[CLEANUP]

swarms/models/zephyr.py

@@ -1,107 +0,0 @@
-"""Zephyr by HF"""
-
-import torch
-from transformers import pipeline
-
-
-class Zephyr:
-    """
-    Zehpyr model from HF
-
-
-    Args:
-        max_new_tokens(int) = Number of max new tokens
-        temperature(float) = temperature of the LLM
-        top_k(float) = top k of the model set to 50
-        top_p(float) = top_p of the model set to 0.95
-
-
-
-    Usage:
-    >>> model = Zephyr()
-    >>> output = model("Generate hello world in python")
-
-
-    """
-
-    def __init__(
-        self,
-        model_name: str = "HuggingFaceH4/zephyr-7b-alpha",
-        tokenize: bool = False,
-        add_generation_prompt: bool = True,
-        system_prompt: str = "You are a friendly chatbot who always responds in the style of a pirate",
-        max_new_tokens: int = 300,
-        temperature: float = 0.5,
-        top_k: float = 50,
-        top_p: float = 0.95,
-        do_sample: bool = True,
-        *args,
-        **kwargs,
-    ):
-        super().__init__()
-        self.model_name = model_name
-        self.tokenize = tokenize
-        self.add_generation_prompt = add_generation_prompt
-        self.system_prompt = system_prompt
-        self.max_new_tokens = max_new_tokens
-        self.temperature = temperature
-        self.top_k = top_k
-        self.top_p = top_p
-        self.do_sample = do_sample
-
-        self.pipe = pipeline(
-            "text-generation",
-            model=self.model_name,
-            torch_dtype=torch.bfloat16,
-            device_map="auto",
-        )
-        self.messages = [
-            {
-                "role": "system",
-                "content": f"{self.system_prompt}\n\nUser:",
-            },
-        ]
-
-    def __call__(self, task: str):
-        """Call the model"""
-        prompt = self.pipe.tokenizer.apply_chat_template(
-            self.messages,
-            tokenize=self.tokenize,
-            add_generation_prompt=self.add_generation_prompt,
-        )
-        outputs = self.pipe(
-            prompt
-        )  # max_new_token=self.max_new_tokens)
-        print(outputs[0]["generated_text"])
-
-    def chat(self, message: str):
-        """
-        Adds a user message to the conversation and generates a chatbot response.
-        """
-        # Add the user message to the conversation
-        self.messages.append({"role": "user", "content": message})
-
-        # Apply the chat template to format the messages
-        prompt = self.pipe.tokenizer.apply_chat_template(
-            self.messages,
-            tokenize=self.tokenize,
-            add_generation_prompt=self.add_generation_prompt,
-        )
-
-        # Generate a response
-        outputs = self.pipe(
-            prompt,
-            max_new_tokens=self.max_new_tokens,
-            do_sample=self.do_sample,
-            temperature=self.temperature,
-            top_k=self.top_k,
-            top_p=self.top_p,
-        )
-
-        # Extract the generated text
-        generated_text = outputs[0]["generated_text"]
-
-        # Optionally, you could also add the chatbot's response to the messages list
-        # However, the below line should be adjusted to extract the chatbot's response only
-        # self.messages.append({"role": "bot", "content": generated_text})
-        return generated_text

swarms/structs/recursive_workflow.py

@@ -36,6 +36,7 @@ class RecursiveWorkflow(BaseStructure):
         stopping_conditions: callable = None,
     ):
         self.stop_token = stop_token
+        self.stopping_conditions = stopping_conditions
         self.task_pool = []
 
         assert (

swarms/structs/sermon_swarm.py

@@ -2,6 +2,7 @@ from typing import Union, Sequence, List, Callable
 from swarms.structs.agent import Agent
 from swarms.structs.base_swarm import AbstractSwarm
 
+
 class SermonSwarm(AbstractSwarm):
     """
     Represents a swarm of agents that communicate through sermons.

swarms/structs/task_queue_base.py

@@ -1,5 +1,5 @@
 import threading
-from abc import ABC, abstractmethod
+from abc import ABC
 
 from swarms.structs.agent import Agent
 from swarms.structs.task import Task
@@ -36,8 +36,8 @@ class TaskQueueBase(ABC):
         self.lock = threading.Lock()
 
     @synchronized_queue
-    @abstractmethod
-    def add_task(self, task: Task) -> bool:
+    # @abstractmethod
+    def add(self, task: Task) -> bool:
         """Adds a task to the queue.
 
         Args:
@@ -46,11 +46,11 @@ class TaskQueueBase(ABC):
         Returns:
             bool: True if the task was successfully added, False otherwise.
         """
-        raise NotImplementedError
+        ...
 
     @synchronized_queue
-    @abstractmethod
-    def get_task(self, agent: Agent) -> Task:
+    # @abstractmethod
+    def get(self, agent: Agent) -> Task:
         """Gets the next task from the queue.
 
         Args:
@@ -59,24 +59,24 @@ class TaskQueueBase(ABC):
         Returns:
             Task: The next task from the queue.
         """
-        raise NotImplementedError
+        ...
 
     @synchronized_queue
-    @abstractmethod
+    # @abstractmethod
     def complete_task(self, task_id: str):
         """Sets the task as completed.
 
         Args:
             task_id (str): The ID of the task to be marked as completed.
         """
-        raise NotImplementedError
+        ...
 
     @synchronized_queue
-    @abstractmethod
-    def reset_task(self, task_id: str):
+    # @abstractmethod
+    def reset(self, task_id: str):
         """Resets the task if the agent failed to complete it.
 
         Args:
             task_id (str): The ID of the task to be reset.
         """
-        raise NotImplementedError
+        ...