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import os
import requests
import joblib
import logging
import zipfile
# Configure logging
logging.basicConfig(level=logging.INFO)
# Get model URLs from environment variables
DIABETES_MODEL_URL = os.getenv("DIABETES_MODEL_URL")
SCALER_URL = os.getenv("SCALER_URL")
MULTI_MODEL_URL = os.getenv("MULTI_MODEL_URL")
# Local paths for downloaded models
MODEL_PATHS = {
"DIABETES_MODEL": "finaliseddiabetes_model.zip",
"SCALER": "finalisedscaler.zip",
"MULTI_MODEL": "nodiabetes.zip",
}
# Extracted model names
EXTRACTED_MODELS = {
"DIABETES_MODEL": "finaliseddiabetes_model.joblib",
"SCALER": "finalisedscaler.joblib",
"MULTI_MODEL": "nodiabetes.joblib",
}
BASE_DIR = os.getcwd() # Get current working directory
def download_model(url, zip_filename):
"""Downloads the model zip file from the given URL and saves it locally."""
zip_path = os.path.join(BASE_DIR, zip_filename)
if not url:
logging.error(f"URL for {zip_filename} is missing!")
return False
try:
response = requests.get(url, allow_redirects=True)
if response.status_code == 200:
with open(zip_path, 'wb') as f:
f.write(response.content)
logging.info(f"Downloaded {zip_filename} successfully.")
return True
else:
logging.error(f"Failed to download {zip_filename}. HTTP Status: {response.status_code}")
return False
except Exception as e:
logging.error(f"Error downloading {zip_filename}: {e}")
return False
def extract_if_needed(zip_filename, extracted_filename):
"""Extracts model file from zip if not already extracted."""
zip_path = os.path.join(BASE_DIR, zip_filename)
extracted_path = os.path.join(BASE_DIR, extracted_filename)
if os.path.exists(extracted_path):
logging.info(f"{extracted_filename} already exists. Skipping extraction.")
return True
if not os.path.exists(zip_path):
logging.error(f"Zip file missing: {zip_path}")
return False
try:
with zipfile.ZipFile(zip_path, 'r') as zip_ref:
zip_ref.extractall(BASE_DIR)
extracted_files = zip_ref.namelist()
logging.info(f"Extracted {zip_filename}, contents: {extracted_files}")
return True
except Exception as e:
logging.error(f"Error extracting {zip_filename}: {e}")
return False
def load_model(model_filename):
"""Loads a model from the given filename."""
model_path = os.path.join(BASE_DIR, model_filename)
if not os.path.exists(model_path):
logging.error(f"Model file not found: {model_path}")
return None
try:
model = joblib.load(model_path)
logging.info(f"Loaded {model_filename} successfully.")
return model
except Exception as e:
logging.error(f"Error loading {model_filename}: {e}")
return None
# **Main Execution**
for model_key, zip_filename in MODEL_PATHS.items():
extracted_filename = EXTRACTED_MODELS[model_key]
# Step 1: Download model if not present
if not os.path.exists(os.path.join(BASE_DIR, zip_filename)):
download_model(globals()[f"{model_key}_URL"], zip_filename)
# Step 2: Extract model file
extract_if_needed(zip_filename, extracted_filename)
# Step 3: Load models
diabetes_model = load_model(EXTRACTED_MODELS["DIABETES_MODEL"])
scaler = load_model(EXTRACTED_MODELS["SCALER"])
multi_model = load_model(EXTRACTED_MODELS["MULTI_MODEL"])
# Final check
if diabetes_model and scaler and multi_model:
logging.info("All models loaded successfully! ✅")
else:
logging.error("Some models failed to load. ❌ Check logs for details.")
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