Upload app.py

app.py

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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)}