| """Server that will listen for GET requests from the client.""" |
| from fastapi import FastAPI |
| from joblib import load |
| from concrete.ml.deployment import FHEModelServer |
| from pydantic import BaseModel |
| import base64 |
| from pathlib import Path |
|
|
| current_dir = Path(__file__).parent |
|
|
| |
| fhe_model_HLM = FHEModelServer( |
| Path.joinpath(current_dir, "deployment/deployment_0") |
| ) |
| fhe_model_MDR1MDCK = FHEModelServer( |
| Path.joinpath(current_dir, "deployment/deployment_1") |
| ) |
| fhe_model_SOLUBILITY = FHEModelServer( |
| Path.joinpath(current_dir, "deployment/deployment_2") |
| ) |
| fhe_model_PROTEIN_BINDING_HUMAN = FHEModelServer( |
| Path.joinpath(current_dir, "deployment/deployment_3") |
| ) |
| fhe_model_PROTEIN_BINDING_RAT = FHEModelServer( |
| Path.joinpath(current_dir, "deployment/deployment_4") |
| ) |
| fhe_model_RLM_CLint = FHEModelServer( |
| Path.joinpath(current_dir, "deployment/deployment_5") |
| ) |
|
|
|
|
| class PredictRequest(BaseModel): |
| evaluation_key: str |
| encrypted_encoding: str |
|
|
|
|
| |
| app = FastAPI() |
|
|
|
|
| |
| @app.get("/") |
| def root(): |
| return {"message": "Welcome to Your Molecular Property prediction FHE Server!"} |
|
|
|
|
| @app.post("/predict_HLM") |
| def predict_HLM(query: PredictRequest): |
| encrypted_encoding = base64.b64decode(query.encrypted_encoding) |
| evaluation_key = base64.b64decode(query.evaluation_key) |
| prediction = fhe_model_HLM.run(encrypted_encoding, evaluation_key) |
|
|
| |
| encoded_prediction = base64.b64encode(prediction).decode() |
| return {"encrypted_prediction": encoded_prediction} |
|
|
| @app.post("/predict_MDR1MDCK") |
| def predict_MDR1MDCK(query: PredictRequest): |
| encrypted_encoding = base64.b64decode(query.encrypted_encoding) |
| evaluation_key = base64.b64decode(query.evaluation_key) |
| prediction = fhe_model_MDR1MDCK.run(encrypted_encoding, evaluation_key) |
|
|
| |
| encoded_prediction = base64.b64encode(prediction).decode() |
| return {"encrypted_prediction": encoded_prediction} |
|
|
| @app.post("/predict_SOLUBILITY") |
| def predict_SOLUBILITY(query: PredictRequest): |
| encrypted_encoding = base64.b64decode(query.encrypted_encoding) |
| evaluation_key = base64.b64decode(query.evaluation_key) |
| prediction = fhe_model_SOLUBILITY.run(encrypted_encoding, evaluation_key) |
|
|
| |
| encoded_prediction = base64.b64encode(prediction).decode() |
| return {"encrypted_prediction": encoded_prediction} |
|
|
| @app.post("/predict_PROTEIN_BINDING_HUMAN") |
| def predict_PROTEIN_BINDING_HUMAN(query: PredictRequest): |
| encrypted_encoding = base64.b64decode(query.encrypted_encoding) |
| evaluation_key = base64.b64decode(query.evaluation_key) |
| prediction = fhe_model_PROTEIN_BINDING_HUMAN.run(encrypted_encoding, evaluation_key) |
|
|
| |
| encoded_prediction = base64.b64encode(prediction).decode() |
| return {"encrypted_prediction": encoded_prediction} |
|
|
|
|
| @app.post("/predict_PROTEIN_BINDING_RAT") |
| def predict_PROTEIN_BINDING_RAT(query: PredictRequest): |
| encrypted_encoding = base64.b64decode(query.encrypted_encoding) |
| evaluation_key = base64.b64decode(query.evaluation_key) |
| prediction = fhe_model_PROTEIN_BINDING_RAT.run(encrypted_encoding, evaluation_key) |
|
|
| |
| encoded_prediction = base64.b64encode(prediction).decode() |
| return {"encrypted_prediction": encoded_prediction} |
|
|
| def predict_RLM_CLint(query: PredictRequest): |
| encrypted_encoding = base64.b64decode(query.encrypted_encoding) |
| evaluation_key = base64.b64decode(query.evaluation_key) |
| prediction = fhe_model_RLM_CLint.run(encrypted_encoding, evaluation_key) |
|
|
| |
| encoded_prediction = base64.b64encode(prediction).decode() |
| return {"encrypted_prediction": encoded_prediction} |
|
|
| @app.post("/predict_RLM_CLint") |
| def predict_RLM_CLint(query: PredictRequest): |
| encrypted_encoding = base64.b64decode(query.encrypted_encoding) |
| evaluation_key = base64.b64decode(query.evaluation_key) |
| prediction = fhe_model_RLM_CLint.run(encrypted_encoding, evaluation_key) |
|
|
| |
| encoded_prediction = base64.b64encode(prediction).decode() |
| return {"encrypted_prediction": encoded_prediction} |
