clean up

.github/workflows/ruff.yml

@@ -0,0 +1,8 @@
+name: Ruff
+on: [ push, pull_request ]
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: chartboost/ruff-action@v1

api/app.py

@@ -42,6 +42,6 @@ async def run(swarm_input: SwarmInput):
     except ValueError as ve:
         logging.error("A ValueError occurred", exc_info=True)
         raise HTTPException(status_code=400, detail=str(ve))
-    except Exception as e:
+    except Exception:
         logging.error("An error occurred", exc_info=True)
         raise HTTPException(status_code=500, detail="An unexpected error occurred")

api/olds/container.py

@@ -1,5 +1,4 @@
 import os
-import re
 from pathlib import Path
 from typing import Dict, List
 

playground/swarms_example.py

@@ -1,5 +1,4 @@
 from swarms import HierarchicalSwarm
-import os
 
 # Retrieve your API key from the environment or replace with your actual key
 api_key = ""

swarms/__init__.py

@@ -1,5 +1,2 @@
 # from swarms import Swarms, swarm
-from swarms.swarms import HierarchicalSwarm, swarm
 # from swarms.workers.worker_ultra_node import WorkerUltraNode, WorkerUltra
-from swarms.workers.worker_node import WorkerNode, worker_node
-from swarms.boss.boss_node import BossNode

swarms/agents/__init__.py

@@ -2,11 +2,8 @@
 
 
 #models
-from swarms.agents.models.llm import LLM
-from swarms.agents.models.hf import HuggingFaceLLM
 
 
 
 #tools
-from swarms.agents.tools.main import process_csv, MaskFormer, ImageEditing, InstructPix2Pix, Text2Image, VisualQuestionAnswering, ImageCaptioning, browse_web_page, WebpageQATool, web_search
 

swarms/agents/agent.py

@@ -4,15 +4,10 @@ from swarms.utils.logger import logger
 
 from langchain.tools import BaseTool
 
-from langchain.callbacks.manager import (
-    AsyncCallbackManagerForToolRun,
-    CallbackManagerForToolRun,
-)
-from typing import List, Any, Dict, Optional, Type
+from typing import List, Any, Optional
 from langchain.memory.chat_message_histories import FileChatMessageHistory
 
 import logging
-from pydantic import BaseModel, Extra
 from swarms.agents.models.hf import HuggingFaceLLM
 
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

swarms/agents/models/__init__.py

@@ -1 +0,0 @@
-from swarms.agents.models.llm import LLM

swarms/agents/models/petals_hf.py

@@ -1,5 +1,3 @@
-from transformers import AutoTokenizer
-from petals import AutoDistributedForCasualLM
 
 class PetalsHFLLM:
     def __init__(self, model_name: str = None, prompt: str = None, device: str = None, use_fast = False, add_bos_token: str = None, cuda=False):

swarms/agents/tools/__init__.py

@@ -1,3 +1,2 @@
 # from swarms.agents.workers import multimodal_agent_tool
-from swarms.agents.tools.main import process_csv, ReadFileTool, WriteFileTool, BaseTool, DuckDuckGoSearchRun
 # Terminal, CodeWriter, CodeEditor

swarms/agents/tools/agent_tools.py

@@ -1,36 +1,12 @@
 
 
 # ---------- Dependencies ----------
-import os
-import asyncio
-import faiss
-from typing import Any, Optional, List
-from contextlib import contextmanager
-
-from pydantic import BaseModel, Field
-from langchain import LLMChain, OpenAI, PromptTemplate
-from langchain.chains.base import Chain
-
-from langchain.experimental import BabyAGI
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.vectorstores.base import VectorStore
-from langchain.vectorstores import FAISS
-
-from langchain.docstore import InMemoryDocstore
-from langchain.chains.qa_with_sources.loading import load_qa_with_sources_chain
-from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
-
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.tools import BaseTool, DuckDuckGoSearchRun
-from langchain.tools.file_management.read import ReadFileTool
-
-from langchain.tools.file_management.write import WriteFileTool
-from langchain.tools.human.tool import HumanInputRun
-from swarms.agents.tools.main import process_csv, WebpageQATool
-
-from langchain.experimental.autonomous_agents.autogpt.agent import AutoGPT
-from langchain.chat_models import ChatOpenAI
-from langchain.tools import tool
+
+
+
+
+
+
 
 # ---------- Constants ----------
 ROOT_DIR = "./data/"

swarms/agents/tools/base.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from typing import Any, Awaitable, Callable, Dict, List, Optional, Type, Union
+from typing import Any, Callable, Optional, Type, Union
 from pydantic import BaseModel
 
 class ToolException(Exception):

swarms/agents/tools/core/code_interpreter/main.py

@@ -4,9 +4,8 @@ import json
 import re
 import uuid
 from dataclasses import dataclass
-from dotenv import load_dotenv
 from io import BytesIO
-from typing import Any, List, Optional, Sequence, Tuple, Union, TYPE_CHECKING
+from typing import Any, List, Optional, Sequence, Tuple, Union
 
 from pydantic import BaseModel, BaseSettings, root_validator
 
@@ -727,9 +726,9 @@ class CodeInterpreterSession:
         for file in self.output_files:
             if str(file.name) in final_response:
                 # rm ![Any](file.name) from the response
-                final_response = re.sub(rf"\n\n!\[.*\]\(.*\)", "", final_response)
+                final_response = re.sub(r"\n\n!\[.*\]\(.*\)", "", final_response)
 
-        if self.output_files and re.search(rf"\n\[.*\]\(.*\)", final_response):
+        if self.output_files and re.search(r"\n\[.*\]\(.*\)", final_response):
             final_response = await remove_download_link(final_response, self.llm)
 
         return CodeInterpreterResponse(content=final_response, files=self.output_files)

swarms/agents/tools/main.py

@@ -10,7 +10,6 @@ from langchain.agents import load_tools
 from langchain.agents.tools import BaseTool
 from langchain.llms.base import BaseLLM
 
-from langchain.chat_models import ChatOpenAI
 
 
 import requests
@@ -58,9 +57,8 @@ class ToolWrapper:
     ) -> BaseTool:
         func = self.func
         if self.is_per_session():
-            func = lambda *args, **kwargs: self.func(
-                *args, **kwargs, get_session=get_session
-            )
+            def func(*args, **kwargs):
+                return self.func(*args, **kwargs, get_session=get_session)
 
         return Tool(
             name=self.name,
@@ -170,7 +168,7 @@ class ExitConversation(BaseToolSet):
         _, executor = get_session()
         del executor
 
-        logger.debug(f"\nProcessed ExitConversation.")
+        logger.debug("\nProcessed ExitConversation.")
 
         return message
     
@@ -422,8 +420,6 @@ class ToolsFactory:
 
 import os
 import subprocess
-import time
-from datetime import datetime
 from typing import Dict, List
 
 from swarms.utils.main import ANSI, Color, Style # test
@@ -940,7 +936,7 @@ class CodeEditor(BaseToolSet):
     @tool(
         name="CodeEditor.READ",
         description="Read and understand code. "
-        f"Input should be filename and line number group. ex. test.py|1-10 "
+        "Input should be filename and line number group. ex. test.py|1-10 "
         "and the output will be code. ",
     )
     def read(self, inputs: str) -> str:
@@ -1470,14 +1466,11 @@ import pandas as pd
 from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
 from langchain.docstore.document import Document
 import asyncio
-import nest_asyncio
 
 # Tools
 from contextlib import contextmanager
 from typing import Optional
 from langchain.agents import tool
-from langchain.tools.file_management.read import ReadFileTool
-from langchain.tools.file_management.write import WriteFileTool
 
 ROOT_DIR = "./data/"
 
@@ -1485,7 +1478,7 @@ from langchain.tools import BaseTool, DuckDuckGoSearchRun
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 
 from pydantic import Field
-from langchain.chains.qa_with_sources.loading import load_qa_with_sources_chain, BaseCombineDocumentsChain
+from langchain.chains.qa_with_sources.loading import BaseCombineDocumentsChain
 
 
 
@@ -1787,14 +1780,6 @@ web_search = DuckDuckGoSearchRun()
 
 ######################## ######################################################## file system
 
-from langchain.tools.file_management import (
-    ReadFileTool,
-    CopyFileTool,
-    DeleteFileTool,
-    MoveFileTool,
-    WriteFileTool,
-    ListDirectoryTool,
-)
 from langchain.agents.agent_toolkits import FileManagementToolkit
 from tempfile import TemporaryDirectory
 

swarms/agents/utils/Agent.py

@@ -1,30 +1,23 @@
 """Chain that takes in an input and produces an action and action input."""
 from __future__ import annotations
 
-import asyncio
 import json
 import logging
-import time
 from abc import abstractmethod
 from pathlib import Path
-from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
+from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
 
 import yaml
 from pydantic import BaseModel, root_validator
 
 from langchain.agents.agent_types import AgentType
-from langchain.agents.tools import InvalidTool
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.callbacks.manager import (
-    AsyncCallbackManagerForChainRun,
     AsyncCallbackManagerForToolRun,
-    CallbackManagerForChainRun,
     CallbackManagerForToolRun,
     Callbacks,
 )
-from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
-from langchain.input import get_color_mapping
 from langchain.prompts.few_shot import FewShotPromptTemplate
 from langchain.prompts.prompt import PromptTemplate
 from langchain.schema import (
@@ -32,12 +25,10 @@ from langchain.schema import (
     AgentFinish,
     BaseOutputParser,
     BasePromptTemplate,
-    OutputParserException,
 )
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.messages import BaseMessage
 from langchain.tools.base import BaseTool
-from langchain.utilities.asyncio import asyncio_timeout
 
 logger = logging.getLogger(__name__)
 

swarms/agents/utils/Calback.py

@@ -51,17 +51,17 @@ class EVALCallbackHandler(BaseCallbackHandler):
     def on_chain_start(
         self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
     ) -> None:
-        logger.info(ANSI(f"Entering new chain.").to(Color.green(), Style.italic()))
+        logger.info(ANSI("Entering new chain.").to(Color.green(), Style.italic()))
         logger.info(ANSI("Prompted Text").to(Color.yellow()) + f': {inputs["input"]}\n')
 
     def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
-        logger.info(ANSI(f"Finished chain.").to(Color.green(), Style.italic()))
+        logger.info(ANSI("Finished chain.").to(Color.green(), Style.italic()))
 
     def on_chain_error(
         self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
     ) -> None:
         logger.error(
-            ANSI(f"Chain Error").to(Color.red()) + ": " + dim_multiline(str(error))
+            ANSI("Chain Error").to(Color.red()) + ": " + dim_multiline(str(error))
         )
 
     def on_tool_start(

swarms/agents/utils/agent_creator.py

@@ -1,4 +1,3 @@
-import os
 import logging
 from typing import Dict, Optional
 from celery import Task
@@ -7,7 +6,6 @@ from langchain.callbacks.manager import CallbackManager
 from langchain.chains.conversation.memory import ConversationBufferMemory
 from langchain.memory.chat_memory import BaseChatMemory
 from swarms.agents.tools.main import BaseToolSet, ToolsFactory
-from swarms.agents.prompts.prompts import EVAL_PREFIX, EVAL_SUFFIX
 
 from swarms.agents.utils.agent_setup import AgentSetup
 # from .callback import EVALCallbackHandler, ExecutionTracingCallbackHandler
@@ -31,7 +29,7 @@ class AgentCreator:
             raise TypeError("Session must be a string")
         if not session:
             raise ValueError("Session is empty")
-        if not (session in self.memories):
+        if session not in self.memories:
             self.memories[session] = self.create_memory()
         return self.memories[session]
 

swarms/orchestrate.py

@@ -45,10 +45,8 @@ Orchestrate(WorkerNode, autoscale=True, nodes=int, swarm_type="flat")
 """
 #in a shared state, provide 3 communication times, during task assignment, task compeltion, and feedback or inability to complete a task.
 from abc import ABC, abstractmethod
-import celery
 from typing import List, Dict, Any
 
-import numpy as np
 import threading
 
 from swarms.agents.memory.ocean import OceanDB

swarms/swarms.py

@@ -2,10 +2,9 @@ import logging
 import asyncio
 
 from swarms.agents.tools.agent_tools import *
-from swarms.workers.worker_node import WorkerNodeInitializer, worker_node
+from swarms.workers.worker_node import WorkerNodeInitializer
 from swarms.boss.boss_node import BossNodeInitializer as BossNode
 
-from swarms.utils.task import Task
 from swarms.agents.models.hf import HuggingFaceLLM
 
 # from langchain import LLMMathChain

swarms/utils/embeddings/__init__.py

@@ -1 +0,0 @@
-from swarms.utils.embeddings.base import Embeddings

swarms/workers/__init__.py

@@ -1,2 +0,0 @@
-from .worker_node import worker_node
-from .worker_ultra_node import WorkerUltraNode

swarms/workers/generative_worker.py

@@ -1,7 +1,7 @@
 import logging
 import re
 from datetime import datetime
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any, Dict, List, Optional
 
 ############
 from langchain.prompts import PromptTemplate
@@ -19,7 +19,6 @@ logger = logging.getLogger(__name__)
 
 #######################
 
-from pydantic import BaseModel, Field
 
 class WorkerSims(BaseMemory):
     llm: BaseLanguageModel

swarms/workers/models/GroundingDINO/__init__.py

@@ -1 +0,0 @@
-from GroundingDINO import groundingdino

swarms/workers/models/GroundingDINO/demo/create_coco_dataset.py

@@ -2,7 +2,6 @@ import typer
 from groundingdino.util.inference import load_model, load_image, predict
 from tqdm import tqdm
 import torchvision
-import torch
 import fiftyone as fo
 
 
@@ -23,7 +22,6 @@ def main(
     
     dataset = fo.Dataset.from_images_dir(image_directory)
 
-    samples = []
 
     if subsample is not None: 
         

swarms/workers/models/GroundingDINO/demo/gradio_app.py

@@ -1,12 +1,8 @@
 import argparse
-from functools import partial
 import cv2
-import requests
 import os
-from io import BytesIO
 from PIL import Image
 import numpy as np
-from pathlib import Path
 
 
 import warnings
@@ -26,7 +22,7 @@ import gradio as gr
 from groundingdino.models import build_model
 from groundingdino.util.slconfig import SLConfig
 from groundingdino.util.utils import clean_state_dict
-from groundingdino.util.inference import annotate, load_image, predict
+from groundingdino.util.inference import annotate, predict
 import groundingdino.datasets.transforms as T
 
 from huggingface_hub import hf_hub_download
@@ -71,7 +67,6 @@ model = load_model_hf(config_file, ckpt_repo_id, ckpt_filenmae)
 
 def run_grounding(input_image, grounding_caption, box_threshold, text_threshold):
     init_image = input_image.convert("RGB")
-    original_size = init_image.size
 
     _, image_tensor = image_transform_grounding(init_image)
     image_pil: Image = image_transform_grounding_for_vis(init_image)

swarms/workers/models/GroundingDINO/demo/inference_on_a_image.py

@@ -1,6 +1,5 @@
 import argparse
 import os
-import sys
 
 import numpy as np
 import torch
@@ -8,7 +7,6 @@ from PIL import Image, ImageDraw, ImageFont
 
 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
 from groundingdino.util.vl_utils import create_positive_map_from_span

swarms/workers/models/GroundingDINO/demo/test_ap_on_coco.py

@@ -1,12 +1,9 @@
 import argparse
-import os
-import sys
 import time
 
-import numpy as np
 import torch
 import torch.nn as nn
-from torch.utils.data import DataLoader, DistributedSampler
+from torch.utils.data import DataLoader
 
 from groundingdino.models import build_model
 import groundingdino.datasets.transforms as T

swarms/workers/models/GroundingDINO/groundingdino/models/GroundingDINO/__init__.py

@@ -12,4 +12,3 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
 # ------------------------------------------------------------------------
 
-from .groundingdino import build_groundingdino

swarms/workers/models/GroundingDINO/groundingdino/models/GroundingDINO/backbone/__init__.py

@@ -1 +0,0 @@
-from .backbone import build_backbone

swarms/workers/models/GroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py

@@ -24,7 +24,7 @@ import torchvision
 from torch import nn
 from torchvision.models._utils import IntermediateLayerGetter
 
-from groundingdino.util.misc import NestedTensor, clean_state_dict, is_main_process
+from groundingdino.util.misc import NestedTensor, is_main_process
 
 from .position_encoding import build_position_encoding
 from .swin_transformer import build_swin_transformer

swarms/workers/models/GroundingDINO/groundingdino/models/GroundingDINO/bertwarper.py

@@ -6,11 +6,7 @@
 # ------------------------------------------------------------------------
 
 import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-from torch import Tensor, nn
-from torchvision.ops.boxes import nms
-from transformers import BertConfig, BertModel, BertPreTrainedModel
+from torch import nn
 from transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions
 
 

swarms/workers/models/GroundingDINO/groundingdino/models/GroundingDINO/groundingdino.py

@@ -20,32 +20,22 @@ from typing import List
 import torch
 import torch.nn.functional as F
 from torch import nn
-from torchvision.ops.boxes import nms
-from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast
 
-from groundingdino.util import box_ops, get_tokenlizer
+from groundingdino.util import get_tokenlizer
 from groundingdino.util.misc import (
     NestedTensor,
-    accuracy,
-    get_world_size,
-    interpolate,
     inverse_sigmoid,
-    is_dist_avail_and_initialized,
     nested_tensor_from_tensor_list,
 )
-from groundingdino.util.utils import get_phrases_from_posmap
-from groundingdino.util.visualizer import COCOVisualizer
-from groundingdino.util.vl_utils import create_positive_map_from_span
 
 from ..registry import MODULE_BUILD_FUNCS
 from .backbone import build_backbone
 from .bertwarper import (
     BertModelWarper,
-    generate_masks_with_special_tokens,
     generate_masks_with_special_tokens_and_transfer_map,
 )
 from .transformer import build_transformer
-from .utils import MLP, ContrastiveEmbed, sigmoid_focal_loss
+from .utils import MLP, ContrastiveEmbed
 
 
 class GroundingDINO(nn.Module):
@@ -152,7 +142,7 @@ class GroundingDINO(nn.Module):
 
         self.backbone = backbone
         self.aux_loss = aux_loss
-        self.box_pred_damping = box_pred_damping = None
+        self.box_pred_damping = None
 
         self.iter_update = iter_update
         assert iter_update, "Why not iter_update?"
@@ -308,7 +298,7 @@ class GroundingDINO(nn.Module):
                 masks.append(mask)
                 poss.append(pos_l)
 
-        input_query_bbox = input_query_label = attn_mask = dn_meta = None
+        input_query_bbox = input_query_label = attn_mask = None
         hs, reference, hs_enc, ref_enc, init_box_proposal = self.transformer(
             srcs, masks, input_query_bbox, poss, input_query_label, attn_mask, text_dict
         )

swarms/workers/models/GroundingDINO/groundingdino/models/GroundingDINO/transformer.py

@@ -250,7 +250,6 @@ class Transformer(nn.Module):
         valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)
 
         # two stage
-        enc_topk_proposals = enc_refpoint_embed = None
 
         #########################################################
         # Begin Encoder

swarms/workers/models/GroundingDINO/groundingdino/models/GroundingDINO/transformer_vanilla.py

@@ -17,16 +17,11 @@ Copy-paste from torch.nn.Transformer with modifications:
 from typing import Optional
 
 import torch
-import torch.nn.functional as F
 from torch import Tensor, nn
 
 from .utils import (
-    MLP,
     _get_activation_fn,
     _get_clones,
-    gen_encoder_output_proposals,
-    gen_sineembed_for_position,
-    sigmoid_focal_loss,
 )
 
 

swarms/workers/models/GroundingDINO/groundingdino/models/__init__.py

@@ -5,7 +5,6 @@
 # Licensed under the Apache License, Version 2.0 [see LICENSE for details]
 # ------------------------------------------------------------------------
 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from .GroundingDINO import build_groundingdino
 
 
 def build_model(args):

swarms/workers/models/GroundingDINO/groundingdino/util/get_tokenlizer.py

@@ -1,4 +1,4 @@
-from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast
+from transformers import AutoTokenizer, BertModel, RobertaModel
 import os
 
 def get_tokenlizer(text_encoder_type):

swarms/workers/models/GroundingDINO/groundingdino/util/utils.py

@@ -268,7 +268,8 @@ def get_embedder(multires, i=0):
     }
 
     embedder_obj = Embedder(**embed_kwargs)
-    embed = lambda x, eo=embedder_obj: eo.embed(x)
+    def embed(x, eo=embedder_obj):
+        return eo.embed(x)
     return embed, embedder_obj.out_dim
 
 

swarms/workers/models/GroundingDINO/groundingdino/util/visualizer.py

@@ -9,7 +9,6 @@
 import datetime
 import os
 
-import cv2
 import matplotlib.pyplot as plt
 import numpy as np
 import torch
@@ -134,7 +133,7 @@ class COCOVisualizer:
 
     def addtgt(self, tgt):
         """ """
-        if tgt is None or not "boxes" in tgt:
+        if tgt is None or "boxes" not in tgt:
             ax = plt.gca()
 
             if "caption" in tgt:

swarms/workers/models/__init__.py

@@ -3,27 +3,5 @@
 # from .GroundingDINO.groundingdino.util import box_ops, SLConfig
 # from .GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap
 # from .segment_anything.segment_anything import build_sam, SamPredictor, SamAutomaticMaskGenerator
-from swarms.workers.models.GroundingDINO.groundingdino.datasets.transforms import (
-    Compose,
-    Normalize,
-    ToTensor,
-    crop,
-    hflip,
-    resize,
-    pad,
-    ResizeDebug,
-    RandomCrop,
-    RandomSizeCrop,
-    CenterCrop,
-    RandomHorizontalFlip,
-    RandomResize,
-    RandomPad,
-    RandomSelect
-)
 
-from swarms.workers.models.GroundingDINO.groundingdino.models import build_model
-from swarms.workers.models.GroundingDINO.groundingdino.util import box_ops
-from swarms.workers.models.GroundingDINO.groundingdino.util.slconfig import SLConfig
-from swarms.workers.models.GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap
 
-from swarms.workers.models.segment_anything import build_sam, SamPredictor, SamAutomaticMaskGenerator

swarms/workers/models/segment_anything/__init__.py

@@ -1 +0,0 @@
-from segment_anything import segment_anything

swarms/workers/models/segment_anything/segment_anything/__init__.py

@@ -4,12 +4,3 @@
 # This source code is licensed under the license found in the
 # LICENSE file in the root directory of this source tree.
 
-from .build_sam import (
-    build_sam,
-    build_sam_vit_h,
-    build_sam_vit_l,
-    build_sam_vit_b,
-    sam_model_registry,
-)
-from .predictor import SamPredictor
-from .automatic_mask_generator import SamAutomaticMaskGenerator

swarms/workers/models/segment_anything/segment_anything/modeling/__init__.py

@@ -4,8 +4,3 @@
 # This source code is licensed under the license found in the
 # LICENSE file in the root directory of this source tree.
 
-from .sam import Sam
-from .image_encoder import ImageEncoderViT
-from .mask_decoder import MaskDecoder
-from .prompt_encoder import PromptEncoder
-from .transformer import TwoWayTransformer

swarms/workers/multi_modal_workers/__init__.py

@@ -1 +0,0 @@
-from swarms.workers.multi_modal_workers.omni_agent.omni_chat import chat_huggingface

swarms/workers/multi_modal_workers/multi_modal_agent.py

@@ -11,10 +11,7 @@ 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
@@ -34,22 +31,10 @@ from swarms.workers.models import (
     Compose,
     Normalize,
     ToTensor,
-    crop,
-    hflip,
-    resize,
-    pad,
-    ResizeDebug,
-    RandomCrop,
-    RandomSizeCrop,
-    CenterCrop,
-    RandomHorizontalFlip,
-    RandomResize,
-    RandomPad,
-    RandomSelect
+    RandomResize
 )
 
 from swarms.workers.models import build_model
-from swarms.workers.models  import box_ops
 from swarms.workers.models import SLConfig
 from swarms.workers.models import clean_state_dict, get_phrases_from_posmap
 
@@ -1019,8 +1004,6 @@ class Segmenting:
         sorted_anns = sorted(masks, key=(lambda x: x['area']), reverse=True)
         ax = plt.gca()
         ax.set_autoscale_on(False)
-        polygons = []
-        color = []
         for ann in sorted_anns:
             m = ann['segmentation']
             img = np.ones((m.shape[0], m.shape[1], 3))
@@ -1363,7 +1346,7 @@ class ObjectSegmenting:
             image = self.sam.show_mask(mask[0].cpu().numpy(), image, random_color=True, transparency=0.3)
 
 
-        merged_mask_image = Image.fromarray(merged_mask)
+        Image.fromarray(merged_mask)
 
         return merged_mask
 
@@ -1371,7 +1354,7 @@ class ObjectSegmenting:
 class ImageEditing:
     template_model = True
     def __init__(self, Text2Box:Text2Box, Segmenting:Segmenting, Inpainting:Inpainting):
-        print(f"Initializing ImageEditing")
+        print("Initializing ImageEditing")
         self.sam = Segmenting
         self.grounding = Text2Box
         self.inpaint = Inpainting

swarms/workers/multi_modal_workers/omni_agent/model_server.py

@@ -8,24 +8,18 @@ from diffusers import DiffusionPipeline, StableDiffusionControlNetPipeline, Cont
 from diffusers.utils import load_image
 from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
 from diffusers.utils import export_to_video
-from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5ForSpeechToSpeech
-from transformers import BlipProcessor, BlipForConditionalGeneration
-from transformers import TrOCRProcessor, VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
+from transformers import SpeechT5Processor, SpeechT5HifiGan, SpeechT5ForSpeechToSpeech
+from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
 from datasets import load_dataset
 from PIL import Image
 # import flask
 # from flask import request, jsonify
 import waitress
 # from flask_cors import CORS
-import io
 from torchvision import transforms
 import torch
 import torchaudio
-from speechbrain.pretrained import WaveformEnhancement
-import joblib
-from huggingface_hub import hf_hub_url, cached_download
-from transformers import AutoImageProcessor, TimesformerForVideoClassification
-from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation, AutoFeatureExtractor
+from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation
 from controlnet_aux import OpenposeDetector, MLSDdetector, HEDdetector, CannyDetector, MidasDetector
 from controlnet_aux.open_pose.body import Body
 from controlnet_aux.mlsd.models.mbv2_mlsd_large import MobileV2_MLSD_Large
@@ -119,7 +113,7 @@ def load_pipes(local_deployment):
                 "device": device
             },
             "espnet/kan-bayashi_ljspeech_vits": {
-                "model": Text2Speech.from_pretrained(f"espnet/kan-bayashi_ljspeech_vits"),
+                "model": Text2Speech.from_pretrained("espnet/kan-bayashi_ljspeech_vits"),
                 "device": device
             },
             "lambdalabs/sd-image-variations-diffusers": {

swarms/workers/multi_modal_workers/omni_agent/omni_chat.py

@@ -20,11 +20,9 @@ import threading
 from queue import Queue
 # import flask
 # from flask import request, jsonify
-import waitress
 # from flask_cors import CORS, cross_origin
 from swarms.workers.multi_modal_workers.omni_agent.get_token_ids import get_token_ids_for_task_parsing, get_token_ids_for_choose_model, count_tokens, get_max_context_length
 from huggingface_hub.inference_api import InferenceApi
-from huggingface_hub.inference_api import ALL_TASKS
 
 parser = argparse.ArgumentParser()
 parser.add_argument("--config", type=str, default="swarms/agents/workers/multi_modal_workers/omni_agent/config.yml")
@@ -253,7 +251,7 @@ def record_case(success, **args):
 
 def image_to_bytes(img_url):
     img_byte = io.BytesIO()
-    type = img_url.split(".")[-1]
+    img_url.split(".")[-1]
     load_image(img_url).save(img_byte, format="png")
     img_data = img_byte.getvalue()
     return img_data
@@ -809,7 +807,7 @@ def run_task(input, command, results, api_key, api_type, api_endpoint):
         else:
             logger.warning(f"Task {command['task']} is not available. ControlNet need to be deployed locally.")
             record_case(success=False, **{"input": input, "task": command, "reason": f"Task {command['task']} is not available. ControlNet need to be deployed locally.", "op":"message"})
-            inference_result = {"error": f"service related to ControlNet is not available."}
+            inference_result = {"error": "service related to ControlNet is not available."}
             results[id] = collect_result(command, "", inference_result)
             return False
     elif task in ["summarization", "translation", "conversational", "text-generation", "text2text-generation"]: # ChatGPT Can do
@@ -872,7 +870,7 @@ def run_task(input, command, results, api_key, api_type, api_endpoint):
                 reason = choose["reason"]
                 best_model_id = choose["id"]
                 hosted_on = "local" if best_model_id in all_avaliable_models["local"] else "huggingface"
-            except Exception as e:
+            except Exception:
                 logger.warning(f"the response [ {choose_str} ] is not a valid JSON, try to find the model id and reason in the response.")
                 choose_str = find_json(choose_str)
                 best_model_id, reason, choose  = get_id_reason(choose_str)

swarms/workers/worker_agent_ultra.py

@@ -1,30 +1,23 @@
 import os
 import logging
-from typing import Optional, Type
-from langchain.callbacks.manager import (
-    AsyncCallbackManagerForToolRun,
-    CallbackManagerForToolRun,
-)
 
 
 from swarms.agents.tools.agent_tools import *
-from typing import List, Any, Dict, Optional
+from typing import List, Dict
 from langchain.memory.chat_message_histories import FileChatMessageHistory
 
 import logging
-from pydantic import BaseModel, Extra
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
 
 
 
-from typing import List, Any, Dict, Optional
+from typing import List, Dict
 from langchain.memory.chat_message_histories import FileChatMessageHistory
-from swarms.utils.main import BaseHandler, FileHandler, FileType
+from swarms.utils.main import BaseHandler, FileType
 from swarms.agents.tools.main import ExitConversation, RequestsGet, CodeEditor, Terminal
 from swarms.utils.main import CsvToDataframe
 from swarms.agents.tools.main import BaseToolSet
-from swarms.utils.main import StaticUploader
 
 
 class WorkerUltraNode:

swarms/workers/worker_node.py

@@ -1,16 +1,8 @@
 from swarms.agents.tools.agent_tools import *
-from langchain.tools import BaseTool
-from typing import Optional, Type
-
-from langchain.callbacks.manager import (
-    AsyncCallbackManagerForToolRun,
-    CallbackManagerForToolRun,
-)
-from typing import List, Any, Dict, Optional
+
 from langchain.memory.chat_message_histories import FileChatMessageHistory
 
 import logging
-from pydantic import BaseModel, Extra
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
 from swarms.agents.tools.main import Terminal

tests/agents/memory/main.py

@@ -1,5 +1,5 @@
 import pytest
-from unittest.mock import Mock, MagicMock
+from unittest.mock import Mock
 from swarms.agents.memory.oceandb import OceanDB
 
 

tests/agents/models/LLM.py

@@ -1,7 +1,7 @@
 import unittest
 import os
-from unittest.mock import patch, MagicMock
-from langchain import PromptTemplate, HuggingFaceHub, ChatOpenAI, LLMChain
+from unittest.mock import patch
+from langchain import HuggingFaceHub, ChatOpenAI
 
 from swarms.agents.models.llm import LLM
 

tests/agents/models/hf.py

@@ -1,8 +1,7 @@
 import pytest
 import torch
-from unittest.mock import Mock, MagicMock
-from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
-from swarms.agents.models.hf import HuggingFaceLLM  # replace with actual import
+from unittest.mock import Mock
+from swarms.agents.models.hf import HuggingFaceLLM  
 
 
 @pytest.fixture
@@ -50,7 +49,7 @@ def test_init_with_quantize(hugging_face_llm, mock_autotokenizer, mock_automodel
     }
     mock_bitsandbytesconfig.return_value = quantization_config
 
-    hugging_face_llm = HuggingFaceLLM(model_id='test', quantize=True)
+    HuggingFaceLLM(model_id='test', quantize=True)
 
     mock_bitsandbytesconfig.assert_called_once_with(**quantization_config)
     mock_autotokenizer.from_pretrained.assert_called_once_with('test')

tests/agents/workers/omni_worker.py

@@ -1,5 +1,4 @@
 import pytest
-from unittest.mock import Mock
 from swarms.worker.omni_worker import OmniWorkerAgent  # replace 'yourmodule' with the actual module name
 
 @pytest.fixture

tests/agents/workers/worker_agent_ultra.py

@@ -1,6 +1,6 @@
 import pytest
-from unittest.mock import Mock, MagicMock
-from swarms.workers.worker_agent_ultra import WorkerUltraNode, WorkerUltraNodeInitializer  # import your module here
+from unittest.mock import Mock
+from swarms.workers.worker_agent_ultra import WorkerUltraNode  # import your module here
 
 def test_create_agent():
     mock_llm = Mock()
@@ -42,4 +42,4 @@ def test_worker_node():
 
 def test_worker_node_no_key():
     with pytest.raises(ValueError):
-        worker = worker_ultra_node(None)
+        worker_ultra_node(None)

tests/agents/workers/worker_node.py

@@ -1,7 +1,6 @@
 import pytest
 from swarms.agents.tools.agent_tools import *
 from unittest.mock import MagicMock, patch
-from pydantic import ValidationError
 from swarms.worker.worker_node import WorkerNodeInitializer, WorkerNode  # replace your_module with actual module name
 
 

tests/agents/workers/worker_ultra.py

@@ -58,6 +58,6 @@ def test_worker_ultra_node_initializer_initialize_vectorstore(worker_ultra_node_
         assert vectorstore is not None
 
 def test_worker_ultra_node_initializer_create_worker_node(worker_ultra_node_initializer):
-    with patch.object(worker_ultra_node_initializer, 'initialize_llm') as mock_llm, patch.object(worker_ultra_node_initializer, 'initialize_toolsets') as mock_toolsets, patch.object(worker_ultra_node_initializer, 'initialize_vectorstore') as mock_vectorstore:
+    with patch.object(worker_ultra_node_initializer, 'initialize_llm'), patch.object(worker_ultra_node_initializer, 'initialize_toolsets'), patch.object(worker_ultra_node_initializer, 'initialize_vectorstore'):
         worker_node = worker_ultra_node_initializer.create_worker_node()
         assert worker_node is not None

tests/boss/boss_node.py

@@ -1,6 +1,5 @@
 import pytest
 from unittest.mock import Mock, patch
-from pydantic import ValidationError
 from swarms.agents.tools.agent_tools import *
 from swarms.boss.boss_node import BossNodeInitializer, BossNode
 # For initializing BossNodeInitializer in multiple tests

tests/swarms.py

@@ -1,6 +1,6 @@
 import pytest
 import logging
-from unittest.mock import Mock, patch
+from unittest.mock import patch
 from swarms.swarms import HierarchicalSwarm  # replace with your actual module name
 
 @pytest.fixture
@@ -69,5 +69,5 @@ def test_initialize_tools_with_extra_tools(mock_llm, swarm):
 @patch('your_module.FAISS')
 def test_initialize_vectorstore(mock_faiss, mock_openai_embeddings, swarm):
     mock_openai_embeddings.return_value.embed_query = 'embed_query'
-    vectorstore = swarm.initialize_vectorstore()
+    swarm.initialize_vectorstore()
     mock_faiss.assert_called_once_with('embed_query', instance_of(faiss.IndexFlatL2), instance_of(InMemoryDocstore), {})