Update app.py

app.py

@@ -1,53 +1,40 @@
 from fastapi import FastAPI
-from contextlib import asynccontextmanager
-import joblib, os, requests, pandas as pd
+import joblib
+import pandas as pd
 from datetime import datetime
 from typing import Literal, Annotated
 from pydantic import BaseModel, Field
+from huggingface_hub import hf_hub_download
 
+# ===============================
+# Hugging Face model config
+# ===============================
 HF_REPO = "samithcs/heart-rate-models"
 HEART_MODEL_FILENAME = "Heart_Rate_Predictor_model.joblib"
 ANOMALY_MODEL_FILENAME = "Anomaly_Detector_model.joblib"
-MODEL_DIR = os.path.join("artifacts", "model_trainer")
-os.makedirs(MODEL_DIR, exist_ok=True)
-
-def download_from_hf(filename):
-    local_path = os.path.join(MODEL_DIR, filename)
-    if os.path.exists(local_path):
-        return local_path
-    url = f"https://huggingface.co/{HF_REPO}/resolve/main/{filename}"
-    with requests.get(url, stream=True) as r:
-        r.raise_for_status()
-        with open(local_path, "wb") as f:
-            for chunk in r.iter_content(chunk_size=8192):
-                f.write(chunk)
-    return local_path
 
 # ===============================
-# Lifespan context
+# Load models directly from HF
 # ===============================
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    global heart_model, heart_features, anomaly_model, anomaly_features
+HEART_MODEL_PATH = hf_hub_download(repo_id=HF_REPO, filename=HEART_MODEL_FILENAME)
+ANOMALY_MODEL_PATH = hf_hub_download(repo_id=HF_REPO, filename=ANOMALY_MODEL_FILENAME)
 
-    HEART_MODEL_PATH = download_from_hf(HEART_MODEL_FILENAME)
-    ANOMALY_MODEL_PATH = download_from_hf(ANOMALY_MODEL_FILENAME)
+heart_model_artifacts = joblib.load(HEART_MODEL_PATH)
+heart_model = heart_model_artifacts['model']
+heart_features = heart_model_artifacts['feature_columns']
 
-    heart_model_artifacts = joblib.load(HEART_MODEL_PATH)
-    heart_model = heart_model_artifacts['model']
-    heart_features = heart_model_artifacts['feature_columns']
-
-    anomaly_model_artifacts = joblib.load(ANOMALY_MODEL_PATH)
-    anomaly_model = anomaly_model_artifacts['model']
-    anomaly_features = anomaly_model_artifacts['feature_columns']
-
-    yield
+anomaly_model_artifacts = joblib.load(ANOMALY_MODEL_PATH)
+anomaly_model = anomaly_model_artifacts['model']
+anomaly_features = anomaly_model_artifacts['feature_columns']
 
 # ===============================
 # FastAPI app
 # ===============================
-app = FastAPI(title="Health Monitoring API", lifespan=lifespan)
+app = FastAPI(title="Health Monitoring API")
 
+@app.get("/")
+def home():
+    return {"message": "Health Monitoring API is running!"}
 
 # ===============================
 # Request schemas
@@ -92,9 +79,8 @@ class AnomalyInput(BaseModel):
     sleep_stage: Annotated[Literal['light_sleep','deep_sleep','rem_sleep'], Field(...)]
     date: Annotated[datetime, Field(...)]
 
-
 # ===============================
-# Utility: preprocess features
+# Utility functions
 # ===============================
 def preprocess_heart_features(data_dict: dict) -> pd.DataFrame:
     data_dict['date_encoded'] = data_dict['date'].timestamp()
@@ -117,10 +103,6 @@ def preprocess_anomaly_features(data_dict: dict) -> pd.DataFrame:
 # ===============================
 # Endpoints
 # ===============================
-@app.get("/")
-def home():
-    return {"message":"Health Monitoring API is running!"}
-
 @app.post("/predict_heart_rate")
 def predict_heart_rate(input_data: HeartRateInput):
     try: