http://127.0.0.1:5000/data returns error code 500 INTERNAL SERVER ERROR

[MixoMax]

When i try to visualize my collection, the /data endpoint causes a 500 server error:

INFO:werkzeug:127.0.0.1 - - [25/Oct/2023 12:58:07] "GET [/data](https://file+.vscode-resource.vscode-cdn.net/data) HTTP/1.1" 500 -
         0         1         2         3         4         5         6    \
0   0.037332 -0.023781  0.012198 -0.005240  0.076604  0.001155 -0.014276   
1   0.030470 -0.003900 -0.015485 -0.023972  0.063941 -0.060111 -0.014092   
2  -0.053416 -0.008176 -0.002372  0.051473  0.060097  0.035009  0.016631   
3  -0.056681 -0.026865 -0.034603  0.030002  0.042702 -0.028928  0.020197   
4   0.013042 -0.042856 -0.013761  0.036834  0.030365  0.038543 -0.010759   
5   0.005877 -0.042724 -0.027274  0.010114  0.023459 -0.004595 -0.005909   
6  -0.066772  0.006261 -0.047721  0.008414  0.056853 -0.047925 -0.010367   
7  -0.059567 -0.039042  0.000922 -0.001134  0.023288 -0.046869 -0.016602   
8  -0.042325 -0.032608 -0.000407  0.018782  0.053377 -0.056314  0.030714   
9  -0.028975  0.032526 -0.045487  0.055819  0.010403  0.034379  0.063932   
10 -0.048563 -0.029803 -0.039211  0.046758  0.066782 -0.002995  0.059443   
11 -0.078533 -0.034406 -0.018979  0.073978  0.067306  0.023627 -0.016573   
12 -0.040124  0.056453 -0.016754 -0.019866  0.001475 -0.035780 -0.007667   
13  0.018375  0.076316 -0.025173 -0.010658 -0.025128 -0.022667  0.035033   
14  0.030359  0.016650  0.029888 -0.049801 -0.043548  0.065044  0.029868   
15  0.013895  0.032976 -0.025223  0.032685  0.038733  0.007347  0.035505   
16 -0.032058  0.079850  0.034362 -0.065866 -0.036254 -0.070180  0.023393   
17 -0.050679  0.043993 -0.074905 -0.004738 -0.025914 -0.052690  0.034850   
18  0.001749  0.004550 -0.036564 -0.070386 -0.052163 -0.057619  0.035614   
19  0.036063  0.070261 -0.053000 -0.037017 -0.036630  0.010571  0.066405   
20  0.024235  0.063356 -0.054793 -0.029787 -0.020596 -0.071459  0.033064   
21 -0.059592  0.041362 -0.036292  0.007127  0.012279  0.014098 -0.052634   
22 -0.060635  0.073746 -0.043353  0.046178  0.000276 -0.031570  0.018131   
23  0.025379 -0.029765  0.002895 -0.017975 -0.038120  0.061182  0.008354   
24 -0.065203  0.037062 -0.025169 -0.043031 -0.002735 -0.039980  0.004093   
25  0.004894  0.055953  0.011643  0.046046  0.015779  0.065883  0.038230   
26 -0.024362 -0.035757 -0.019241  0.026651  0.039099 -0.065665 -0.016519   
27 -0.027676  0.026229 -0.044375 -0.000928 -0.007377 -0.066107  0.018930   
28 -0.072507  0.018927  0.003620  0.060673  0.030222  0.052429  0.030121   
29  0.054735  0.068607 -0.073256 -0.002494  0.046521 -0.029290  0.022471   
30 -0.037575  0.011722 -0.030505  0.072252  0.074436 -0.019824  0.038547   
31  0.046324  0.012548 -0.041335  0.034979  0.030446  0.023014 -0.023115   
32  0.009296 -0.021602 -0.057132  0.026589 -0.027671 -0.023843 -0.054125   
33 -0.047493 -0.036450 -0.013627  0.052395  0.047732 -0.058966 -0.037612   

         7         8         9    ...       502       503       504       505  \
0   0.008024  0.060044  0.029361  ... -0.009279 -0.023715 -0.015751 -0.013304   
1  -0.010978  0.043185  0.020901  ... -0.005410  0.003037 -0.011462 -0.064343   
2  -0.015547  0.042151  0.037624  ...  0.023804  0.029791  0.024964 -0.040885   
3  -0.013639  0.034665  0.020015  ...  0.006872  0.048773  0.039655 -0.069227   
4  -0.016177  0.068578  0.003459  ... -0.024571  0.062359 -0.006766 -0.036526   
5  -0.027050  0.058815  0.005194  ... -0.025979  0.069981  0.013728 -0.056290   
6  -0.033108 -0.060129  0.007087  ... -0.023777  0.030056  0.005010 -0.052337   
7  -0.017220  0.020616  0.005380  ...  0.014116  0.054109 -0.018877 -0.018004   
8  -0.058906 -0.071288 -0.084879  ...  0.043875  0.021019 -0.010141 -0.073011   
9  -0.015690 -0.089544  0.004032  ...  0.008158  0.077191  0.005024 -0.069949   
10 -0.051377 -0.079748 -0.077363  ... -0.047476  0.024338  0.006369 -0.085023   
11 -0.007470 -0.038817  0.026817  ... -0.071988  0.055395  0.064981  0.030653   
12  0.020591  0.080325  0.060433  ...  0.053331  0.065969 -0.036978 -0.083878   
13 -0.056395  0.054601  0.033762  ...  0.049224  0.047197 -0.053725 -0.028474   
14  0.033852  0.057624 -0.045598  ...  0.000804  0.065920 -0.030025 -0.081059   
15 -0.026954  0.043701  0.016701  ...  0.002854  0.059009 -0.072334 -0.034390   
16 -0.051204  0.077887 -0.048077  ...  0.039129  0.037951 -0.033659  0.012854   
17 -0.011362  0.036235 -0.058835  ...  0.034847  0.072305  0.048777 -0.075487   
18 -0.015401 -0.053733 -0.057567  ...  0.017474  0.069098 -0.049530 -0.075420   
19 -0.022096  0.072510 -0.056408  ...  0.057827  0.062493 -0.038635 -0.046401   
20  0.015191 -0.047425  0.009777  ...  0.044122  0.072265 -0.074245 -0.077606   
21  0.062831  0.032591  0.037691  ...  0.021291  0.060686 -0.021363 -0.023141   
22 -0.019911 -0.069257  0.016237  ...  0.029327  0.066845  0.009949 -0.086336   
23 -0.038988 -0.093563 -0.036053  ...  0.003514  0.070978  0.034271  0.022625   
24 -0.021587 -0.072259 -0.044717  ...  0.030433  0.072023 -0.037791 -0.081257   
25 -0.020064 -0.060841 -0.055939  ...  0.038341  0.010689 -0.084277 -0.079162   
26  0.021258 -0.053852 -0.038993  ... -0.038840  0.024913 -0.050933 -0.074633   
27  0.041727 -0.074194 -0.064412  ...  0.043329  0.064479  0.049853 -0.077119   
28 -0.003505 -0.096430  0.035436  ... -0.002518  0.068902 -0.013336 -0.038727   
29 -0.011793 -0.039794 -0.016975  ... -0.017931  0.049746 -0.030473 -0.059766   
30  0.010207 -0.044357 -0.054522  ...  0.064027  0.024848  0.054487 -0.070881   
31 -0.014810 -0.084757  0.027470  ... -0.087155  0.026196 -0.083433 -0.053757   
32 -0.061350 -0.076431  0.007814  ...  0.056633  0.053737  0.016689  0.026026   
33 -0.050325 -0.074288 -0.044579  ...  0.071013 -0.040185  0.022621 -0.061165   

         506       507       508       509       510       511  
0   0.007775 -0.095998 -0.046793  0.030200  0.045731 -0.027019  
1   0.016610 -0.087086 -0.051602  0.030389  0.050576 -0.020754  
2   0.039993 -0.092495 -0.058261  0.019242  0.042531 -0.030493  
3   0.041741 -0.090846 -0.063663  0.012026  0.042810 -0.027029  
4   0.015731 -0.099377 -0.049119  0.021458  0.022096  0.000982  
5   0.017304 -0.092860 -0.059497  0.024467  0.036667  0.002914  
6   0.026571 -0.086814  0.005377  0.014170  0.040233 -0.012169  
7   0.031023 -0.082871  0.003966  0.007463  0.036262  0.039472  
8   0.026039 -0.086807 -0.044998  0.007627  0.055528 -0.036907  
9   0.052685 -0.088993 -0.023056  0.025650  0.039750  0.005575  
10  0.022355 -0.081196 -0.000355  0.011888  0.037407  0.002402  
11  0.007430 -0.080597  0.009811  0.044425  0.006977 -0.010428  
12  0.058908 -0.076054 -0.000748 -0.018091  0.070045  0.058598  
13  0.047309 -0.060535 -0.035036  0.030780  0.076246  0.051501  
14  0.048645  0.073527  0.032925  0.029199  0.020705  0.075084  
15 -0.025052 -0.071407 -0.048819  0.026808  0.000149  0.037452  
16 -0.083757 -0.071528  0.044818  0.013089 -0.021773  0.052537  
17  0.015151 -0.082091 -0.002808  0.005803  0.048171 -0.060211  
18 -0.006031  0.071764 -0.020130 -0.015293  0.015983  0.026699  
19  0.049587 -0.072828 -0.028956  0.002941  0.022244  0.012457  
20  0.071769 -0.058493 -0.025794  0.005382  0.058536  0.047511  
21  0.000378 -0.085529  0.000841  0.046993  0.046224 -0.028370  
22  0.035358 -0.084977 -0.026532  0.015352  0.052120  0.036663  
23 -0.003215  0.004681  0.068580 -0.013297  0.009633 -0.045675  
24 -0.029970 -0.087881  0.022975  0.003078  0.021798  0.013759  
25  0.073087  0.000363  0.032176  0.015431 -0.035607  0.041946  
26  0.051575 -0.059862  0.026390  0.048598  0.036532  0.041015  
27  0.028671 -0.078497  0.024266  0.048963  0.017075  0.020607  
28 -0.067752 -0.092406  0.032862  0.055852 -0.001208  0.055984  
29  0.043003 -0.068191  0.020234  0.057081 -0.039051  0.028884  
30  0.038409 -0.076699  0.053538  0.017457  0.037722  0.002966  
31  0.017005 -0.087661 -0.003598 -0.023067 -0.009526  0.014693  
32 -0.081903 -0.078386 -0.031118  0.056724 -0.039457  0.038010  
33  0.001368 -0.077690  0.047063  0.054346  0.051049 -0.025174  

[34 rows x 512 columns]
Size of the dataframe: (34, 512)
ERROR:chromaviz.visualize:Exception on [/data](https://file+.vscode-resource.vscode-cdn.net/data) [GET]
Traceback (most recent call last):
  File "/home/linus/.local/lib/python3.10/site-packages/flask/app.py", line 1455, in wsgi_app
    response = self.full_dispatch_request()
  File "/home/linus/.local/lib/python3.10/site-packages/flask/app.py", line 869, in full_dispatch_request
    rv = self.handle_user_exception(e)
  File "/home/linus/.local/lib/python3.10/site-packages/flask_cors/extension.py", line 176, in wrapped_function
    return cors_after_request(app.make_response(f(*args, **kwargs)))
  File "/home/linus/.local/lib/python3.10/site-packages/flask/app.py", line 867, in full_dispatch_request
    rv = self.dispatch_request()
  File "/home/linus/.local/lib/python3.10/site-packages/flask/app.py", line 852, in dispatch_request
    return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args)
  File "/home/linus/.local/lib/python3.10/site-packages/chromaviz/visualize.py", line 59, in data_api
    pca_result_50 = pca_50.fit_transform(df)
  File "/home/linus/.local/lib/python3.10/site-packages/sklearn/utils/_set_output.py", line 157, in wrapped
    data_to_wrap = f(self, X, *args, **kwargs)
  File "/home/linus/.local/lib/python3.10/site-packages/sklearn/base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
  File "/home/linus/.local/lib/python3.10/site-packages/sklearn/decomposition/_pca.py", line 460, in fit_transform
    U, S, Vt = self._fit(X)
  File "/home/linus/.local/lib/python3.10/site-packages/sklearn/decomposition/_pca.py", line 510, in _fit
    return self._fit_full(X, n_components)
  File "/home/linus/.local/lib/python3.10/site-packages/sklearn/decomposition/_pca.py", line 524, in _fit_full
    raise ValueError(
ValueError: n_components=50 must be between 0 and min(n_samples, n_features)=34 with svd_solver='full'

[vinaysudhakaran]

I am facing the same issue.

[coffeecodeconverter]

The issue is caused because the collection you point it to, needs a minimum of 50+ data points.
as denoted by this error:

ValueError: n_components=50 must be between 0 and min(n_samples, n_features)=34 with svd_solver='full'

these lines:

[34 rows x 512 columns]
Size of the dataframe: (34, 512)

confirm you only had 34 x documents in your collection
and were embedded with a model that has a dimensionality of 512

so you were 16 x documents short of the minimum, hence your error
add more documents to your collection, or try a bigger dataset