clean up

Former-commit-id: 6d7fca8402

apps/open-sourcerer/main.py

@@ -14,9 +14,11 @@ interpreter.api_key = os.getenv("OPENAI_API_KEY")
 # interpreter.api_base = os.getenv("API_BASE")
 # interpreter.auto_run = True
 
+
 def split_text(text, chunk_size=1500):
     #########################################################################
-    return [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
+    return [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)]
+
 
 # discord initial
 intents = discord.Intents.all()
@@ -28,6 +30,7 @@ send_image = False
 
 model = whisper.load_model("base")
 
+
 def transcribe(audio):
 
     # load audio and pad/trim it to fit 30 seconds
@@ -45,6 +48,7 @@ def transcribe(audio):
     result = whisper.decode(model, mel, options)
     return result.text
 
+
 @client.event
 async def on_message(message):
     await client.process_commands(message)

apps/open-sourcerer/voice.py

@@ -13,7 +13,6 @@ dotenv.load_dotenv(".env")
 interpreter.auto_run = True
 
 
-
 set_api_key("ELEVEN_LABS_API_KEY")
 
 

playground/models/fuyu.py

@@ -2,5 +2,3 @@ from swarms.models import Fuyu
 
 fuyu = Fuyu()
 fuyu("Hello, my name is", "images/github-banner-swarms.png")
-
-    

swarms/__init__.py

@@ -1,22 +1,20 @@
+from swarms import workers
+from swarms.workers.worker import Worker
+from swarms.chunkers import chunkers
+from swarms import models
+from swarms import structs
+from swarms import swarms
+from swarms.swarms.orchestrate import Orchestrator
+from swarms import agents
+from swarms.logo import logo
+import os
 import warnings
 
 warnings.filterwarnings("ignore", category=UserWarning)
 
 # disable tensorflow warnings
-import os
 
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
 
 
-from swarms.logo import logo
-
 print(logo)
-
-from swarms import agents
-from swarms.swarms.orchestrate import Orchestrator
-from swarms import swarms
-from swarms import structs
-from swarms import models
-from swarms.chunkers import chunkers
-from swarms.workers.worker import Worker
-from swarms import workers

swarms/agents/__init__.py

@@ -1,12 +1,12 @@
-"""Agent Infrastructure, models, memory, utils, tools"""
-"""Agent Infrastructure, models, memory, utils, tools"""
 from swarms.agents.omni_modal_agent import OmniModalAgent
 from swarms.agents.hf_agents import HFAgent
-
-
-# utils
 from swarms.agents.message import Message
 from swarms.agents.stream_response import stream
 from swarms.agents.base import AbstractAgent
 from swarms.agents.registry import Registry
 from swarms.agents.idea_to_image_agent import Idea2Image
+"""Agent Infrastructure, models, memory, utils, tools"""
+"""Agent Infrastructure, models, memory, utils, tools"""
+
+
+# utils

swarms/agents/meta_prompter.py

@@ -108,7 +108,7 @@ class MetaPrompterAgent:
     def get_new_instructions(self, meta_output):
         """Get New Instructions from the meta_output"""
         delimiter = "Instructions: "
-        new_instructions = meta_output[meta_output.find(delimiter) + len(delimiter) :]
+        new_instructions = meta_output[meta_output.find(delimiter) + len(delimiter):]
         return new_instructions
 
     def run(self, task: str):

swarms/agents/multi_modal_visual_agent.py

@@ -207,12 +207,12 @@ def blend_gt2pt(old_image, new_image, sigma=0.15, steps=100):
     kernel[steps:-steps, :steps] = left
     kernel[steps:-steps, -steps:] = right
 
-    pt_gt_img = easy_img[pos_h : pos_h + old_size[1], pos_w : pos_w + old_size[0]]
+    pt_gt_img = easy_img[pos_h: pos_h + old_size[1], pos_w: pos_w + old_size[0]]
     gaussian_gt_img = (
         kernel * gt_img_array + (1 - kernel) * pt_gt_img
     )  # gt img with blur img
     gaussian_gt_img = gaussian_gt_img.astype(np.int64)
-    easy_img[pos_h : pos_h + old_size[1], pos_w : pos_w + old_size[0]] = gaussian_gt_img
+    easy_img[pos_h: pos_h + old_size[1], pos_w: pos_w + old_size[0]] = gaussian_gt_img
     gaussian_img = Image.fromarray(easy_img)
     return gaussian_img
 

swarms/chunkers/base.py

@@ -58,7 +58,7 @@ class BaseChunker(ABC):
             half_token_count = token_count // 2
 
             if current_separator:
-                separators = self.separators[self.separators.index(current_separator) :]
+                separators = self.separators[self.separators.index(current_separator):]
             else:
                 separators = self.separators
 
@@ -84,7 +84,7 @@ class BaseChunker(ABC):
                             subchanks[: balance_index + 1]
                         )
                         second_subchunk = separator.value + separator.value.join(
-                            subchanks[balance_index + 1 :]
+                            subchanks[balance_index + 1:]
                         )
                     else:
                         first_subchunk = (
@@ -92,7 +92,7 @@ class BaseChunker(ABC):
                             + separator.value
                         )
                         second_subchunk = separator.value.join(
-                            subchanks[balance_index + 1 :]
+                            subchanks[balance_index + 1:]
                         )
 
                     first_subchunk_rec = self._chunk_recursively(

swarms/embeddings/openai.py

@@ -347,7 +347,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
                 disallowed_special=self.disallowed_special,
             )
             for j in range(0, len(token), self.embedding_ctx_length):
-                tokens.append(token[j : j + self.embedding_ctx_length])
+                tokens.append(token[j: j + self.embedding_ctx_length])
                 indices.append(i)
 
         batched_embeddings: List[List[float]] = []
@@ -366,7 +366,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
         for i in _iter:
             response = embed_with_retry(
                 self,
-                input=tokens[i : i + _chunk_size],
+                input=tokens[i: i + _chunk_size],
                 **self._invocation_params,
             )
             batched_embeddings.extend(r["embedding"] for r in response["data"])
@@ -428,7 +428,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
                 disallowed_special=self.disallowed_special,
             )
             for j in range(0, len(token), self.embedding_ctx_length):
-                tokens.append(token[j : j + self.embedding_ctx_length])
+                tokens.append(token[j: j + self.embedding_ctx_length])
                 indices.append(i)
 
         batched_embeddings: List[List[float]] = []
@@ -436,7 +436,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
         for i in range(0, len(tokens), _chunk_size):
             response = await async_embed_with_retry(
                 self,
-                input=tokens[i : i + _chunk_size],
+                input=tokens[i: i + _chunk_size],
                 **self._invocation_params,
             )
             batched_embeddings.extend(r["embedding"] for r in response["data"])

swarms/models/__init__.py

@@ -4,7 +4,6 @@ from swarms.models.mistral import Mistral
 from swarms.models.openai_models import OpenAI, AzureOpenAI, OpenAIChat
 
 
-
 # MultiModal Models
 from swarms.models.idefics import Idefics
 from swarms.models.kosmos_two import Kosmos

swarms/models/bing_chat.py

@@ -6,6 +6,7 @@ from EdgeGPT.EdgeUtils import ImageQuery, Query, Cookie
 from EdgeGPT.ImageGen import ImageGen
 from pathlib import Path
 
+
 class BingChat:
     """
     EdgeGPT model by OpenAI

swarms/models/chat_openai.py

@@ -458,7 +458,7 @@ class BaseOpenAI(BaseLLM):
                 )
             params["max_tokens"] = self.max_tokens_for_prompt(prompts[0])
         sub_prompts = [
-            prompts[i : i + self.batch_size]
+            prompts[i: i + self.batch_size]
             for i in range(0, len(prompts), self.batch_size)
         ]
         return sub_prompts
@@ -469,7 +469,7 @@ class BaseOpenAI(BaseLLM):
         """Create the LLMResult from the choices and prompts."""
         generations = []
         for i, _ in enumerate(prompts):
-            sub_choices = choices[i * self.n : (i + 1) * self.n]
+            sub_choices = choices[i * self.n: (i + 1) * self.n]
             generations.append(
                 [
                     Generation(

swarms/models/fuyu.py

@@ -30,6 +30,7 @@ class Fuyu:
 
 
     """
+
     def __init__(
         self,
         pretrained_path: str = "adept/fuyu-8b",

swarms/models/openai_models.py

@@ -462,7 +462,7 @@ class BaseOpenAI(BaseLLM):
                 )
             params["max_tokens"] = self.max_tokens_for_prompt(prompts[0])
         sub_prompts = [
-            prompts[i : i + self.batch_size]
+            prompts[i: i + self.batch_size]
             for i in range(0, len(prompts), self.batch_size)
         ]
         return sub_prompts
@@ -473,7 +473,7 @@ class BaseOpenAI(BaseLLM):
         """Create the LLMResult from the choices and prompts."""
         generations = []
         for i, _ in enumerate(prompts):
-            sub_choices = choices[i * self.n : (i + 1) * self.n]
+            sub_choices = choices[i * self.n: (i + 1) * self.n]
             generations.append(
                 [
                     Generation(

swarms/tools/autogpt.py

@@ -125,7 +125,7 @@ class WebpageQATool(BaseTool):
         results = []
         # TODO: Handle this with a MapReduceChain
         for i in range(0, len(web_docs), 4):
-            input_docs = web_docs[i : i + 4]
+            input_docs = web_docs[i: i + 4]
             window_result = self.qa_chain(
                 {"input_documents": input_docs, "question": question},
                 return_only_outputs=True,

swarms/tools/developer.py

@@ -306,7 +306,7 @@ class WriteCommand:
     @staticmethod
     def from_str(command: str) -> "WriteCommand":
         filepath = command.split(WriteCommand.separator)[0]
-        return WriteCommand(filepath, command[len(filepath) + 1 :])
+        return WriteCommand(filepath, command[len(filepath) + 1:])
 
 
 class CodeWriter:
@@ -433,7 +433,7 @@ class ReadCommand:
         if self.start == self.end:
             code = code[self.start - 1]
         else:
-            code = "".join(code[self.start - 1 : self.end])
+            code = "".join(code[self.start - 1: self.end])
         return code
 
     @staticmethod
@@ -590,9 +590,9 @@ class PatchCommand:
         lines[self.start.line] = (
             lines[self.start.line][: self.start.col]
             + self.content
-            + lines[self.end.line][self.end.col :]
+            + lines[self.end.line][self.end.col:]
         )
-        lines = lines[: self.start.line + 1] + lines[self.end.line + 1 :]
+        lines = lines[: self.start.line + 1] + lines[self.end.line + 1:]
 
         after = self.write_lines(lines)
 

swarms/tools/tool.py

@@ -577,6 +577,7 @@ class Tool(BaseTool):
             **kwargs,
         )
 
+
 class EdgeGPTTool:
     def __init__(self, model):
         self.model = model
@@ -584,6 +585,7 @@ class EdgeGPTTool:
     def run(self, prompt):
         return self.model.ask(prompt)
 
+
 class StructuredTool(BaseTool):
     """Tool that can operate on any number of inputs."""
 
@@ -850,10 +852,4 @@ def tool(
     else:
         raise ValueError("Too many arguments for tool decorator")
 
-class EdgeGPTTool(BaseTool):
-    def __init__(self, model, name="EdgeGPTTool", description="Tool that uses EdgeGPTModel to generate responses"):
-        super().__init__(name=name, description=description)
-        self.model = model
 
-    def _run(self, prompt):
-        return self.model.__call__(prompt)

swarms/utils/main.py

@@ -365,7 +365,7 @@ class FileHandler:
         try:
             if url.startswith(os.environ.get("SERVER", "http://localhost:8000")):
                 local_filepath = url[
-                    len(os.environ.get("SERVER", "http://localhost:8000")) + 1 :
+                    len(os.environ.get("SERVER", "http://localhost:8000")) + 1:
                 ]
                 local_filename = Path("file") / local_filepath.split("/")[-1]
                 src = self.path / local_filepath