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"""
This is the main entry point for the FastAPI application.
The app handles the request to predict toxicity for a list of SMILES strings.
"""
#---------------------------------------------------------------------------------------
# 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 as predict_func
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("/")
def root():
return {
"message": "Toxicity Prediction API",
"endpoints": {
"/metadata": "GET - API metadata and capabilities",
"/healthz": "GET - Health check",
"/predict": "POST - Predict toxicity for SMILES"
},
"usage": "Send POST to /predict with {'smiles': ['your_smiles_here']} and Authorization header"
}
@app.get("/metadata")
def metadata():
return {
"name": "AwesomeTox",
"version": "1.0.0",
"max_batch_size": 256,
"tox_endpoints": ["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"],
}
@app.get("/healthz")
def healthz():
return {"ok": True}
@app.post("/predict", response_model=Response)
def predict(request: Request):
predictions = predict_func(request.smiles)
return {"predictions": predictions, "model_info": {"name":"random_clf", "version":"1.0.0"}}
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