server.py

"""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}