File size: 2,280 Bytes
5feba25 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | import os
from pathlib import Path
from dotenv import load_dotenv
# Load environment
load_dotenv()
# Paths - robust for both local and HF Spaces deployment
BASE_DIR = Path(__file__).parent.parent
# Try multiple possible model locations
_possible_paths = [
BASE_DIR / "models" / "GradientBoosting_model.pkl", # Local development
BASE_DIR / "model" / "GradientBoosting_model.pkl", # If named 'model' instead
Path("/app/models/GradientBoosting_model.pkl"), # HF Spaces absolute path
Path.cwd() / "models" / "GradientBoosting_model.pkl", # Current working directory
]
MODEL_PATH = None
for path in _possible_paths:
if path.exists():
MODEL_PATH = path
break
# Default to first path if none found (will error gracefully)
if MODEL_PATH is None:
MODEL_PATH = _possible_paths[0]
# Groq API configuration
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
GROQ_MODEL = "llama-3.1-70b-versatile"
GROQ_TEMPERATURE = 0
GROQ_TIMEOUT = 30
# Medical features (all 16)
DEFAULT_MODEL_FEATURES = [
"LengthOfStay",
"Smoking",
"Family History",
"HbA1c",
"Glucose",
"Age",
"Diet Score",
"Alcohol",
"Physical Activity",
"Blood Pressure",
"BMI",
"Cholesterol",
"Sleep Hours",
"Stress Level",
"Triglycerides",
"Oxygen Saturation"
]
# Feature validation ranges (min, max, expected type)
FEATURE_RANGES = {
"Age": (0, 150, float),
"Glucose": (70, 400, float),
"HbA1c": (3, 15, float),
"BMI": (10, 60, float),
"Cholesterol": (100, 400, float),
"Triglycerides": (20, 500, float),
"Blood Pressure": (60, 200, float),
"Physical Activity": (0, 24, float),
"Sleep Hours": (0, 24, float),
"Stress Level": (1, 10, float),
"Diet Score": (1, 10, float),
"Smoking": (0, 1, int),
"Alcohol": (0, 1, int),
"Family History": (0, 1, int),
"LengthOfStay": (0, 365, int),
"Oxygen Saturation": (80, 100, float),
}
# Model configuration
MIN_FEATURES_FOR_PREDICTION = 16 # Ask for all 16 features
MAX_RETRIES = 3
# Prediction classes
CLASS_NAMES = [
"Arthritis",
"Asthma",
"Cancer",
"Diabetes",
"Healthy",
"Hypertension",
"Obesity",
"Other/Unknown",
]
# App configuration
DEBUG_MODE = True
CONVERSATION_MAX_TURNS = 20
|