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

@@ -3,6 +3,7 @@
 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.
@@ -28,11 +29,36 @@ After that, you can install the prediction system and web framework:
 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)
-
-
+ * `type`: one of the available [MODEL_TYPES](https://github.com/ChEB-AI/python-chebifier/blob/dev/chebifier/model_registry.py), e.g. `gat`,
+ * `ckpt_path`: path to the model checkpoint (deep learning models only),
+ * `batch_size`: number of molecules that are passed to the model at once,
+ * `calibration_name` (optional): the name the model's calibration files in `ENSEMBLE_DIR` were written under, if it differs from the name shown in the web app,
+ * `model_weight` (optional, default 1): how much the model's votes count, independently of the class. This is the value the "Ensemble settings" sliders start from.
+
+Besides the models, the configuration points at the calibration of the ensemble:
+ * `ENSEMBLE_DIR`: directory holding the calibration written by `chebifier build` (`prediction_thresholds.yaml`, `<model>_classwise_f1.txt`, `best_hyperparameters.csv`). Only the files of the models listed in `MODELS` have to be present - a model without a `_classwise_f1.txt` votes with full trust and a neutral threshold, so its influence is set by its model weight alone.
+ * `ENSEMBLE_CLASSES`: the class list the ensemble was calibrated on, one ChEBI id per line. The class-wise F1 scores are stored positionally, so this list has to match the calibration exactly.
+ * `CHEBI_GRAPH`: the ChEBI graph pickle the calibration was built against. If the file is missing, it is downloaded from [Hugging Face](https://huggingface.co/datasets/chebai/chebifier).
+ * `INCONSISTENCY_RESOLUTION`: `score-based` (the default) or `none`.
+ * `STATS_DB` (optional): SQLite file the number of classified molecules is counted in, shown in the
+   app as "x molecules classified since ...". Only a per-day count is stored, never the molecules.
+   Under uWSGI the request is answered by one of several worker processes, so the count cannot live
+   in memory - the increment is a single upserting statement in a transaction, which concurrent
+   workers cannot lose the way a read-modify-write on a plain file would. Put the file on local
+   disk (SQLite locking is unreliable over NFS) and on a path that survives a deploy. Delete the
+   file to reset the counter.
+ * `PR_CURVE` (optional): a CSV of measured operating points (`threshold`, `full_micro_precision`,
+   `full_micro_recall`), as written by the evaluation grid. It backs the precision/recall sliders in
+   the ensemble settings: the user asks for more precision or more recall, and the app picks the
+   threshold that delivered it on the test set. Without the file the operating point stays fixed at
+   the one the ensemble reports.
+
+### How a prediction is explained
+
+Molecules can be entered as SMILES or InChI strings, mixed freely. An InChI is translated to SMILES
+before it reaches the base learners, and the response carries the SMILES each input was read as.
+
+For each predicted class, the web app reports the ensemble score - a probability in [0, 1], with the class predicted above the ensemble's decision threshold - together with the share of that decision each base learner is responsible for (the shares sum to 1) and the raw 0-1 prediction each model made for the class.
 
 ### Setup Frontend
 
@@ -44,14 +70,22 @@ npm run build
 
 ### Run in development
 
-You can now start the development server with 
+You can now start the development server with
 
 ```
 cd backend
 flask run
 ```
 
-The server should now run at [localhost:5000](localhost:5000)
+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. Startup takes a while: the ChEBI graph, the model
+checkpoints and the SMILES lookup table are all loaded up front.
+
+The backend has to run in an environment that has `chebifier` and all of its base learners
+installed (`chebai`, `chebai-graph`, `chemlog`, `chemlog-extra`, `c3p`). If that environment is a
+virtualenv of the python-chebifier checkout, run flask from it directly, e.g.
+`../../python-chebifier/.venv/Scripts/python -m flask --app app run`.
 
 ## Citation