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be201e7 4b86909 b24bda8 4b86909 182627d 4b86909 b24bda8 182627d b24bda8 182627d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | """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
# Load the model
fhe_model = FHEModelServer("deployment/financial_rating")
fhe_legal_model = FHEModelServer("deployment/legal_rating")
class PredictRequest(BaseModel):
evaluation_key: str
encrypted_encoding: str
# Initialize an instance of FastAPI
app = FastAPI()
# Define the default route
@app.get("/")
def root():
return {"message": "Welcome to Your Sentiment Classification FHE Model Server!"}
@app.post("/predict_sentiment")
def predict_sentiment(query: PredictRequest):
fhe_model = FHEModelServer("deployment/financial_rating")
encrypted_encoding = base64.b64decode(query.encrypted_encoding)
evaluation_key = base64.b64decode(query.evaluation_key)
prediction = fhe_model.run(encrypted_encoding, evaluation_key)
# Encode base64 the prediction
encoded_prediction = base64.b64encode(prediction).decode()
return {"encrypted_prediction": encoded_prediction}
@app.post("/predict_legal")
def predict_legal(query: PredictRequest):
fhe_legal_model = FHEModelServer("deployment/legal_rating")
encrypted_encoding = base64.b64decode(query.encrypted_encoding)
evaluation_key = base64.b64decode(query.evaluation_key)
prediction = fhe_legal_model.run(encrypted_encoding, evaluation_key)
# Encode base64 the prediction
encoded_prediction = base64.b64encode(prediction).decode()
return {"encrypted_prediction": encoded_prediction}
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