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import mlflow.sklearn
from flask import Flask, request, jsonify
from joblib import load
import requests
import sys
def download_model(model_url, local_path):
"""Downloads a model from the specified URL and saves it locally."""
try:
response = requests.get(model_url)
with open(local_path, 'wb') as f:
f.write(response.content)
except Exception as e:
print(f"Error downloading model: {e}")
raise # Re-raise the exception for proper handling
def load_model_and_log(model_path):
"""Loads a pre-trained Scikit-learn model from the specified path and logs it with MLflow."""
try:
# Load the model using joblib
model = load(model_path)
# Log the loaded model with MLflow
mlflow.sklearn.log_model(model, "loaded_model")
# Get the active MLflow run (the one you just logged)
active_run = mlflow.active_run()
# Get the run ID
run_id = active_run.info.run_id
print("Run ID:", run_id)
return model, run_id
except Exception as e:
print(f"Error loading model: {e}")
raise # Re-raise the exception for proper handling
def load_model_from_mlflow(run_id):
"""Loads a model from MLflow based on the provided run ID."""
try:
# Load the model from MLflow
loaded_model = mlflow.sklearn.load_model(f"runs:/{run_id}/loaded_model")
return loaded_model
except Exception as e:
print(f"Error loading model from MLflow: {e}")
raise # Re-raise the exception for proper handling
def predict(model):
"""Makes predictions using the provided model."""
try:
input_data = request.get_json()
prediction = model.predict(input_data)
return jsonify({'prediction': prediction.tolist()})
except Exception as e:
print(f"Error making prediction: {e}")
return jsonify({'error': str(e)}), 500 # Internal Server Error
app = Flask(__name__)
# Get model URL from command line argument
model_url = sys.argv[1]
# Path to save the downloaded model locally
local_model_path = "/tmp/model.joblib"
# Download the model from the specified URL
download_model(model_url, local_model_path)
# Load the model on app startup and log it with MLflow
model, run_id = load_model_and_log(local_model_path)
# Load the model from MLflow based on the run ID
loaded_model = load_model_from_mlflow(run_id)
@app.route('/predict', methods=['POST'])
def inference():
return predict(loaded_model)
if __name__ == '__main__':
app.run(host='0.0.0.0', port=5000) |