Deploying app with fonctions dependencies and multiple python scripts files

Deployment
1

Hi everyone,

First, I’d like to say that I am happy to be here, I just discover streamlit and it seems to be a incredible tools but I might not have understand something.

I built my application using 5 différents files. The app is about NBA datas and is composed of 3 parts:

  • The first one is calling fonctions using beautiful soup and pandas to build and display some dataframes but nothing comes out when I Iaunch a research . It was perfectly working in local and I don’t get any error.
  • The second part is using fonctions with sklearn models. It was also working before the deployement and it gives me this message now :
    AttributeError: Can’t get attribute ‘PolynomialFeatures’ on <module ‘sklearn.preprocessing._data’ from ‘/home/appuser/venv/lib/python3.7/site-packages/sklearn/preprocessing/_data.py’>
  • The last part is also using a fonction with this type of model and gives me this message :
    raise ValueError(“All objects passed were None”).
    For this last error, I’ve already seen it while I was testing my app before deployment and I fixed it but it looks like it’s still a problem.

I was wondering if I did something wrong when I built my app that causes these errors. If you want to check by yourself, here is the link : https://share.streamlit.io/bastienchicot/nbapp_streamlit/NBApp.py

Of course, I did a requirements files with all the dependecies but nothing seems to work.
Thank you and sorry because the app is all in french btw

Bastien

3

Hi @BastienChicot, welcome to the Streamlit community!

I get the following error from ‘Projections individuelles’

Traceback (most recent call last):

  File "/home/appuser/venv/lib/python3.7/site-packages/streamlit/script_runner.py", line 354, in _run_script

    exec(code, module.__dict__)

  File "/app/nbapp_streamlit/NBApp.py", line 19, in <module>

    onglet_prediction()

  File "/app/nbapp_streamlit/Fonctions.py", line 90, in onglet_prediction

    predp,predp_2,predf,preda,predr=calcul_predictions(a,opp_option,b,starter,home,month,m)

  File "/app/nbapp_streamlit/Services/predictions_joueur.py", line 118, in calcul_predictions

    df1['FGA_pred'] = MLP.predict(FGA)

  File "/home/appuser/venv/lib/python3.7/site-packages/sklearn/utils/metaestimators.py", line 119, in <lambda>

    out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs)

  File "/home/appuser/venv/lib/python3.7/site-packages/sklearn/pipeline.py", line 407, in predict

    Xt = transform.transform(Xt)

  File "/home/appuser/venv/lib/python3.7/site-packages/sklearn/compose/_column_transformer.py", line 604, in transform

    Xs = self._fit_transform(X, None, _transform_one, fitted=True)

  File "/home/appuser/venv/lib/python3.7/site-packages/sklearn/compose/_column_transformer.py", line 467, in _fit_transform

    self._iter(fitted=fitted, replace_strings=True), 1))

  File "/home/appuser/venv/lib/python3.7/site-packages/joblib/parallel.py", line 1048, in __call__

    if self.dispatch_one_batch(iterator):

  File "/home/appuser/venv/lib/python3.7/site-packages/joblib/parallel.py", line 866, in dispatch_one_batch

    self._dispatch(tasks)

  File "/home/appuser/venv/lib/python3.7/site-packages/joblib/parallel.py", line 784, in _dispatch

    job = self._backend.apply_async(batch, callback=cb)

  File "/home/appuser/venv/lib/python3.7/site-packages/joblib/_parallel_backends.py", line 208, in apply_async

    result = ImmediateResult(func)

  File "/home/appuser/venv/lib/python3.7/site-packages/joblib/_parallel_backends.py", line 572, in __init__

    self.results = batch()

  File "/home/appuser/venv/lib/python3.7/site-packages/joblib/parallel.py", line 263, in __call__

    for func, args, kwargs in self.items]

  File "/home/appuser/venv/lib/python3.7/site-packages/joblib/parallel.py", line 263, in <listcomp>

    for func, args, kwargs in self.items]

  File "/home/appuser/venv/lib/python3.7/site-packages/sklearn/pipeline.py", line 719, in _transform_one

    res = transformer.transform(X)

  File "/home/appuser/venv/lib/python3.7/site-packages/sklearn/pipeline.py", line 549, in _transform

    Xt = transform.transform(Xt)

  File "/home/appuser/venv/lib/python3.7/site-packages/sklearn/preprocessing/_data.py", line 1551, in transform

    accept_sparse=('csr', 'csc'))

  File "/home/appuser/venv/lib/python3.7/site-packages/sklearn/utils/validation.py", line 72, in inner_f

    return f(**kwargs)

  File "/home/appuser/venv/lib/python3.7/site-packages/sklearn/utils/validation.py", line 598, in check_array

    array = np.asarray(array, order=order, dtype=dtype)

  File "/home/appuser/venv/lib/python3.7/site-packages/numpy/core/_asarray.py", line 83, in asarray

    return array(a, dtype, copy=False, order=order)

  File "/home/appuser/venv/lib/python3.7/site-packages/pandas/core/generic.py", line 1781, in __array__

    return np.asarray(self._values, dtype=dtype)

  File "/home/appuser/venv/lib/python3.7/site-packages/numpy/core/_asarray.py", line 83, in asarray

    return array(a, dtype, copy=False, order=order)

**ValueError: could not convert string to float:**

In this case, Python doesn’t know how to auto-convert a string into numeric. Since you are likely passing a value in from the multi-select box, you should ensure that you are doing the string-to-number conversion yourself to make sure there aren’t any type mismatches.

Best,
Randy

4

Oh ok thank you. I’ll check that.
By the way, someone help me with the logs of the app but wasn’t able to tell me why the first page doesn’t work.

5

Thank you very much for this, I realised that selectbox needed to be transform as string or int before getting pass to a function.

1 Like
6

OK I will switch this subject, now I clearly know that the problem comes from beautiful soup 4 or the way my functions are written because the chart I display from basketball reference aren’t showing up on the first page and I test without them and the app is working.
I got to find out that now

1 Like
7

If it would help, we’ve got the #streamlit-fr channel on our Discord, if it would make it easier for you to find someone in the community to help:

2 Likes