This PR introduces an overhaul for the front page and login features while keeping the rest of the application intact.
## Major Changes
- TailwindCSS + DaisyUI Integration: Add modern CSS framework for component-based utility styling
- Build System: Added pnpm for CSS building; can be extended for updated frontend builds in the future
- Navbar + Footer: Redesigned and includable; old version retained for unstyled elements
- Optimized Assets: Added minified and CSS-stylable logos
## New Features
- Static Pages: Added comprehensive mockups of static pages (legal, privacy policy, terms of use, contact, etc.). **Note:** These have to be fixed before a public release, as their content is largely unreviewed and incorrect. Probably best to do in a separate PR that only contains updates to these.
- Discourse API: Implement minimal features based on RestAPI for controllable results.
## Current bugs
- [x] The static pages include the default navbar and footer on the login page. This will likely not work, as users need to access it before logging in; no good workaround so far (problem with Django templating system).
- [ ] The front page predict link is currently non-functional; the redesigned page is almost ready but better done in a separate PR as it also touches Django code.
- [x] Visual bug with the news cards. Still intend to fix for this PR
Co-authored-by: Tim Lorsbach <tim@lorsba.ch>
Reviewed-on: enviPath/enviPy#179
Reviewed-by: jebus <lorsbach@envipath.com>
Co-authored-by: Tobias O <tobias.olenyi@envipath.com>
Co-committed-by: Tobias O <tobias.olenyi@envipath.com>
## Changes
- All text input fields are now cleaned with nh3 to remove html tags. We allow certain html tags under `settings.py/ALLOWED_HTML_TAGS` so we can easily update the tags we allow in the future.
- All names and descriptions now use the template tag `nh_safe` in all html files.
- Usernames and emails are a small exception and are not allowed any html tags
Co-authored-by: Liam Brydon <62733830+MyCreativityOutlet@users.noreply.github.com>
Co-authored-by: jebus <lorsbach@envipath.com>
Co-authored-by: Tim Lorsbach <tim@lorsba.ch>
Reviewed-on: enviPath/enviPy#171
Reviewed-by: jebus <lorsbach@envipath.com>
Co-authored-by: liambrydon <lbry121@aucklanduni.ac.nz>
Co-committed-by: liambrydon <lbry121@aucklanduni.ac.nz>
# Summary
I have introduced a new base `class Dataset` in `ml.py` which all datasets should subclass. It stores the dataset as a polars DataFrame with the column names and number of columns determined by the subclass. It implements generic methods such as `add_row`, `at`, `limit` and dataset saving. It also details abstract methods required by the subclasses. These include `X`, `y` and `generate_dataset`.
There are two subclasses that currently exist. `RuleBasedDataset` for the MLRR models and `EnviFormerDataset` for the enviFormer models.
# Old Dataset to New RuleBasedDataset Functionality Translation
- [x] \_\_init\_\_
- self.columns and self.num_labels moved to base Dataset class
- self.data moved to base class with name self.df along with initialising from list or from another DataFrame
- struct_features, triggered and observed remain the same
- [x] \_block\_indices
- function moved to base Dataset class
- [x] structure_id
- stays in RuleBasedDataset, now requires an index for the row of interest
- [x] add_row
- moved to base Dataset class, now calls add_rows so one or more rows can be added at a time
- [x] times_triggered
- stays in RuleBasedDataset, now does a look up using polars df.filter
- [x] struct_features (see init)
- [x] triggered (see init)
- [x] observed (see init)
- [x] at
- removed in favour of indexing with getitem
- [x] limit
- removed in favour of indexing with getitem
- [x] classification_dataset
- stays in RuleBasedDataset, largely the same just with new dataset construction using add_rows
- [x] generate_dataset
- stays in RuleBasedDataset, largely the same just with new dataset construction using add_rows
- [x] X
- moved to base Dataset as @abstract_method, RuleBasedDataset implementation functionally the same but uses polars
- [x] trig
- stays in RuleBasedDataset, functionally the same but uses polars
- [x] y
- moved to base Dataset as @abstract_method, RuleBasedDataset implementation functionally the same but uses polars
- [x] \_\_get_item\_\_
- moved to base dataset, now passes item to the dataframe for polars to handle
- [x] to_arff
- stays in RuleBasedDataset, functionally the same but uses polars
- [x] \_\_repr\_\_
- moved to base dataset
- [x] \_\_iter\_\_
- moved to base Dataset, now uses polars iter_rows
# Base Dataset class Features
The following functions are available in the base Dataset class
- init - Create the dataset from a list of columns and data in format list of list. Or can create a dataset from a polars Dataframe, this is essential for recreating itself during indexing. Can create an empty dataset by just passing column names.
- add_rows - Add rows to the Dataset, we check that the new data length is the same but it is presumed that the column order matches the existing dataframe
- add_row - Add one row, see add_rows
- block_indices - Returns the column indices that start with the given prefix
- columns - Property, returns dataframe.columns
- shape - Property, returns dataframe.shape
- X - Abstract method to be implemented by the subclasses, it should represent the input to a ML model
- y - Abstract method to be implemented by the subclasses, it should represent the target for a ML model
- generate_dataset - Abstract and static method to be implemented by the subclasses, should return an initialised subclass of Dataset
- iter - returns the iterable from dataframe.iter_rows()
- getitem - passes the item argument to the dataframe. If the result of indexing the dataframe is another dataframe, the new dataframe is packaged into a new Dataset of the same subclass. If the result of indexing is something else (int, float, polar Series) return the result.
- save - Pickle and save the dataframe to the given path
- load - Static method to load the dataset from the given path
- to_numpy - returns the dataframe as a numpy array. Required for compatibility with training of the ECC model
- repr - return a representation of the dataset
- len - return the length of the dataframe
- iter_rows - Return dataframe.iterrows with arguments passed through. Mainly used to get the named iterable which returns rows of the dataframe as dict of column names: column values instead of tuple of column values.
- filter - pass to dataframe.filter and recreates self with the result
- select - pass to dataframe.select and recreates self with the result
- with_columns - pass to dataframe.with_columns and recreates self with the result
- sort - pass to dataframe.sort and recreates self with the result
- item - pass to dataframe.item
- fill_nan - fill the dataframe nan's with value
- height - Property, returns the height (number of rows) of the dataframe
- [x] App domain
- [x] MACCS alternatives
Co-authored-by: Liam Brydon <62733830+MyCreativityOutlet@users.noreply.github.com>
Reviewed-on: enviPath/enviPy#184
Reviewed-by: jebus <lorsbach@envipath.com>
Co-authored-by: liambrydon <lbry121@aucklanduni.ac.nz>
Co-committed-by: liambrydon <lbry121@aucklanduni.ac.nz>
Bump Python Version to 3.12
Make use of "epauth" optional
Cache `srs` property of rules to speed up apply
Adjust view names for use of `reverse()`
Fix Views for Scenario Attachments
Added Simply Compare View/Template to identify differences between rdkit and ambit
Make migrations consistent with tests + compare
Fixes#76
Set default year for Scenario Modal
Fix html tags for package description
Added Tests for Pathway / Rule
Added remove stereo for apply
Co-authored-by: Tim Lorsbach <tim@lorsba.ch>
Reviewed-on: enviPath/enviPy#132
This PR adds a new Field to all existing Models.
As its a data migrations the Migration is added.
Co-authored-by: Tim Lorsbach <tim@lorsba.ch>
Reviewed-on: enviPath/enviPy#63
