI version is: 537.0.0
13
+
14
+ 2025-09-03 13:35:40,041 INFO ___FILE_ONLY___ Installing components from version: 537.0.0
15
+
16
+ 2025-09-03 13:35:40,041 INFO ___FILE_ONLY___
17
+
18
+ 2025-09-03 13:35:40,041 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
+ 2025-09-03 13:35:40,042 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
+ 2025-09-03 13:35:40,042 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
+ 2025-09-03 13:35:40,058 INFO ___FILE_ONLY___ ┌────────────────────────────────────────────────┐
22
+ 2025-09-03 13:35:40,058 INFO ___FILE_ONLY___
23
+
24
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.gitattributes CHANGED
@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
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+ sample_data/mnist_test.csv filter=lfs diff=lfs merge=lfs -text
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README.md CHANGED
@@ -1,12 +1,6 @@
1
  ---
2
- title: Movie
3
- emoji: 🏆
4
- colorFrom: gray
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- colorTo: indigo
6
  sdk: gradio
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  sdk_version: 5.44.1
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- app_file: app.py
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- pinned: false
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  ---
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-
12
- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
1
  ---
2
+ title: movie
3
+ app_file: app.py
 
 
4
  sdk: gradio
5
  sdk_version: 5.44.1
 
 
6
  ---
 
 
app.py ADDED
@@ -0,0 +1,685 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import pandas as pd
2
+ #from google.colab import drive
3
+ #drive.mount('/content/drive')
4
+ #%cd /content/drive/MyDrive/movie
5
+ df = pd.read_csv("TMDB_movie_dataset_v11.csv")
6
+ !pip install gensim
7
+ from collections import defaultdict
8
+ import numpy as np
9
+ import time
10
+ import gensim
11
+ from gensim.models.keyedvectors import KeyedVectors
12
+ from sklearn.decomposition import TruncatedSVD
13
+ import matplotlib.pyplot as plt
14
+ import pickle
15
+
16
+ import gradio as gr
17
+ !pip install cogworks-data
18
+
19
+ from cogworks_data.language import get_data_path
20
+ %matplotlib inline
21
+
22
+ class movie:
23
+ def __init__(self, name, idd, keywords_vector, hot, index):
24
+ self.name = name
25
+ self.id = idd
26
+ self.text_vector = keywords_vector
27
+ self.genre_vector = hot
28
+ self.index = index
29
+
30
+ class Filter:
31
+ def __init__(self):
32
+ self.lang = []
33
+ self.date = False
34
+ self.before = None
35
+ self.after = None
36
+ self.pop = 0
37
+ self.rat = 0
38
+ self.runtime = False
39
+ self.more = None
40
+ self.less = None
41
+ self.no_adult = False
42
+ self.company = []
43
+ self.rev = 0
44
+
45
+ def add_lang(self, langu):
46
+ self.lang = langu
47
+
48
+ def dates(self, after, before = 2030):
49
+ self.date = True
50
+ self.after = after
51
+ self.before = before
52
+
53
+ def popp(self, pop):
54
+ self.pop = pop
55
+
56
+ def ratt(self, rat):
57
+ self.rat = rat
58
+
59
+ def length(self, less, more = 0):
60
+ self.runtime = True
61
+ self.less = less
62
+ self.more = more
63
+
64
+ def adult(self):
65
+ self.no_adult = True
66
+
67
+ def add_comp(self, comp):
68
+ self.company =comp
69
+
70
+ def revenue(self, num):
71
+ self.rev = num
72
+
73
+
74
+
75
+
76
+
77
+ path = get_data_path("glove.6B.50d.txt.w2v")
78
+ t0 = time.time()
79
+ glove = KeyedVectors.load_word2vec_format(path, binary=False)
80
+ t1 = time.time()
81
+
82
+ from sentence_transformers import SentenceTransformer
83
+ model = SentenceTransformer('all-MiniLM-L6-v2')
84
+ all_genres = ['TV Movie', 'Animation', 'Adventure', 'Science Fiction', 'Action', 'Horror', 'History', 'Family', 'Western', 'Drama', 'Mystery', 'Romance', 'Music', 'Fantasy', 'Crime', 'War', 'Documentary', 'Thriller', 'Comedy']
85
+
86
+ with open("all_vectors_genre.pkl", "rb") as f:
87
+ all_vectors_genre = pickle.load(f)
88
+ with open("index_map.pkl", "rb") as f:
89
+ index_map = pickle.load(f)
90
+ with open("movies3.pkl", "rb") as f:
91
+ movies = pickle.load(f)
92
+ with open("all_vectors_text.pkl", "rb") as f:
93
+ all_vectors_text = pickle.load(f)
94
+ with open("weights.pkl", "rb") as f:
95
+ w = pickle.load(f)
96
+
97
+ all_vectors_year = []
98
+ for movie in movies:
99
+ ind = movie.index
100
+ if type(df.loc[ind, "release_date"]) == str:
101
+ all_vectors_year.append(int(df.loc[ind, "release_date"][0:4]))
102
+ else:
103
+ all_vectors_year.append(0)
104
+ all_vectors_year = np.array(all_vectors_year)
105
+ languages = {
106
+ "ab": "Abkhazian",
107
+ "aa": "Afar",
108
+ "af": "Afrikaans",
109
+ "ak": "Akan",
110
+ "sq": "Albanian",
111
+ "am": "Amharic",
112
+ "ar": "Arabic",
113
+ "an": "Aragonese",
114
+ "hy": "Armenian",
115
+ "as": "Assamese",
116
+ "av": "Avaric",
117
+ "ae": "Avestan",
118
+ "ay": "Aymara",
119
+ "az": "Azerbaijani",
120
+ "bm": "Bambara",
121
+ "ba": "Bashkir",
122
+ "eu": "Basque",
123
+ "be": "Belarusian",
124
+ "bn": "Bengali",
125
+ "bi": "Bislama",
126
+ "bs": "Bosnian",
127
+ "br": "Breton",
128
+ "bg": "Bulgarian",
129
+ "my": "Burmese",
130
+ "ca": "Catalan",
131
+ "ch": "Chamorro",
132
+ "ce": "Chechen",
133
+ "ny": "Chichewa",
134
+ "cn": "Cantonese",
135
+ "zh": "Chinese",
136
+ "cu": "Church Slavic",
137
+ "cv": "Chuvash",
138
+ "kw": "Cornish",
139
+ "co": "Corsican",
140
+ "cr": "Cree",
141
+ "hr": "Croatian",
142
+ "cs": "Czech",
143
+ "da": "Danish",
144
+ "dv": "Divehi",
145
+ "nl": "Dutch",
146
+ "dz": "Dzongkha",
147
+ "en": "English",
148
+ "eo": "Esperanto",
149
+ "et": "Estonian",
150
+ "ee": "Ewe",
151
+ "fo": "Faroese",
152
+ "fj": "Fijian",
153
+ "fi": "Finnish",
154
+ "fr": "French",
155
+ "ff": "Fulah",
156
+ "gd": "Scottish Gaelic",
157
+ "gl": "Galician",
158
+ "lg": "Ganda",
159
+ "ka": "Georgian",
160
+ "de": "German",
161
+ "el": "Greek",
162
+ "gn": "Guarani",
163
+ "gu": "Gujarati",
164
+ "ht": "Haitian Creole",
165
+ "ha": "Hausa",
166
+ "he": "Hebrew",
167
+ "hz": "Herero",
168
+ "hi": "Hindi",
169
+ "ho": "Hiri Motu",
170
+ "hu": "Hungarian",
171
+ "is": "Icelandic",
172
+ "io": "Ido",
173
+ "ig": "Igbo",
174
+ "id": "Indonesian",
175
+ "ia": "Interlingua",
176
+ "ie": "Interlingue",
177
+ "iu": "Inuktitut",
178
+ "ik": "Inupiaq",
179
+ "ga": "Irish",
180
+ "it": "Italian",
181
+ "ja": "Japanese",
182
+ "jv": "Javanese",
183
+ "kl": "Kalaallisut (Greenlandic)",
184
+ "kn": "Kannada",
185
+ "kr": "Kanuri",
186
+ "ks": "Kashmiri",
187
+ "kk": "Kazakh",
188
+ "km": "Khmer",
189
+ "ki": "Kikuyu",
190
+ "rw": "Kinyarwanda",
191
+ "ky": "Kyrgyz",
192
+ "kv": "Komi",
193
+ "kg": "Kongo",
194
+ "ko": "Korean",
195
+ "kj": "Kuanyama",
196
+ "ku": "Kurdish",
197
+ "lo": "Lao",
198
+ "la": "Latin",
199
+ "lv": "Latvian",
200
+ "li": "Limburgish",
201
+ "ln": "Lingala",
202
+ "lt": "Lithuanian",
203
+ "lu": "Luba-Katanga",
204
+ "lb": "Luxembourgish",
205
+ "mk": "Macedonian",
206
+ "mg": "Malagasy",
207
+ "ms": "Malay",
208
+ "ml": "Malayalam",
209
+ "mt": "Maltese",
210
+ "gv": "Manx",
211
+ "mi": "Maori",
212
+ "mr": "Marathi",
213
+ "mh": "Marshallese",
214
+ "mo": "Moldovan",
215
+ "mn": "Mongolian",
216
+ "na": "Nauru",
217
+ "nv": "Navajo",
218
+ "nd": "North Ndebele",
219
+ "nr": "South Ndebele",
220
+ "ng": "Ndonga",
221
+ "ne": "Nepali",
222
+ "se": "Northern Sami",
223
+ "no": "Norwegian",
224
+ "nb": "Norwegian Bokmål",
225
+ "nn": "Norwegian Nynorsk",
226
+ "ii": "Sichuan Yi",
227
+ "oc": "Occitan",
228
+ "oj": "Ojibwa",
229
+ "or": "Oriya",
230
+ "om": "Oromo",
231
+ "os": "Ossetian",
232
+ "pi": "Pali",
233
+ "pa": "Punjabi",
234
+ "ps": "Pashto",
235
+ "fa": "Persian",
236
+ "pl": "Polish",
237
+ "pt": "Portuguese",
238
+ "qu": "Quechua",
239
+ "rm": "Romansh",
240
+ "ro": "Romanian",
241
+ "rn": "Kirundi",
242
+ "ru": "Russian",
243
+ "sm": "Samoan",
244
+ "sg": "Sango",
245
+ "sa": "Sanskrit",
246
+ "sc": "Sardinian",
247
+ "sr": "Serbian",
248
+ "sn": "Shona",
249
+ "sh": "Serbo-Croatian",
250
+ "sd": "Sindhi",
251
+ "si": "Sinhala",
252
+ "sk": "Slovak",
253
+ "sl": "Slovenian",
254
+ "so": "Somali",
255
+ "st": "Southern Sotho",
256
+ "es": "Spanish",
257
+ "su": "Sundanese",
258
+ "sw": "Swahili",
259
+ "ss": "Swati",
260
+ "sv": "Swedish",
261
+ "tl": "Tagalog",
262
+ "ty": "Tahitian",
263
+ "tg": "Tajik",
264
+ "ta": "Tamil",
265
+ "tt": "Tatar",
266
+ "te": "Telugu",
267
+ "th": "Thai",
268
+ "bo": "Tibetan",
269
+ "ti": "Tigrinya",
270
+ "to": "Tongan",
271
+ "ts": "Tsonga",
272
+ "tn": "Tswana",
273
+ "tr": "Turkish",
274
+ "tk": "Turkmen",
275
+ "tw": "Twi",
276
+ "ug": "Uighur",
277
+ "uk": "Ukrainian",
278
+ "ur": "Urdu",
279
+ "uz": "Uzbek",
280
+ "ve": "Venda",
281
+ "vi": "Vietnamese",
282
+ "vo": "Volapük",
283
+ "wa": "Walloon",
284
+ "cy": "Welsh",
285
+ "wo": "Wolof",
286
+ "xh": "Xhosa",
287
+ "xx": "Unknown/Other",
288
+ "yi": "Yiddish",
289
+ "yo": "Yoruba",
290
+ "za": "Zhuang",
291
+ "zu": "Zulu",
292
+ }
293
+
294
+ def ml(movies_list, k, *ratings):
295
+ from sklearn.metrics.pairwise import cosine_similarity
296
+ k = int(k)
297
+ movie_count = len(movies_list)
298
+ ratings = list(ratings)
299
+ text_sim = np.zeros((k, movie_count))
300
+ genre_sim = np.zeros((k, movie_count))
301
+ date_sim = np.zeros((k, movie_count))
302
+ for i in range(k):
303
+ for j in range(movie_count):
304
+ text_sim[i, j]= cosine_similarity(movies[rec_global[i]].text_vector.reshape(1,-1), movies[user_inp[j]].text_vector.reshape(1, -1)).flatten()
305
+ genre_sim[i, j] = cosine_similarity(movies[rec_global[i]].genre_vector.reshape(1,-1), movies[user_inp[j]].genre_vector.reshape(1, -1)).flatten()
306
+ year = int(df.loc[movies[user_inp[j]].index, "release_date"][0:4])
307
+ year2 = int(df.loc[movies[rec_global[i]].index, "release_date"][0:4])
308
+ differ = np.abs(year2 - year)
309
+ date_sim[i, j] = np.exp(-differ / 5)
310
+
311
+ t = algo_type(algo, text_sim)
312
+ g = algo_type(algo, genre_sim)
313
+ d = algo_type(algo, date_sim)
314
+
315
+ for i in range(len(ratings)):
316
+ if ratings[i] == "N/A":
317
+ ratings[i] = 3
318
+
319
+ print(w)
320
+ neww = eg_update_session(w, np.stack([t, g, d], axis = 1), ratings, eta=0.1, passes=1)
321
+ print(neww)
322
+ with open("weights.pkl", "wb") as f:
323
+ pickle.dump(neww, f)
324
+
325
+ slider_updates = []
326
+ for i in range(20):
327
+ slider_updates.append(gr.update(visible=False))
328
+ return *slider_updates, gr.update(visible = False)
329
+
330
+
331
+
332
+ def get_top_10(similarities, user_inp, filterr, movies, pool):
333
+ top10_index = []
334
+ order = np.argsort(similarities)
335
+ count= 0
336
+ #print("enter")
337
+ for i in range(len(order)-1, -1, -1):
338
+ num = order[i]
339
+ if num not in user_inp:
340
+ ind = movies[num].index
341
+ if ((df.loc[ind, "vote_count"] > filterr.pop or df.loc[ind, "revenue"] > filterr.rev) and df.loc[ind, "vote_average"]> filterr.rat):
342
+ allowed = True
343
+ if (len(filterr.lang)>0 and df.loc[ind, "original_language"] not in filterr.lang):
344
+ allowed = False
345
+ if (filterr.no_adult and df.loc[ind, "adult"]):
346
+ allowed = False
347
+ if (filterr.date):
348
+ if type(df.loc[ind, "release_date"]) == str:
349
+ date = int(df.loc[ind, "release_date"][0:4])
350
+ if (date<filterr.after or date>filterr.before):
351
+ allowed = False
352
+ else:
353
+ allowed = False
354
+ if (filterr.runtime):
355
+ runtime = df.loc[ind, "runtime"]
356
+ if (runtime<filterr.more or runtime>filterr.less):
357
+ allowed = False
358
+
359
+ if (allowed):
360
+ count+=1
361
+ top10_index.append(order[i])
362
+ if (count==pool):
363
+ return top10_index
364
+
365
+ def project_to_simplex(w):
366
+ w = np.asarray(w, dtype=float)
367
+ u = np.sort(w)[::-1]
368
+ cssv = np.cumsum(u)
369
+ rho = np.nonzero(u * (np.arange(len(w))+1) > (cssv - 1))[0][-1]
370
+ theta = (cssv[rho] - 1.0) / (rho + 1)
371
+ return np.maximum(w - theta, 0.0)
372
+
373
+ def rating_to_target(r):
374
+ return {1:0.10, 2:0.30, 3:0.50, 4:0.70, 5:0.9}[int(r)]
375
+
376
+ def eg_update_one(w, x, r, eta):
377
+ """
378
+ w : (3,) current weights [text, genre, year], on the simplex
379
+ x : (3,) features for one item [text_sim, genre_sim, year_sim] in [0,1]
380
+ r : float target in [0,1] (mapped from 1..5)
381
+ eta: learning rate (0.2–0.6 typical)
382
+ """
383
+ x = np.asarray(x, float)
384
+ yhat = float(np.dot(w, x))
385
+ grad = (yhat - r) * x
386
+ w_new = w * np.exp(-eta * grad)
387
+ return project_to_simplex(w_new)
388
+
389
+ def eg_update_session(w, X_session, ratings, eta=0.2, passes=1):
390
+ """
391
+ X_session: (K,3) features for the K shown items (rows correspond to shown order)
392
+ ratings : list/array of ints in {1,2,3,4,5} (length K)
393
+ passes : small number of passes over the session to stabilize
394
+ """
395
+ w_cur = w.copy()
396
+ targets = np.array([rating_to_target(r) for r in ratings], float)
397
+ for _ in range(passes):
398
+ for x, r in zip(X_session, targets):
399
+ w_cur = eg_update_one(w_cur, x, r, eta = eta)
400
+ return w_cur
401
+
402
+ def algo_type(algo, user):
403
+ if (algo == 1):
404
+ #exponent (peak)
405
+ similarities = np.exp(user / 0.5).mean(axis=0)
406
+
407
+ elif (algo == 2):
408
+ #average
409
+ similarities = user.mean(axis = 0)
410
+
411
+ elif (algo == 5):
412
+ #harmonic mean (most middle)
413
+ #user_shifted = user - np.min(user)
414
+ similarities = user.shape[0] / np.sum(1 / (user), axis=0)
415
+
416
+ elif (algo == 4):
417
+ #geo mean (somewhat middle)
418
+ #user_shifted = user - np.min(user)
419
+ similarities = np.exp(np.mean(np.log(user), axis=0))
420
+
421
+ else:
422
+ #geo + avg (middle)
423
+ arithmetic = user.mean(axis=0)
424
+ #user_shifted = user - np.min(user)
425
+ geometric = np.exp(np.mean(np.log(user), axis=0))
426
+ similarities = 0.5 * arithmetic + 0.5 * geometric
427
+ return similarities
428
+
429
+ user_inp = []
430
+ user = []
431
+ algo = 0
432
+ rec_global = []
433
+
434
+ def recommend(movies_list, langs, after, before, rating, rt_min, rt_max, no_adult, k, pop_list, mod):
435
+ global user_inp
436
+ global user
437
+ global algo
438
+ global rec_global
439
+ user_inp = []
440
+ user = []
441
+ algo = 0
442
+ rec_global = []
443
+ if not movies_list:
444
+ return []
445
+ k = int(k)
446
+
447
+ from sklearn.metrics.pairwise import cosine_similarity
448
+ user_text=np.zeros(384)
449
+ user_genre=np.zeros(len(all_genres))
450
+
451
+ movie_count = len(movies_list)
452
+ for user_movie in movies_list:
453
+ num = index_map[user_movie.lower()]
454
+ if isinstance(num, list):
455
+ num = num[0]
456
+ user_inp.append(num)
457
+
458
+ similarities_text = cosine_similarity(all_vectors_text, movies[num].text_vector.reshape(1, -1)).flatten()
459
+ similarities_genre = cosine_similarity(all_vectors_genre, movies[num].genre_vector.reshape(1, -1)).flatten()
460
+ year = int(df.loc[movies[num].index, "release_date"][0:4])
461
+ diff = np.abs(all_vectors_year - year)
462
+ date = np.exp(-diff / 5)
463
+ similarities = similarities_text*w[0]+similarities_genre*w[1]+date*w[2]
464
+ similarities = (similarities+1)/2
465
+ user.append(similarities)
466
+ user = np.array(user)
467
+
468
+ filterr = Filter()
469
+ if no_adult:
470
+ filterr.adult()
471
+ if langs:
472
+ filterr.add_lang(langs)
473
+ filterr.ratt(rating)
474
+ filterr.length(rt_max, rt_min)
475
+ #filterr.length(180, 120)
476
+ filterr.dates(after, before)
477
+
478
+
479
+ if "Unheard" in pop_list:
480
+ popul = 5
481
+ elif "Hidden Gems" in pop_list:
482
+ popul = 4
483
+ elif "Mid Tier" in pop_list:
484
+ popul = 3
485
+ elif "Popular Picks" in pop_list:
486
+ popul = 2
487
+ else:
488
+ popul = 1
489
+
490
+ if (popul == 1):
491
+ filterr.popp(3000)
492
+ filterr.revenue(90000000)
493
+ elif (popul == 2):
494
+ filterr.popp(500)
495
+ filterr.revenue(5000000)
496
+ elif (popul == 3):
497
+ filterr.popp(90)
498
+ filterr.revenue(1000000)
499
+ elif (popul == 4):
500
+ filterr.popp(60)
501
+ filterr.revenue(100000)
502
+ else:
503
+ filterr.popp(10)
504
+ filterr.revenue(10000)
505
+
506
+ if (mod == "Spotlight Matches"):
507
+ algo = 1
508
+ if (mod =="Strong Picks"):
509
+ algo = 2
510
+ if mod == "Balanced Blend":
511
+ algo = 3
512
+ if mod == "Common Ground":
513
+ algo = 4
514
+ if mod == "Strong Agreement":
515
+ algo = 5
516
+
517
+ similarities = algo_type(algo, user)
518
+
519
+ rec_global = get_top_10(similarities, user_inp, filterr, movies, k)
520
+
521
+ top10_movies = [movies[i].name for i in rec_global]
522
+ top10_scores = similarities[rec_global]
523
+ top10_overview = [movies[i].index for i in rec_global]
524
+
525
+ results = [
526
+ [f"https://image.tmdb.org/t/p/w342{df.loc[top10_overview[i], 'poster_path']}", f"{i+1}. {df.loc[top10_overview[i], 'title']} ({df.loc[top10_overview[i], 'release_date'][:4]})"]
527
+ for i in range(len(top10_overview))
528
+ ]
529
+
530
+ slider_updates = []
531
+ for i in range(20):
532
+ if i < k:
533
+ slider_updates.append(gr.update(visible=True, label=f"Rate: {top10_movies[i]}"))
534
+ else:
535
+ slider_updates.append(gr.update(visible=False))
536
+
537
+ return results, *slider_updates, gr.update(visible = True)
538
+
539
+
540
+
541
+
542
+
543
+ import gradio as gr
544
+
545
+ def add_item_mov(txt, items):
546
+ txt = (txt or "").strip()
547
+ #check if in movie list
548
+ if txt and txt not in items and txt.lower() in index_map:
549
+ items = items + [txt]
550
+ return items, gr.update(choices=items, value=[]), gr.update(value="")
551
+
552
+ def add_item(txt, items):
553
+ txt = (txt or "").strip()
554
+ #check if in movie list
555
+ if txt and txt not in items:
556
+ items = items + [txt]
557
+ return items, gr.update(choices=items, value=[]), gr.update(value="")
558
+
559
+ def remove_items(selected, items):
560
+ selected = selected or []
561
+ items = [x for x in items if x not in selected]
562
+ return items, gr.update(choices=items, value=[])
563
+
564
+ def clear_items():
565
+ return [], gr.update(choices=[], value=[])
566
+
567
+ def enforce(selected):
568
+ if "Unheard" in selected:
569
+ return ["Blockbusters", "Popular Picks", "Mid Tier", "Hidden Gems", "Unheard"]
570
+ if "Hidden Gems" in selected:
571
+ return ["Blockbusters", "Popular Picks", "Mid Tier", "Hidden Gems"]
572
+ if "Mid Tier" in selected:
573
+ return ["Blockbusters", "Popular Picks", "Mid Tier"]
574
+ if "Popular Picks" in selected:
575
+ return ["Blockbusters", "Popular Picks"]
576
+ return ["Blockbusters"]
577
+
578
+ def setup_ratings(titles):
579
+ updates = []
580
+ for i, t in enumerate(titles):
581
+ updates.append(gr.update(label=f"Rate: {t}", visible=True, value=None))
582
+ for _ in range(N - len(titles)):
583
+ updates.append(gr.update(visible=False))
584
+ return updates
585
+
586
+
587
+ with gr.Blocks(title="Movie Recommender") as demo:
588
+ gr.Markdown("## Movie Recommender")
589
+
590
+ movies_list = gr.State([])
591
+ langs = gr.State([])
592
+ titles_state = gr.State([])
593
+
594
+
595
+ with gr.Row():
596
+ with gr.Column():
597
+ gr.Markdown("### Movies")
598
+ m_in = gr.Textbox(placeholder="Add a movie", label = "Add Movie")
599
+ with gr.Row():
600
+ m_add = gr.Button("Add", variant="primary")
601
+ m_clear = gr.Button("Clear")
602
+ m_list = gr.CheckboxGroup(choices=[], label="Current (select to remove)")
603
+ m_rm = gr.Button("Remove Selected")
604
+
605
+ gr.Markdown("### Filters")
606
+ with gr.Accordion("Filters", open=False):
607
+ with gr.Column():
608
+ gr.Markdown("### Languages")
609
+ l_in = gr.Dropdown(
610
+ choices=[(name, code) for code, name in sorted(languages.items(), key=lambda x: x[1])],
611
+ label="Select language",
612
+ multiselect=False,
613
+ interactive=True,
614
+ )
615
+ with gr.Row():
616
+ l_add = gr.Button("Add", variant="primary")
617
+ l_clear = gr.Button("Clear")
618
+ l_list = gr.CheckboxGroup(choices=[], label="Languages (select to remove)")
619
+ l_rm = gr.Button("Remove Selected")
620
+
621
+
622
+
623
+ with gr.Row():
624
+ after = gr.Number(label="After year", value=1900)
625
+ before = gr.Number(label="Before year", value=2025)
626
+ with gr.Row():
627
+ rating = gr.Slider(0.0, 10.0, value=0.0, step=0.1, label="Min rating")
628
+ with gr.Row():
629
+ rt_min = gr.Number(label="Min runtime (min)", value=0)
630
+ rt_max = gr.Number(label="Max runtime (min)", value=1000)
631
+
632
+ no_adult = gr.Checkbox(label="Exclude adult content", value=False)
633
+
634
+
635
+ gr.Markdown("### Recommendation Models")
636
+ with gr.Accordion("Recommendation Models", open=False):
637
+ with gr.Column():
638
+ pop_list = gr.CheckboxGroup(choices=["Blockbusters", "Popular Picks", "Mid Tier", "Hidden Gems", "Unheard"],
639
+ value = ["Blockbusters", "Popular Picks"],
640
+ label="Which ones do you want to include?", interactive=True)
641
+
642
+ model = gr.Radio(choices=["Spotlight Matches", "Strong Picks", "Balanced Blend", "Common Ground", "Strong Agreement"], value = "Balanced Blend", label="Pick one", interactive=True)
643
+
644
+
645
+ gr.Markdown("---")
646
+ with gr.Row():
647
+ k = gr.Slider(1, 20, value=10, step=1, label="How many recommendations?")
648
+ go = gr.Button("Get Recommendations", variant="primary")
649
+ gallery = gr.Gallery(columns=5, object_fit="contain")
650
+
651
+ sliders = []
652
+ with gr.Column():
653
+ for start in range(0, 20, 5):
654
+ with gr.Row():
655
+ for i in range(start, start + 5):
656
+ s = gr.Dropdown(choices=["N/A", 1, 2, 3, 4, 5], value = "N/A", visible=False, interactive = True, label=f"Rate: {i+1}")
657
+ sliders.append(s)
658
+ save = gr.Button("Submit ratings", visible = False)
659
+
660
+
661
+
662
+
663
+ m_add.click(add_item_mov, [m_in, movies_list], [movies_list, m_list, m_in])
664
+ m_in.submit(add_item_mov, [m_in, movies_list], [movies_list, m_list, m_in])
665
+
666
+ m_rm.click(remove_items, [m_list, movies_list], [movies_list, m_list])
667
+
668
+ m_clear.click(clear_items, None, [movies_list, m_list])
669
+
670
+ l_add.click(add_item, [l_in, langs], [langs, l_list, l_in])
671
+ l_rm.click(remove_items, [l_list, langs], [langs, l_list])
672
+
673
+ l_clear.click(clear_items, None, [langs, l_list])
674
+
675
+
676
+ pop_list.change(enforce, pop_list, pop_list)
677
+
678
+ # Recommend
679
+ go.click(recommend, [movies_list, langs, after, before, rating, rt_min, rt_max, no_adult, k, pop_list, model],[gallery, *sliders, save])
680
+ save.click(ml, [movies_list, k, *sliders], [*sliders, save])
681
+
682
+
683
+ if __name__ == "__main__":
684
+ demo.launch(share = True)
685
+
requirements.txt ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ gradio
2
+ pandas
3
+ numpy
4
+ matplotlib
5
+ scikit-learn
6
+ gensim
7
+ cogworks-data
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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+ ]
sample_data/california_housing_test.csv ADDED
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sample_data/california_housing_train.csv ADDED
The diff for this file is too large to render. See raw diff
 
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