--- a
+++ b/test.py
@@ -0,0 +1,32 @@
+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