From dedf458a8a123b47a615d405411581fcd05abf56 Mon Sep 17 00:00:00 2001
From: Kye Gomez <swarms_wd@Kyes-MacBook-Pro.local>
Date: Tue, 2 Jul 2024 15:05:42 -0700
Subject: [PATCH] [CLEANUP]

---
 new_workflow_concurrent.py | 137 -------------------------------------
 1 file changed, 137 deletions(-)
 delete mode 100644 new_workflow_concurrent.py

diff --git a/new_workflow_concurrent.py b/new_workflow_concurrent.py
deleted file mode 100644
index 67f86cdc..00000000
--- a/new_workflow_concurrent.py
+++ /dev/null
@@ -1,137 +0,0 @@
-import threading
-from dataclasses import dataclass, field
-from typing import Callable, List, Optional, Any
-
-from swarms.utils.logger import logger
-from swarms.structs.agent import Agent
-from swarms.structs.base_workflow import BaseWorkflow
-from swarms import OpenAIChat
-import os
-
-
-@dataclass
-class ConcurrentWorkflow(BaseWorkflow):
-    """
-    ConcurrentWorkflow class for running a set of tasks concurrently using N number of autonomous agents.
-
-    Args:
-        max_workers (int): The maximum number of workers to use for the threading.Thread.
-        autosave (bool): Whether to save the state of the workflow to a file. Default is False.
-        saved_state_filepath (str): The filepath to save the state of the workflow to. Default is "runs/concurrent_workflow.json".
-        print_results (bool): Whether to print the results of each task. Default is False.
-        return_results (bool): Whether to return the results of each task. Default is False.
-        use_processes (bool): Whether to use processes instead of threads. Default is False.
-
-    Examples:
-    >>> from swarms.models import OpenAIChat
-    >>> from swarms.structs import ConcurrentWorkflow
-    >>> llm = OpenAIChat(openai_api_key="")
-    >>> workflow = ConcurrentWorkflow(max_workers=5, agents=[llm])
-    >>> workflow.run()
-    """
-
-    max_loops: int = 1
-    max_workers: int = 5
-    autosave: bool = False
-    agents: List[Agent] = field(default_factory=list)
-    saved_state_filepath: Optional[str] = "runs/concurrent_workflow.json"
-    print_results: bool = True  # Modified: Set print_results to True
-    return_results: bool = False
-    stopping_condition: Optional[Callable] = None
-
-    def run(
-        self, task: Optional[str] = None, *args, **kwargs
-    ) -> Optional[List[Any]]:
-        """
-        Executes the tasks in parallel using multiple threads.
-
-        Args:
-            task (Optional[str]): A task description if applicable.
-            *args: Additional arguments.
-            **kwargs: Additional keyword arguments.
-
-        Returns:
-            Optional[List[Any]]: A list of the results of each task, if return_results is True. Otherwise, returns None.
-        """
-        loop = 0
-        results = []
-
-        while loop < self.max_loops:
-            if not self.agents:
-                logger.warning("No agents found in the workflow.")
-                break
-
-            threads = [
-                threading.Thread(
-                    target=self.execute_agent, args=(agent, task)
-                )
-                for agent in self.agents
-            ]
-
-            for thread in threads:
-                thread.start()
-
-            for thread in threads:
-                thread.join()
-
-            if self.return_results:
-                results.extend(
-                    [
-                        thread.result
-                        for thread in threads
-                        if hasattr(thread, "result")
-                    ]
-                )
-
-            loop += 1
-
-            if self.stopping_condition and self.stopping_condition(
-                results
-            ):
-                break
-
-        return results if self.return_results else None
-
-    def list_agents(self):
-        """Prints a list of the agents in the workflow."""
-        for agent in self.agents:
-            logger.info(agent)
-
-    def save(self):
-        """Saves the state of the workflow to a file."""
-        self.save_state(self.saved_state_filepath)
-
-    def execute_agent(
-        self, agent: Agent, task: Optional[str] = None, *args, **kwargs
-    ):
-        try:
-            result = agent.run(task, *args, **kwargs)
-            if self.print_results:
-                logger.info(f"Agent {agent}: {result}")
-            if self.return_results:
-                return result
-        except Exception as e:
-            logger.error(f"Agent {agent} generated an exception: {e}")
-
-
-api_key = os.environ["OPENAI_API_KEY"]
-
-# Model
-swarm = ConcurrentWorkflow(
-    agents=[
-        Agent(
-            llm=OpenAIChat(
-                openai_api_key=api_key,
-                max_tokens=4000,
-            ),
-            max_loops=4,
-            dashboard=False,
-        )
-    ],
-)
-
-
-# Run the workflow
-swarm.run(
-    "Generate a report on the top 3 biggest expenses for small businesses and how businesses can save 20%"
-)