[CLEANUP]

swarms/structs/concurrent_workflow.py

@@ -3,9 +3,6 @@ from queue import Queue
 from typing import List
 from swarms.structs.agent import Agent
 from swarms.utils.loguru_logger import logger
-from dotenv import load_dotenv
-import os
-from swarms.models.popular_llms import OpenAIChat
 
 
 class ConcurrentWorkflow:
@@ -91,25 +88,25 @@ class ConcurrentWorkflow:
         return None
 
 
-# Load the environment variables
-load_dotenv()
+# # Load the environment variables
+# load_dotenv()
 
-# Get the API key from the environment
-api_key = os.environ.get("OPENAI_API_KEY")
+# # Get the API key from the environment
+# api_key = os.environ.get("OPENAI_API_KEY")
 
-# Initialize the language model (assuming this should be done outside the class and passed to it)
-llm = OpenAIChat(temperature=0.5, openai_api_key=api_key, max_tokens=4000)
+# # Initialize the language model (assuming this should be done outside the class and passed to it)
+# llm = OpenAIChat(temperature=0.5, openai_api_key=api_key, max_tokens=4000)
 
-# Initialize agents
-agents = [
-    Agent(llm=llm, max_loops=1, autosave=True, dashboard=True)
-    for _ in range(1)
-]
+# # Initialize agents
+# agents = [
+#     Agent(llm=llm, max_loops=1, autosave=True, dashboard=True)
+#     for _ in range(1)
+# ]
 
-# Task to be executed by each agent
-task = "Generate a 10,000 word blog on health and wellness."
+# # Task to be executed by each agent
+# task = "Generate a 10,000 word blog on health and wellness."
 
-# Initialize and run the ConcurrentWorkflow
-workflow = ConcurrentWorkflow(agents=agents, max_loops=1)
-result = workflow.run(task)
-logger.info(f"Final result: {result}")
+# # Initialize and run the ConcurrentWorkflow
+# workflow = ConcurrentWorkflow(agents=agents, max_loops=1)
+# result = workflow.run(task)
+# logger.info(f"Final result: {result}")