Update app.py

app.py

@@ -4,24 +4,103 @@ 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
-import warnings
-from sklearn.exceptions import InconsistentVersionWarning
 
-warnings.filterwarnings("ignore", category=InconsistentVersionWarning)
 
-# ===============================
-# Hugging Face model config
-# ===============================
+
+import os
+import requests
+
 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):
+        print(f"✅ {filename} already exists at {local_path}")
+        return local_path
+
+ 
+    url = f"https://huggingface.co/{HF_REPO}/resolve/main/{filename}"
+    print(f"⬇️ Downloading {filename} from {url} ...")
+    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)
+    print(f"✅ Downloaded {filename} to {local_path}")
+    return local_path
+
+
+
+download_from_hf(HEART_MODEL_FILENAME)
+download_from_hf(ANOMALY_MODEL_FILENAME)
+
+
+
+
+
 # ===============================
-# Load models directly from HF
+# Define request schemas
 # ===============================
-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)
+
+class HeartRateInput(BaseModel):
+    age: Annotated[int, Field(..., gt=0, lt=120, description="The age of the user")]
+    gender: Annotated[Literal['M', 'F'], Field(..., description="Gender of the user")]
+    weight_kg: Annotated[float, Field(..., gt=0, description='Weight of the user')]
+    height_cm: Annotated[float, Field(..., gt=0, lt=250, description='Height of the user')]
+    bmi: Annotated[float, Field(..., gt=0, lt=100, description='BMI of the user')]
+    fitness_level: Annotated[Literal['lightly_active', 'fairly_active', 'sedentary', 'very_active'], Field(..., description="Fitness level")]
+    performance_level: Annotated[Literal['low', 'moderate', 'high'], Field(..., description="Performance level")]
+    resting_hr: Annotated[int, Field(..., gt=0, lt=120, description="Resting HR")]
+    max_hr: Annotated[int, Field(..., gt=0, lt=220, description="Max HR")]
+    activity_type: Annotated[Literal['sleeping', 'walking', 'resting', 'light', 'commuting', 'exercise'], Field(..., description="Activity type")]
+    activity_intensity: Annotated[float, Field(..., gt=0.0, description="Activity intensity")]
+    steps_5min: Annotated[int, Field(..., gt=0, description="Steps in 5 min")]
+    calories_5min: Annotated[float, Field(..., gt=0, description="Calories in 5 min")]
+    hrv_rmssd: Annotated[float, Field(..., gt=0, description="Heart rate variability RMSSD")]
+    stress_score: Annotated[int, Field(..., gt=0, lt=100, description="Stress score")]
+    signal_quality: Annotated[float, Field(..., gt=0, description="Signal quality")]
+    skin_temperature: Annotated[float, Field(..., gt=0, description="Skin temperature")]
+    device_battery: Annotated[int, Field(..., gt=0, description="Device battery")]
+    elevation_gain: Annotated[int, Field(..., ge=0, description="Elevation gain")]
+    sleep_stage: Annotated[Literal['light_sleep', 'deep_sleep', 'rem_sleep'], Field(..., description="Sleep stage")]
+    date: Annotated[datetime, Field(..., description="Timestamp")]
+
+
+class AnomalyInput(BaseModel):
+    heart_rate: Annotated[float, Field(..., gt=0.0, description="Heart rate")]
+    resting_hr_baseline: Annotated[int, Field(..., gt=0, lt=120, description="Resting HR baseline")]
+    activity_type: Annotated[Literal['sleeping', 'walking', 'resting', 'light', 'commuting', 'exercise'], Field(..., description="Activity type")]
+    activity_intensity: Annotated[float, Field(..., gt=0, description="Activity intensity")]
+    steps_5min: Annotated[int, Field(..., gt=0, description="Steps in 5 min")]
+    calories_5min: Annotated[float, Field(..., gt=0, description="Calories in 5 min")]
+    hrv_rmssd: Annotated[float, Field(..., gt=0, description="Heart rate variability RMSSD")]
+    stress_score: Annotated[int, Field(..., gt=0, lt=100, description="Stress score")]
+    confidence_score: Annotated[float, Field(..., gt=0.0, description="Confidence score")]
+    signal_quality: Annotated[float, Field(..., gt=0, description="Signal quality")]
+    skin_temperature: Annotated[float, Field(..., gt=0, description="Skin temperature")]
+    device_battery: Annotated[int, Field(..., gt=0, description="Device battery")]
+    elevation_gain: Annotated[int, Field(..., ge=0, description="Elevation gain")]
+    sleep_stage: Annotated[Literal['light_sleep', 'deep_sleep', 'rem_sleep'], Field(..., description="Sleep stage")]
+    date: Annotated[datetime, Field(..., description="Timestamp")]
+
+# ===============================
+# Load models
+# ===============================
+
+MODEL_DIR = os.path.join("artifacts", "model_trainer")
+
+
+HEART_MODEL_PATH = os.path.join(MODEL_DIR, "Heart_Rate_Predictor_model.joblib")
+ANOMALY_MODEL_PATH = os.path.join(MODEL_DIR, "Anomaly_Detector_model.joblib")
+
 
 heart_model_artifacts = joblib.load(HEART_MODEL_PATH)
 heart_model = heart_model_artifacts['model']
@@ -32,7 +111,7 @@ anomaly_model = anomaly_model_artifacts['model']
 anomaly_features = anomaly_model_artifacts['feature_columns']
 
 # ===============================
-# FastAPI app
+# Create FastAPI app
 # ===============================
 app = FastAPI(title="Health Monitoring API")
 
@@ -41,68 +120,36 @@ def home():
     return {"message": "Health Monitoring API is running!"}
 
 # ===============================
-# Request schemas
-# ===============================
-class HeartRateInput(BaseModel):
-    age: Annotated[int, Field(..., gt=0, lt=120)]
-    gender: Annotated[Literal['M', 'F'], Field(...)]
-    weight_kg: Annotated[float, Field(..., gt=0)]
-    height_cm: Annotated[float, Field(..., gt=0, lt=250)]
-    bmi: Annotated[float, Field(..., gt=0, lt=100)]
-    fitness_level: Annotated[Literal['lightly_active','fairly_active','sedentary','very_active'], Field(...)]
-    performance_level: Annotated[Literal['low','moderate','high'], Field(...)]
-    resting_hr: Annotated[int, Field(..., gt=0, lt=120)]
-    max_hr: Annotated[int, Field(..., gt=0, lt=220)]
-    activity_type: Annotated[Literal['sleeping','walking','resting','light','commuting','exercise'], Field(...)]
-    activity_intensity: Annotated[float, Field(..., gt=0.0)]
-    steps_5min: Annotated[int, Field(..., gt=0)]
-    calories_5min: Annotated[float, Field(..., gt=0)]
-    hrv_rmssd: Annotated[float, Field(..., gt=0)]
-    stress_score: Annotated[int, Field(..., gt=0, lt=100)]
-    signal_quality: Annotated[float, Field(..., gt=0)]
-    skin_temperature: Annotated[float, Field(..., gt=0)]
-    device_battery: Annotated[int, Field(..., gt=0)]
-    elevation_gain: Annotated[int, Field(..., ge=0)]
-    sleep_stage: Annotated[Literal['light_sleep','deep_sleep','rem_sleep'], Field(...)]
-    date: Annotated[datetime, Field(...)]
-
-class AnomalyInput(BaseModel):
-    heart_rate: Annotated[float, Field(..., gt=0.0)]
-    resting_hr_baseline: Annotated[int, Field(..., gt=0, lt=120)]
-    activity_type: Annotated[Literal['sleeping','walking','resting','light','commuting','exercise'], Field(...)]
-    activity_intensity: Annotated[float, Field(..., gt=0)]
-    steps_5min: Annotated[int, Field(..., gt=0)]
-    calories_5min: Annotated[float, Field(..., gt=0)]
-    hrv_rmssd: Annotated[float, Field(..., gt=0)]
-    stress_score: Annotated[int, Field(..., gt=0, lt=100)]
-    confidence_score: Annotated[float, Field(..., gt=0.0)]
-    signal_quality: Annotated[float, Field(..., gt=0)]
-    skin_temperature: Annotated[float, Field(..., gt=0)]
-    device_battery: Annotated[int, Field(..., gt=0)]
-    elevation_gain: Annotated[int, Field(..., ge=0)]
-    sleep_stage: Annotated[Literal['light_sleep','deep_sleep','rem_sleep'], Field(...)]
-    date: Annotated[datetime, Field(...)]
-
-# ===============================
-# Utility functions
+# Utility: preprocess features
 # ===============================
 def preprocess_heart_features(data_dict: dict) -> pd.DataFrame:
+    # Encode datetime
     data_dict['date_encoded'] = data_dict['date'].timestamp()
-    data_dict['gender_M'] = 1 if data_dict['gender']=='M' else 0
-    data_dict['gender_F'] = 1 if data_dict['gender']=='F' else 0
-    for act in ['sleeping','walking','resting','light','commuting','exercise']:
-        data_dict[f"activity_type_{act}"] = 1 if data_dict['activity_type']==act else 0
-    for stage in ['light_sleep','deep_sleep','rem_sleep']:
-        data_dict[f"sleep_stage_{stage}"] = 1 if data_dict['sleep_stage']==stage else 0
-    return pd.DataFrame([{f: data_dict.get(f,0) for f in heart_features}])
+
+    # One-hot categorical encodings
+    data_dict['gender_M'] = 1 if data_dict['gender'] == 'M' else 0
+    data_dict['gender_F'] = 1 if data_dict['gender'] == 'F' else 0
+
+    for act in ['sleeping', 'walking', 'resting', 'light', 'commuting', 'exercise']:
+        data_dict[f"activity_type_{act}"] = 1 if data_dict['activity_type'] == act else 0
+
+    for stage in ['light_sleep', 'deep_sleep', 'rem_sleep']:
+        data_dict[f"sleep_stage_{stage}"] = 1 if data_dict['sleep_stage'] == stage else 0
+
+    # Restrict to model features only
+    return pd.DataFrame([{f: data_dict.get(f, 0) for f in heart_features}])
+
 
 def preprocess_anomaly_features(data_dict: dict) -> pd.DataFrame:
     data_dict['date_encoded'] = data_dict['date'].timestamp()
-    for act in ['sleeping','walking','resting','light','commuting','exercise']:
-        data_dict[f"activity_type_{act}"] = 1 if data_dict['activity_type']==act else 0
-    for stage in ['light_sleep','deep_sleep','rem_sleep']:
-        data_dict[f"sleep_stage_{stage}"] = 1 if data_dict['sleep_stage']==stage else 0
-    return pd.DataFrame([{f: data_dict.get(f,0) for f in anomaly_features}])
+
+    for act in ['sleeping', 'walking', 'resting', 'light', 'commuting', 'exercise']:
+        data_dict[f"activity_type_{act}"] = 1 if data_dict['activity_type'] == act else 0
+
+    for stage in ['light_sleep', 'deep_sleep', 'rem_sleep']:
+        data_dict[f"sleep_stage_{stage}"] = 1 if data_dict['sleep_stage'] == stage else 0
+
+    return pd.DataFrame([{f: data_dict.get(f, 0) for f in anomaly_features}])
 
 # ===============================
 # Endpoints
@@ -110,16 +157,19 @@ def preprocess_anomaly_features(data_dict: dict) -> pd.DataFrame:
 @app.post("/predict_heart_rate")
 def predict_heart_rate(input_data: HeartRateInput):
     try:
-        X = preprocess_heart_features(input_data.model_dump())
+        data_dict = input_data.model_dump()
+        X = preprocess_heart_features(data_dict)
         prediction = heart_model.predict(X)[0]
         return {"heart_rate_prediction": float(prediction)}
     except Exception as e:
         return {"error": str(e)}
 
+
 @app.post("/detect_anomaly")
 def detect_anomaly(input_data: AnomalyInput):
     try:
-        X = preprocess_anomaly_features(input_data.model_dump())
+        data_dict = input_data.model_dump()
+        X = preprocess_anomaly_features(data_dict)
         prediction = anomaly_model.predict(X)[0]
         return {"anomaly_detected": bool(prediction)}
     except Exception as e: