[Feature] Biotransformer in enviPath (#364)

Co-authored-by: Tim Lorsbach <tim@lorsba.ch>
Reviewed-on: enviPath/enviPy#364

biotransformer/__init__.py

@@ -0,0 +1,112 @@
+import enum
+from datetime import datetime
+from typing import List
+
+import requests
+from django.conf import settings as s
+
+# Once stable these will be exposed by enviPy-plugins lib
+from envipy_additional_information import EnviPyModel, UIConfig, WidgetType  # noqa: I001
+from envipy_additional_information import register  # noqa: I001
+
+from bridge.contracts import Classifier  # noqa: I001
+from bridge.dto import (
+    BuildResult,
+    EnviPyDTO,
+    EvaluationResult,
+    RunResult,
+    TransformationProductPrediction,
+)  # noqa: I001
+
+
+class BiotransformerEnvType(enum.Enum):
+    CYP450 = "CYP450"
+    ALLHUMAN = "ALLHUMAN"
+    ECBASED = "ECBASED"
+    HGUT = "HGUT"
+    PHASEII = "PHASEII"
+    ENV = "ENV"
+
+
+@register("biotransformerconfig")
+class BiotransformerConfig(EnviPyModel):
+    env_type: BiotransformerEnvType
+
+    class UI:
+        title = "Biotransformer Type"
+        env_type = UIConfig(widget=WidgetType.SELECT, label="Biotransformer Type", order=1)
+
+
+class Biotransformer(Classifier):
+    Config = BiotransformerConfig
+
+    def __init__(self, config: BiotransformerConfig | None = None):
+        super().__init__(config)
+        self.url = f"{s.BIOTRANSFORMER_URL}/biotransformer"
+
+    @classmethod
+    def requires_rule_packages(cls) -> bool:
+        return False
+
+    @classmethod
+    def requires_data_packages(cls) -> bool:
+        return False
+
+    @classmethod
+    def identifier(cls) -> str:
+        return "biotransformer3"
+
+    @classmethod
+    def name(cls) -> str:
+        return "Biotransformer 3.0"
+
+    @classmethod
+    def display(cls) -> str:
+        return "Biotransformer 3.0"
+
+    def build(self, eP: EnviPyDTO, *args, **kwargs) -> BuildResult | None:
+        return
+
+    def run(self, eP: EnviPyDTO, *args, **kwargs) -> RunResult:
+        smiles = [c.smiles for c in eP.get_compounds()]
+        preds = self._post(smiles)
+
+        results = []
+
+        for substrate in preds.keys():
+            results.append(
+                TransformationProductPrediction(
+                    substrate=substrate,
+                    products=preds[substrate],
+                )
+            )
+
+        return RunResult(
+            producer=eP.get_context().url,
+            description=f"Generated at {datetime.now()}",
+            result=results,
+        )
+
+    def evaluate(self, eP: EnviPyDTO, *args, **kwargs) -> EvaluationResult:
+        pass
+
+    def build_and_evaluate(self, eP: EnviPyDTO, *args, **kwargs) -> EvaluationResult:
+        pass
+
+    def _post(self, smiles: List[str]) -> dict[str, dict[str, float]]:
+        data = {"substrates": smiles, "mode": self.config.env_type.value}
+        res = requests.post(self.url, json=data)
+
+        res.raise_for_status()
+
+        # Example Response JSON:
+        # {
+        #     'products': {
+        #         'CN1C=NC2=C1C(=O)N(C(=O)N2C)C': {
+        #             'CN1C2=C(C(=O)N(C)C1=O)NC=N2': 0.5,
+        #             'CN1C=NC2=C1C(=O)N(C)C(=O)N2.CN1C=NC2=C1C(=O)NC(=O)N2C.CO': 0.5
+        #         }
+        #     }
+        # }
+
+        return res.json()["products"]