This will hack the ketcher submission to work again (see #207).
The problem seems to be that the iframe loads slower than the script tag so the reference is not available on page load.
Registering from within the code to poll until ketcher is ready is a bit messy.
Tracked the introduced dept in #212.
Reviewed-on: enviPath/enviPy#213
Co-authored-by: Tobias O <tobias.olenyi@envipath.com>
Co-committed-by: Tobias O <tobias.olenyi@envipath.com>
Adds packages to the footer (instead of Browse link) and removes search from footer (search page not directly linked anymore).
Packages are also available in Browse now.
This is temporary until there is a proper data browsing page.
Co-authored-by: jebus <lorsbach@envipath.com>
Reviewed-on: enviPath/enviPy#196
Co-authored-by: Tobias O <tobias.olenyi@envipath.com>
Co-committed-by: Tobias O <tobias.olenyi@envipath.com>
## Major Changes
- Predict Pathway is now a separate view as it is meant as adavanced prediction interface
## Current status
Co-authored-by: Tim Lorsbach <tim@lorsba.ch>
Reviewed-on: enviPath/enviPy#188
Co-authored-by: Tobias O <tobias.olenyi@envipath.com>
Co-committed-by: Tobias O <tobias.olenyi@envipath.com>
Implementing a search modal (stretching the level of dynamic that is possible without going to frameworks).
## Major Change
- Search needs packages and is available everywhere now; so had to add reviewed and user packages to global context.
Co-authored-by: Tim Lorsbach <tim@lorsba.ch>
Reviewed-on: enviPath/enviPy#185
Co-authored-by: Tobias O <tobias.olenyi@envipath.com>
Co-committed-by: Tobias O <tobias.olenyi@envipath.com>
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>
## Changes
- Ability to change the threshold from a command line argument.
- Names of data packages included in model name
- Names of data, rule and eval packages included in the model description
- EnviFormer models are now viewable on the admin site
- Ignore CO2 for training and evaluating EnviFormer
Co-authored-by: Liam Brydon <62733830+MyCreativityOutlet@users.noreply.github.com>
Reviewed-on: enviPath/enviPy#173
Reviewed-by: jebus <lorsbach@envipath.com>
Co-authored-by: liambrydon <lbry121@aucklanduni.ac.nz>
Co-committed-by: liambrydon <lbry121@aucklanduni.ac.nz>
The caching is now finished. The cache is created in `settings.py` giving us the most flexibility for using it in the future.
The cache is currently updated/accessed by `tasks.py/get_ml_model` which can be called from whatever task needs to access ml models in this way (currently, `predict` and `predict_simple`).
This implementation currently caches all ml models including the relative reasoning. If we don't want this and only want to cache enviFormer, i can change it to that. However, I don't think there is a harm in having the other models be cached as well.
Co-authored-by: Liam Brydon <62733830+MyCreativityOutlet@users.noreply.github.com>
Reviewed-on: enviPath/enviPy#156
Co-authored-by: liambrydon <lbry121@aucklanduni.ac.nz>
Co-committed-by: liambrydon <lbry121@aucklanduni.ac.nz>
## Summary
This PR improves the local development setup experience by adding Docker Compose and Makefile for streamlined setup.
## Changes
- **Added `docker-compose.yml`**: for one-command PostgreSQL database setup
- **Added `Makefile`**: Convenient shortcuts for common dev tasks (\`make setup\`, \`make dev\`, etc.)
- **Updated `README.md`**: Quick development setup instructions using Make
-
- **Added**: RDkit installation pain point documentation
- **Fixed**: Made Java feature properly dependent
## Why these changes?
The application uses PostgreSQL-specific features (\`ArrayField\`) and requires an anonymous user created by the bootstrap command. This PR makes the setup process trivial for new developers:
```bash
cp .env.local.example .env
make setup # Starts DB, runs migrations, bootstraps data
make dev # Starts development server
```
Java fix:
Moved global Java import to inline to avoid everyone having to configure the Java path.
Numerous changes to view and settings.
- Applied ruff-formatting
## Testing
Verified complete setup from scratch works with:
- PostgreSQL running in Docker
- All migrations applied
- Bootstrap data loaded successfully
- Anonymous user created
- The development server starts correctly.
Co-authored-by: Tobias O <tobias.olenyi@tum.de>
Co-authored-by: Tobias O <tobias.olenyi@envipath.com>
Co-authored-by: Liam <62733830+limmooo@users.noreply.github.com>
Reviewed-on: enviPath/enviPy#143
Reviewed-by: jebus <lorsbach@envipath.com>
Reviewed-by: liambrydon <lbry121@aucklanduni.ac.nz>
Co-authored-by: t03i <mail+envipath@t03i.net>
Co-committed-by: t03i <mail+envipath@t03i.net>
## Changes
- I have finished the backend integration of EnviFormer (#19), this includes, dataset building, model finetuning, model evaluation and model prediction with the finetuned model.
- `PackageBasedModel` has been adjusted to be more abstract, this includes making the `_save_model` method and making `compute_averages` a static class function.
- I had to bump the python-version in `pyproject.toml` to >=3.12 from >=3.11 otherwise uv failed to install EnviFormer.
- The default EnviFormer loading during `settings.py` has been removed.
## Future Fix
I noticed you have a little bit of code in `PackageBasedModel` -> `evaluate_model` for using the `eval_packages` during evaluation instead of train/test splits on `data_packages`. It doesn't seem finished, I presume we want this for all models, so I will take care of that in a new branch/pullrequest after this request is merged.
Also, I haven't done anything for a POST request to finetune the model, I'm not sure if that is something we want now.
Co-authored-by: Liam Brydon <62733830+MyCreativityOutlet@users.noreply.github.com>
Reviewed-on: enviPath/enviPy#141
Reviewed-by: jebus <lorsbach@envipath.com>
Co-authored-by: liambrydon <lbry121@aucklanduni.ac.nz>
Co-committed-by: liambrydon <lbry121@aucklanduni.ac.nz>
jebusandTim Lorsbach
t03i
