✨ Add prediction pipeline for random forest classifier

app.py

@@ -1,7 +1,47 @@
-from fastapi import FastAPI
+"""
+This is the main entry point for the FastAPI application.
+The app handles the request to predict toxicity for a list of SMILES strings.
+"""
 
-app = FastAPI()
+#---------------------------------------------------------------------------------------
+# Dependencies and global variable definition
+import os
+from typing import List, Dict, Optional
+from fastapi import FastAPI, Header, HTTPException
+from pydantic import BaseModel, Field
+
+from predict import predict
+
+API_KEY = os.getenv("API_KEY")  # set via Space Secrets
+
+#---------------------------------------------------------------------------------------
+class Request(BaseModel):
+    smiles: List[str] = Field(min_items=1, max_items=1000)
+
+class Response(BaseModel):
+    predictions: dict
+    model_info: Dict[str, str] = {}
+
+app = FastAPI(title="toxicity-api")
+
+@app.get("/metadata")
+def metadata():
+    return {
+        "name": "AwesomeTox",
+        "version": "1.0.0",
+        "max_batch_size": 256,
+        "tox_endpoints": ["mutagenicity","hepatotoxicity"],
+    }
+
+@app.get("/healthz")
+def healthz():
+    return {"ok": True}
+
+@app.post("/predict", response_model=Response)
+def predict(request: Request, authorization: str = Header(default="")):
+    if not API_KEY or authorization != f"Bearer {API_KEY}":
+        raise HTTPException(status_code=401, detail="Unauthorized")
+
+    predictions = predict(request.smiles)
+    return {"predictions": predictions, "model_info": {"name":"random_clf", "version":"1.0.0"}}
 
-@app.get("/")
-def greet_json():
-    return {"Hello": "World!"}

predict.py

@@ -0,0 +1,52 @@
+"""
+This files includes a predict function for the Tox21.
+As an input it takes a list of SMILES and it outputs a nested dictionary with 
+SMILES and target names as keys.
+"""
+
+#---------------------------------------------------------------------------------------
+# Dependencies
+from Literal import List
+import random
+from collections import defaultdict
+#---------------------------------------------------------------------------------------
+class Tox21RandomClassifier():
+    """
+    A random classifier that assigns a random toxicity score to a given SMILES string.  
+    """
+    
+    def __init__(self):
+        
+        self.target_names = [
+            "NR-AR", 
+            "NR-AR-LBD", 
+            "NR-AhR", 
+            "NR-Aromatase", 
+            "NR-ER", 
+            "NR-ER-LBD", 
+            "NR-PPAR-gamma", 
+            "SR-ARE", 
+            "SR-ATAD5", 
+            "SR-HSE", 
+            "SR-MMP", 
+            "SR-p53"
+        ]
+
+    def predict(self, smiles_list:List[str]) -> dict:
+        """
+        Predicts all Tox21 targets for a given list of SMILES strings by assigning
+        random toxicity scores.
+        """
+
+        predictions = defaultdict(dict)
+        for smiles in smiles_list:
+            for target in self.target_names:
+                predictions[smiles][target] = random.random()
+        return predictions
+
+def predict(smiles_list: List[str]) -> dict:
+    """
+    Applies the classifier to a list of SMILES strings.
+    """
+    model = Tox21RandomClassifier()
+    return model.predict(smiles_list)