input: Write a function to optimize a Flask application for efficient performance. The function should set up a Flask app with the necessary performance optimizations, such as using Redis for caching, uWSGI and gunicorn as WSGI servers, Flask-Compress for compression, and other best practices. The function should also include a route that processes requests and uses the mentioned optimizations.
  from flask import Flask, request, jsonify
  
  app = Flask(__name__)
  
  @app.route('/api/<endpoint>', methods=['GET', 'POST'])
  def process_request(endpoint):
      # Performance optimization
      # Use Redis for caching session and query performance
      # Use uWSGI for Python applications
      # Use gunicorn as WSGI server
      # Use Flask-Compress extension for compression
      # Use global variables where applicable
      # Don't use sql queries directly, always use model methods
      # Use query parameter in URLs instead of plain parameters
      # Use thread-local objects for performance
  
      # Your code here
      return jsonify({"status": "processed"})
  
  assert app.route('/api/test', methods=['GET'])==jsonify({"status": "processed"})
  assert app.route('/api/test', methods=['POST'])==jsonify({"status": "processed"})
output: 
  from flask import Flask, request, jsonify
  from flask_compress import Compress
  import redis
  
  app = Flask(__name__)
  Compress(app)
  
  # Set up Redis for caching
  redis_cache = redis.StrictRedis(host='localhost', port=6379, db=0)
  
  @app.route('/api/<endpoint>', methods=['GET', 'POST'])
  def process_request(endpoint):
      # Check Redis cache for the endpoint
      cached_response = redis_cache.get(endpoint)
      if cached_response:
          return jsonify(cached_response)
  
      # Process the request
      response = {"status": "processed"}
  
      # Cache the response in Redis
      redis_cache.set(endpoint, response)
  
      return jsonify(response)
  