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from fastapi import FastAPI
import joblib
import pandas as pd
from datetime import datetime
from typing import Literal, Annotated
from pydantic import BaseModel, Field
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)
# ===============================
# Define request schemas
# ===============================
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']
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']
# ===============================
# Create FastAPI app
# ===============================
app = FastAPI(title="Health Monitoring API")
@app.get("/")
def home():
return {"message": "Health Monitoring API is running!"}
# ===============================
# Utility: preprocess features
# ===============================
def preprocess_heart_features(data_dict: dict) -> pd.DataFrame:
# Encode datetime
data_dict['date_encoded'] = data_dict['date'].timestamp()
# 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}])
# ===============================
# Endpoints
# ===============================
@app.post("/predict_heart_rate")
def predict_heart_rate(input_data: HeartRateInput):
try:
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:
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:
return {"error": str(e)}
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