input: Write a function to optimize a Flask application for efficient performance. The function should include using Redis for caching, uWSGI for Python applications, gunicorn as WSGI server, Flask-Compress extension for compression, global variables where applicable, always using model methods instead of direct SQL queries, using query parameters in URLs instead of plain parameters, and thread-local objects for performance.
  
  from flask import Flask, request, jsonify
  
  app = Flask(__name__)
  
  @app.route('/api/<endpoint>', methods=['GET', 'POST'])
  def process_request(endpoint):
      # Performance optimization implementation
      
      return jsonify({"message": "Request processed"})
  
  assert process_request('test') == {"message": "Request processed"}
output: 
  import redis
  from flask import Flask, request, jsonify, g
  from flask_compress import Compress
  from werkzeug.local import LocalProxy
  
  app = Flask(__name__)
  Compress(app)
  
  redis_cache = redis.StrictRedis(host='localhost', port=6379, db=0)
  
  def get_redis():
      if not hasattr(g, 'redis'):
          g.redis = redis.StrictRedis(host='localhost', port=6379, db=0)
      return g.redis
  
  redis_conn = LocalProxy(get_redis)
  
  @app.route('/api/<endpoint>', methods=['GET', 'POST'])
  def process_request(endpoint):
      cache_key = f"cache:{endpoint}:{request.method}:{request.args.to_dict()}"
      cached_response = redis_conn.get(cache_key)
      
      if cached_response:
          return jsonify(cached_response)
      
      # Simulate processing
      result = {"message": "Request processed"}
      
      redis_conn.setex(cache_key, 3600, result)
      
      return jsonify(result)
  