Change pred and simil endpoints input params to pass 2 list of subjec…

…ts/objects to predict

.gitignore

@@ -1,4 +1,5 @@
 /data
+trapi-predict-kit/
 
 /models/*
 !/models/*.mlem

CONTRIBUTING.md

@@ -110,48 +110,7 @@ If you make changes to the data in the `data` folder you will need to add and pu
 
 ## 📝 Integrate new prediction models
 
-The `openpredict` library provides a decorator `@trapi_predict` to annotate your functions that generate predictions.
-
-Predictions generated from functions decorated with `@trapi_predict` can easily be imported in the Translator OpenPredict API, exposed as an API endpoint to get predictions from the web, and queried through the Translator Reasoner API (TRAPI)
-
-```python
-from trapi_predict_kit import trapi_predict, PredictOptions, PredictOutput
-
-@trapi_predict(path='/predict',
-    name="Get predicted targets for a given entity",
-    description="Return the predicted targets for a given entity: drug (DrugBank ID) or disease (OMIM ID), with confidence scores.",
-    relations=[
-        {
-            'subject': 'biolink:Drug',
-            'predicate': 'biolink:treats',
-            'object': 'biolink:Disease',
-        },
-        {
-            'subject': 'biolink:Disease',
-            'predicate': 'biolink:treated_by',
-            'object': 'biolink:Drug',
-        },
-    ]
-)
-def get_predictions(
-        input_id: str, options: PredictOptions
-    ) -> PredictOutput:
-    # Add the code the load the model and get predictions here
-    predictions = {
-        "hits": [
-            {
-                "id": "DB00001",
-                "type": "biolink:Drug",
-                "score": 0.12345,
-                "label": "Leipirudin",
-            }
-        ],
-        "count": 1,
-    }
-    return predictions
-```
-
-You can use [our cookiecutter template](https://github.com/MaastrichtU-IDS/cookiecutter-openpredict-api/) to quickly bootstrap a repository with everything ready to start developing your prediction models, to then easily publish and integrate them in the Translator ecosystem
+Checkout the [documentation of the `trapi-predict-kit` library](https://maastrichtu-ids.github.io/trapi-predict-kit/getting-started/expose-model) to add new predictions models.
 
 ## 📬 Pull Request process
 

Dockerfile

@@ -45,6 +45,7 @@ ENV MODULE_NAME=trapi.main
 ENV VARIABLE_NAME=app
 
 RUN pip install -e ".[train,test]"
+RUN pip install -e ./trapi-predict-kit
 
 RUN dvc pull -f
 
@@ -55,8 +56,10 @@ EXPOSE 8808
 # Build entrypoint script to pull latest dvc changes before startup
 RUN echo "#!/bin/bash" > /entrypoint.sh && \
     echo "dvc pull" >> /entrypoint.sh && \
+    # echo "pip install -e ." >> /entrypoint.sh && \
     echo "/start.sh" >> /entrypoint.sh && \
     chmod +x /entrypoint.sh
 
 
-CMD [ "/entrypoint.sh" ]
+# CMD [ "/entrypoint.sh" ]
+CMD [ "uvicorn", "trapi.main:app", "--debug", "--reload"]

docker-compose.yml

@@ -24,8 +24,8 @@ services:
     environment:
       PYTHONUNBUFFERED: '1'
       LOG_LEVEL: 'INFO'
-    # entrypoint: pytest --cov=src tests/integration
-    entrypoint:  pytest tests/integration/test_openpredict_api.py
+    entrypoint: pytest --cov=src tests/integration
+    # entrypoint:  pytest tests/integration/test_train_model.py -s
     # entrypoint:  pytest tests/integration/test_openpredict_api.py::test_post_trapi -s
     # entrypoint:  pytest tests/package/test_decorator.py -s
 

models/openpredict_baseline.ttl

@@ -52,27 +52,27 @@
 
 <https://w3id.org/openpredict/openpredict_baseline/accuracy> a mls:EvaluationMeasure ;
     rdfs:label "accuracy" ;
-    mls:hasValue 8.66941e-01 .
+    mls:hasValue 8.704152e-01 .
 
 <https://w3id.org/openpredict/openpredict_baseline/average_precision> a mls:EvaluationMeasure ;
     rdfs:label "average_precision" ;
-    mls:hasValue 8.514183e-01 .
+    mls:hasValue 8.660077e-01 .
 
 <https://w3id.org/openpredict/openpredict_baseline/f1> a mls:EvaluationMeasure ;
     rdfs:label "f1" ;
-    mls:hasValue 7.813454e-01 .
+    mls:hasValue 7.87334e-01 .
 
 <https://w3id.org/openpredict/openpredict_baseline/precision> a mls:EvaluationMeasure ;
     rdfs:label "precision" ;
-    mls:hasValue 8.627594e-01 .
+    mls:hasValue 8.68504e-01 .
 
 <https://w3id.org/openpredict/openpredict_baseline/recall> a mls:EvaluationMeasure ;
     rdfs:label "recall" ;
-    mls:hasValue 7.144006e-01 .
+    mls:hasValue 7.201709e-01 .
 
 <https://w3id.org/openpredict/openpredict_baseline/roc_auc> a mls:EvaluationMeasure ;
     rdfs:label "roc_auc" ;
-    mls:hasValue 8.924977e-01 .
+    mls:hasValue 8.990311e-01 .
 
 <https://w3id.org/openpredict/run/openpredict_baseline> a mls:Run ;
     dc:identifier "openpredict_baseline" ;
@@ -95,7 +95,7 @@
     mls:hasOutput <https://w3id.org/openpredict/openpredict_baseline/Model>,
         <https://w3id.org/openpredict/run/openpredict_baseline/ModelEvaluation> ;
     mls:realizes openpredict:LogisticRegression ;
-    prov:generatedAtTime "2022-12-14T13:05:44.989115"^^xsd:dateTime .
+    prov:generatedAtTime "2023-10-16T10:01:01.382159"^^xsd:dateTime .
 
 <https://w3id.org/openpredict/run/openpredict_baseline/ModelEvaluation> a mls:ModelEvaluation,
         prov:Entity ;

pyproject.toml

@@ -32,7 +32,7 @@ dynamic = ["version"]
 dependencies = [
     "requests >=2.23.0",
     "trapi-predict-kit @ git+https://github.com/MaastrichtU-IDS/trapi-predict-kit.git",
-    # "trapi-predict-kit @ {root:uri}/../trapi-predict-kit",
+    # "trapi-predict-kit @ {root:uri}/trapi-predict-kit",
 
     "pydantic >=1.9",
     "fastapi >=0.68.1",