code quality

README.md

@@ -75,13 +75,22 @@ for result in results:
 - The `Worker` is an fully feature complete agent with an llm, tools, and a vectorstore for long term memory!
 
 ```python
-
+from langchain.models import ChatOpenAI
 from swarms import Worker
 
+llm = ChatOpenAI(
+    model_name='gpt-4', 
+    openai_api_key="api-key", 
+    temperature=0.5
+)
 
 node = Worker(
-    openai_api_key="",
+    llm=llm,
     ai_name="Optimus Prime",
+    ai_role="Worker in a swarm",
+    external_tools = None,
+    human_in_the_loop = False,
+    temperature = 0.5,
 )
 
 task = "What were the winning boston marathon times for the past 5 years (ending in 2022)? Generate a table of the year, name, country of origin, and times."

apps/discord.py

@@ -1,4 +1,3 @@
-import discord
 from discord.ext import commands
 from langchain.llms import OpenAIChat
 from swarms.agents import OmniModalAgent

docs/old-docs/design/abstraction.py

@@ -1,4 +1,4 @@
-from swarms import Model, Agent, WorkerNode, vectorstore, tools, orchestrator
+from swarms import Model, Agent, vectorstore, tools, orchestrator
 
 #1 model
 Model(openai)

example.py

@@ -1,7 +1,11 @@
-from langchain.models import OpenAIChat
+from langchain.models import ChatOpenAI
 from swarms import Worker
 
-llm = OpenAIChat()
+llm = ChatOpenAI(
+    model_name='gpt-4', 
+    openai_api_key="api-key", 
+    temperature=0.5
+)
 
 node = Worker(
     llm=llm,

playground/debate.py

@@ -1,5 +1,4 @@
-import re
-from typing import Callable, Dict, List
+from typing import Callable, List
 
 import numpy as np
 import tenacity
@@ -7,8 +6,6 @@ from langchain.chat_models import ChatOpenAI
 from langchain.output_parsers import RegexParser
 from langchain.prompts import PromptTemplate
 from langchain.schema import (
-    AIMessage,
-    BaseMessage,
     HumanMessage,
     SystemMessage,
 )

playground/godmode.py

@@ -15,6 +15,6 @@ llms = [
 
 god_mode = GodMode(llms)
 
-task = f"What are the biggest risks facing humanity?"
+task = "What are the biggest risks facing humanity?"
 
 god_mode.print_responses(task)

swarms/__init__.py

@@ -20,4 +20,4 @@ from swarms import swarms
 from swarms.swarms.orchestrate import Orchestrator
 
 #agents
-from swarms import agents
+from swarms import agents

swarms/agents/__init__.py

@@ -1,3 +1,4 @@
+
 """Agent Infrastructure, models, memory, utils, tools"""
 
 #agents
@@ -10,4 +11,4 @@ from swarms.agents.omni_modal_agent import OmniModalAgent
 
 #utils
 from swarms.agents.message import Message
-from swarms.agents.stream_response import stream
+from swarms.agents.stream_response import stream

swarms/agents/aot.py

@@ -112,9 +112,9 @@ class OpenAI:
             rejected_solutions=None
         ):
         if (type(state) == str):
-            state_text = state
+            pass
         else:
-            state_text = '\n'.join(state)
+            '\n'.join(state)
         print("New state generating thought:", state, "\n\n")
         prompt = f"""
         Accomplish the task below by decomposing it as many very explicit subtasks as possible, be very explicit and thorough denoted by 

swarms/agents/multi_modal_visual_agent.py

@@ -7,12 +7,8 @@ import uuid
 from PIL import Image, ImageDraw, ImageOps, ImageFont
 import math
 import numpy as np
-import argparse
 import inspect
-import tempfile
-from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
 from transformers import pipeline, BlipProcessor, BlipForConditionalGeneration, BlipForQuestionAnswering
-from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
 
 from diffusers import StableDiffusionPipeline, StableDiffusionInpaintPipeline, StableDiffusionInstructPix2PixPipeline
 from diffusers import EulerAncestralDiscreteScheduler
@@ -29,7 +25,6 @@ from langchain.llms.openai import OpenAI
 # Grounding DINO
 import groundingdino.datasets.transforms as T
 from groundingdino.models import build_model
-from groundingdino.util import box_ops
 from groundingdino.util.slconfig import SLConfig
 from groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap
 

swarms/agents/profitpilot.py

@@ -1,4 +1,3 @@
-import os
 import re
 from typing import Any, Callable, Dict, List, Union
 
@@ -18,7 +17,6 @@ from langchain.vectorstores import Chroma
 from pydantic import BaseModel, Field
 from swarms.models.prompts.sales import SALES_AGENT_TOOLS_PROMPT, conversation_stages
 from swarms.tools.interpreter_tool import compile
-from swarms.agents.omni_modal_agent import OmniModalAgent
 
 
 # classes

swarms/boss/boss_node.py

@@ -1,6 +1,5 @@
 import logging
 import os
-from typing import Optional
 
 import faiss
 from langchain import LLMChain, OpenAI, PromptTemplate

swarms/models/__init__.py

@@ -1,3 +1,6 @@
+# from swarms.models.palm import GooglePalm
+# from swarms.models.openai import OpenAIChat
+#prompts
 from swarms.models.anthropic import Anthropic
 # from swarms.models.palm import GooglePalm
 from swarms.models.petals import Petals

swarms/models/huggingface.py

@@ -1 +0,0 @@
-from exa import Inference, GPTQInference, MultiModalInference

swarms/models/prompts/agent_prompt_auto.py

@@ -1,5 +1,5 @@
 import time
-from typing import Any, Callable, List
+from typing import Any, List
 from swarms.models.prompts.agent_prompt_generator import get_prompt
 
 class TokenUtils:

swarms/models/prompts/debate.py

@@ -38,10 +38,4 @@ def debate_monitor(game_description, word_limit, character_names):
 
 
 def generate_character_header(game_description, topic, character_name, character_description):
-    prompt =  f"""{game_description}
-    Your name is {character_name}.
-    You are a presidential candidate.
-    Your description is as follows: {character_description}
-    You are debating the topic: {topic}.
-    Your goal is to be as creative as possible and make the voters think you are the best candidate.
-    """
+    pass

swarms/structs/__init__.py

@@ -1,3 +1,4 @@
 #structs
+#structs
 from swarms.structs.workflow import Workflow
-from swarms.structs.task import Task
+from swarms.structs.task import Task

swarms/swarms/__init__.py

@@ -1,4 +1,5 @@
 # swarms
+# swarms
 from swarms.swarms.dialogue_simulator import DialogueSimulator
 from swarms.swarms.autoscaler import AutoScaler
 from swarms.swarms.orchestrate import Orchestrator

swarms/swarms/god_mode.py

@@ -29,7 +29,10 @@ class GodMode:
 
 
     """
-    def __init__(self, llms):
+    def __init__(
+        self, 
+        llms
+    ):
         self.llms = llms
 
     def run(self, task):

swarms/swarms/multi_agent_debate.py

@@ -7,11 +7,12 @@ def select_speaker(step: int, agents: List[Worker]) -> int:
     # This function selects the speaker in a round-robin fashion
     return step % len(agents)
 
-
 class MultiAgentDebate:
     """
     MultiAgentDebate
     
+    Args:
+    
     
     """
     def __init__(

swarms/swarms/orchestrate.py

@@ -281,13 +281,13 @@ class Orchestrator:
         )
 
         #store the mesage in the vector database
-        added = self.collection.add(
+        self.collection.add(
             embeddings=[message_vector],
             documents=[message],
             ids=[f"{sender_id}_to_{receiver_id}"]
         )
 
-        result = self.run(
+        self.run(
             objective=f"chat with agent {receiver_id} about {message}"
         )
 

swarms/tools/autogpt.py

@@ -8,17 +8,13 @@ from typing import Optional
 import pandas as pd
 from langchain.agents import tool
 from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
-from langchain.chains.qa_with_sources.loading import load_qa_with_sources_chain
 from langchain.docstore.document import Document
 
 ROOT_DIR = "./data/"
 
 from langchain.chains.qa_with_sources.loading import BaseCombineDocumentsChain
-from langchain.chat_models import ChatOpenAI
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.tools import BaseTool
-from langchain.tools.file_management.read import ReadFileTool
-from langchain.tools.file_management.write import WriteFileTool
 from pydantic import Field
 
 from swarms.utils.logger import logger

swarms/tools/code_intepretor.py

@@ -1,6 +1,6 @@
 #props to shroominic
 from swarms.tools.base import Tool, ToolException
-from typing import Callable, Any, List
+from typing import Any, List
 from codeinterpreterapi import CodeInterpreterSession, File, ToolException
 
 class CodeInterpreter(Tool):

swarms/tools/stt.py

@@ -75,7 +75,7 @@ class SpeechToText:
             use_auth_token=self.hf_api_key, 
             device=device
         )
-        diarize_segments = diarize_model(audio_file)
+        diarize_model(audio_file)
         
         try:
             segments = result["segments"]
@@ -113,7 +113,7 @@ class SpeechToText:
             device=self.device
         )
 
-        diarize_segments = diarize_model(audio_file)
+        diarize_model(audio_file)
         
         try:
             segments = result["segments"]

swarms/workers/worker.py

@@ -17,16 +17,9 @@ from swarms.tools.autogpt import (
 )
 from swarms.utils.decorators import error_decorator, log_decorator, timing_decorator
 
-# self.llm = ChatOpenAI(
-#     model_name=model_name, 
-#     openai_api_key=self.openai_api_key, 
-#     temperature=self.temperature
-# )
-
 #cache
 ROOT_DIR = "./data/"
 
-
 #main
 class Worker:
     """
@@ -79,9 +72,6 @@ class Worker:
         self.setup_tools(external_tools)
         self.setup_memory()
         self.setup_agent()
-
-        # self.task_queue = []
-        # self.executor = concurrent.futures.ThreadPoolExecutor()
         
     def reset(self):
         """

tests/agents/omni_modal.py

@@ -1,15 +1,5 @@
 import pytest
 from langchain.base_language import BaseLanguageModel
-from langchain_experimental.autonomous_agents.hugginggpt.repsonse_generator import (
-    load_response_generator,
-)
-from langchain_experimental.autonomous_agents.hugginggpt.task_executor import (
-    TaskExecutor,
-)
-from langchain_experimental.autonomous_agents.hugginggpt.task_planner import (
-    load_chat_planner,
-)
-from transformers import load_tool
 
 from swarms.agents import (
     OmniModalAgent,  # Replace `your_module_name` with the appropriate module name