from fastapi import FastAPI

import numpy as np

import tensorflow as tf

from pydantic import BaseModel

model = tf.keras.models.load_model("heart_monitor_lstm.h5")

scaler_mean = np.load("scaler.npy")

print(scaler_mean)

app = FastAPI()

latest_prediction = {"status": "Waiting...", "confidence": 0.0}

class HeartData(BaseModel):

    heart_rate: float

    spo2: float

@app.post("/predict")

def predict_heart_condition(data: HeartData):

    global latest_prediction

    input_data = np.array([[data.heart_rate, data.spo2]]) - scaler_mean

    input_data = input_data.reshape(1, 1, 2)

    prediction = model.predict(input_data)[0][0]

    result = "Abnormal" if prediction > 0.5 else "Normal"

    latest_prediction = {"status": result, "confidence": round(prediction * 100, 1)}

    return latest_prediction

@app.get("/get_latest_prediction")

def get_latest_prediction():

    return latest_prediction