oegedijk/explainerdashboard: v0.5.7: what input features controls, dataframe input robustness

Version 0.5.7: Bug Fixes Allow FeatureInputComponent (what-if inputs) to customize numeric ranges and rounding, and apply min/max/step to inputs. Add input_features and hide_features to FeatureInputComponent so what-if fields can be explicitly ordered and selectively hidden, while preserving the full callback input contract. Fix issue #220: accept single-row list/array X_row inputs in get_contrib_df, and harden related row-input paths (get_col_value_plus_prediction, pdp_df, and classifier/regression prediction_result_df) with regression tests. Fix issue #262: add feature-based filters to classifier/regression random_index(...) (numeric ranges and categorical inclusion), enabling what-if random selection constrained by input feature values. Improve compatibility with AutoGluon/custom wrappers by coercing pandas DataFrame outputs from predict_proba/predict to numpy arrays before indexing in classifier/regression helper paths. Harden one-vs-all scorer handling so make_one_vs_all_scorer also accepts classifiers whose predict_proba returns a pandas DataFrame. Fix ExplainerHub add_dashboard_route after first request by allowing dynamic dashboard registration/setup during route-triggered add, and add a regression test for issue #269. Fix issue #273: avoid crashes when sorting categorical values containing mixed types and NaNs across explainer setup, PDP/category ordering, and categorical plot paths. Add regression tests for mixed-type categorical sorting and mixed target-label sorting fallbacks in explainers and plots.