various updates

.github/ISSUE_TEMPLATE/wrong-prediction.yml

@@ -0,0 +1,55 @@
+name: Wrong prediction
+description: Report a ChEBI class Chebifier got wrong - one it predicted that does not apply, or one it missed
+title: "[Prediction] "
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for helping to improve Chebifier. If you arrived here from the "Report a wrong
+        prediction" button in the web app, the molecule and the prediction details are already
+        filled in below - please tell us what should have happened instead, and why.
+  - type: input
+    id: molecule
+    attributes:
+      label: Molecule
+      description: The SMILES or InChI string that was classified.
+      placeholder: CC(=O)Oc1ccccc1C(=O)O
+    validations:
+      required: true
+  - type: dropdown
+    id: kind
+    attributes:
+      label: What went wrong?
+      options:
+        - A class was predicted that does not apply
+        - A class that applies was not predicted
+        - The classification is right but the explanation is not
+        - Something else
+    validations:
+      required: true
+  - type: textarea
+    id: classes
+    attributes:
+      label: Classes concerned
+      description: Which ChEBI classes are affected? Please give the ChEBI ids where you can.
+      placeholder: |
+        CHEBI:22315 (alkaloid) should not have been predicted.
+    validations:
+      required: true
+  - type: textarea
+    id: rationale
+    attributes:
+      label: Why is this wrong?
+      description: >
+        What is the correct classification, and what makes it correct? A definition, a reference or
+        the structural feature that decides it all help us a great deal.
+    validations:
+      required: true
+  - type: textarea
+    id: prediction
+    attributes:
+      label: Prediction details
+      description: >
+        Filled in by the web app - the ensemble score, what each model contributed, and the
+        settings the prediction was made with. Leave it as it is unless it is empty.
+      render: text

.gitignore

@@ -138,4 +138,5 @@ react-app/node_modules/
 data/
 backend/data/
 .vscode/
-backend/config.json
+backend/config.json
+.playwright-mcp/

README.md

@@ -3,14 +3,37 @@
 Chebifier is a tool for automated classification of chemicals in the [ChEBI](https://www.ebi.ac.uk/chebi/) ontology. This repository only hosts the front end of Chebifier. For the classification itself, see [python-chebifier](https://github.com/ChEB-AI/python-chebifier).
 
 ## News
+- 2026/08/18: Recalibrated ensemble (with ~500 new classes), added new deep learning models (v252) and model attributions. Now supports InChI input, user feedback, and extended ensemble settings.
 - 2026/02/16: Added Lopster and new deep learning models.
 - 2025/11/11: Fixed processing error for GNNs.
 - 2025/11/05: Added new models (v244, including GAT, 3-STAR models and augmented GNNs), redesigned frontend.
-- 2025/10/01: Fixed issue where server crashed if running predict without adding a SMILES string.
-- 2025/10/01: Improved loading times significantly by only passing ChEBI-related information when needed.
 
 ## Installation
 
+### Setup Frontend
+
+Change to the respective directory and build the node.js files
+```
+cd react-app
+npm run build
+```
+
+### Run in development
+
+You can now start the development server with
+
+```
+cd backend
+flask run
+```
+
+The server should now run at [localhost:5000](localhost:5000), serving both the API and the built
+frontend. Start it from the `backend` directory - the configuration and `data/disjoint_*.csv` are
+looked up relative to the working directory. The first startup may take a bit longer: the ChEBI graph, the model
+checkpoints and the ChEBI lookup table are all loaded up front.
+
+The backend has to run in an environment that has `chebifier` installed.
+
 ### Setup Backend
 
 Some dependencies require that pytorch is already installed:
@@ -21,37 +44,28 @@ After that, you can install the prediction system and web framework:
 
 `pip install -r backend/requirements.txt`
 
-*Chebifier* comes with a number of mandatory configuration files. `config.template.json` contains a template for a *Chebifier* configuration. Copy the contents of this file 
+`config.template.json` contains a template for a *Chebifier* configuration. Copy the contents of this file 
 
 `cp backend/config.template.json backend/config.json`
 
-and change the path for each setting according to your setup.
-
-The ensemble can take any models that are implemented in [python-chebifier](https://github.com/ChEB-AI/python-chebifier). See the repository for example configurations. Common arguments for a model are:
- * `type`: one of the available [MODEL_TYPES](https://github.com/ChEB-AI/python-chebifier/blob/dev/chebifier/model_registry.py), e.g. `electra`,
- * `batch_size`: Number of molecules that are passed to the model at once,
- * `classwise_weights_path` (optional): Weights that should be assigned to each class (i.e., trust scores calculated on a validation set with [this script](https://github.com/ChEB-AI/python-chebai/blob/dev/chebai/result/generate_class_properties.py)
-
+and change the path for each setting according to your setup. An example configuration is available on [Hugging Face](https://huggingface.co/datasets/chebai/chebifier/blob/main/web_assets/config.json). Note that this does not touch the actual ensemble behaviour. For the ensemble, see [python-chebifier](https://github.com/ChEB-AI/python-chebifier). 
 
+To use the precision/recall sliders, you can download the csv table from [Hugging Face](https://huggingface.co/datasets/chebai/chebifier/blob/main/web_assets/pr_curve.csv) and set the "PR_CURVE" parameter in the config file. 
 
-### Setup Frontend
-
-Change to the respective directory and build the node.js files
-```
-cd react-app
-npm run build
-```
-
-### Run in development
+### Windows: enable long paths
 
-You can now start the development server with 
+On the first startup the model checkpoints are downloaded from Hugging Face into the local cache. Some
+checkpoint paths exceed Windows' default 260-character `MAX_PATH` limit, which makes the download fail
+with a `FileNotFoundError` during `build_base_learners`. Enable long-path support once, in an
+**Administrator** PowerShell:
 
-```
-cd backend
-flask run
+```powershell
+Set-ItemProperty -Path 'HKLM:\SYSTEM\CurrentControlSet\Control\FileSystem' -Name LongPathsEnabled -Value 1 -Type DWord
 ```
 
-The server should now run at [localhost:5000](localhost:5000)
+Then start a fresh terminal (the flag is read at process startup) before running the backend again; a
+reboot may be needed if a fresh shell still fails. Python 3.6+ is long-path-aware, so no code or cache
+changes are required.
 
 ## Citation