Merge e238ffd2ca into 59055c764c

Dockerfile

@@ -1,25 +1,25 @@
 
 # ==================================
-# Use an official Python runtime as a parent image
+# Use an official Python runtime as a parent image image
 FROM python:3.9-slim
-RUN apt-get update && apt-get -y install libgl1-mesa-dev libglib2.0-0 build-essential; apt-get clean
+#RUN apt-get update && apt-get -y install libgl1-mesa-dev libglib2.0-0 build-essential; apt-get cleangl1-mesa-dev libglib2.0-0 build-essential; apt-get clean
-RUN pip install opencv-contrib-python-headless
+#RUN pip install opencv-contrib-python-headlessss
 
 # Set environment variables
 ENV PYTHONDONTWRITEBYTECODE 1
 ENV PYTHONUNBUFFERED 1
 
 # Set the working directory in the container
-WORKDIR /usr/src/swarm_cloud
+WORKDIR /usr/src/swarms
 
 
 # Install Python dependencies
-# COPY requirements.txt and pyproject.toml if you're using poetry for dependency management
+# COPY requirements.txt and pyproject.toml if you're using poetry for dependency managementf you're using poetry for dependency management
 COPY requirements.txt .
 RUN pip install --upgrade pip
 RUN pip install --no-cache-dir -r requirements.txt
 
-# Install the 'swarms' package, assuming it's available on PyPI
+# Install the 'swarms' package, assuming it's available on PyPIs available on PyPI
 RUN pip install swarms
 
 # Copy the rest of the application
@@ -27,18 +27,18 @@ COPY . .
 
 # Add entrypoint script if needed
 # COPY ./entrypoint.sh .
-# RUN chmod +x /usr/src/swarm_cloud/entrypoint.sh
+# RUN chmod +x /usr/src/swarm_cloud/entrypoint.shnt.sh
 
-# Expose port if your application has a web interface
+# Expose port if your application has a web interfaceinterface
 # EXPOSE 5000
 
-# # Define environment variable for the swarm to work
+# # Define environment variable for the swarm to workm to work
 # ENV SWARM_API_KEY=your_swarm_api_key_here
 
-# # Add Docker CMD or ENTRYPOINT script to run the application
+# # Add Docker CMD or ENTRYPOINT script to run the applicationun the application
 # CMD python your_swarm_startup_script.py
-# Or use the entrypoint script if you have one
+# Or use the entrypoint script if you have onene
-# ENTRYPOINT ["/usr/src/swarm_cloud/entrypoint.sh"]
+# ENTRYPOINT ["/usr/src/swarm_cloud/entrypoint.sh"]nt.sh"]
 
-# If you're using `CMD` to execute a Python script, make sure it's executable
+# If you're using `CMD` to execute a Python script, make sure it's executablescript, make sure it's executable
 # RUN chmod +x your_swarm_startup_script.py

README.md

@@ -42,14 +42,9 @@ Features:
 ```python
 import os
 
-from dotenv import load_dotenv
-
 # Import the OpenAIChat model and the Agent struct
 from swarms import Agent, OpenAIChat
 
-# Load the environment variables
-load_dotenv()
-
 # Get the API key from the environment
 api_key = os.environ.get("OPENAI_API_KEY")
 
@@ -138,13 +133,8 @@ The `Worker` is a simple all-in-one agent equipped with an LLM, tools, and RAG f
 # Importing necessary modules
 import os
 
-from dotenv import load_dotenv
-
 from swarms import OpenAIChat, Worker, tool
 
-# Loading environment variables from .env file
-load_dotenv()
-
 # Retrieving the OpenAI API key from environment variables
 api_key = os.getenv("OPENAI_API_KEY")
 
@@ -279,12 +269,8 @@ Sequential Workflow enables you to sequentially execute tasks with `Agent` and t
 ```python
 import os
 
-from dotenv import load_dotenv
-
 from swarms import Agent, OpenAIChat, SequentialWorkflow
 
-load_dotenv()
-
 # Load the environment variables
 api_key = os.getenv("OPENAI_API_KEY")
 
@@ -336,13 +322,8 @@ for task in workflow.tasks:
 ```python
 import os
 
-from dotenv import load_dotenv
-
 from swarms import Agent, ConcurrentWorkflow, OpenAIChat, Task
 
-# Load environment variables from .env file
-load_dotenv()
-
 # Load environment variables
 llm = OpenAIChat(openai_api_key=os.getenv("OPENAI_API_KEY"))
 agent = Agent(llm=llm, max_loops=1)
@@ -368,13 +349,8 @@ workflow.run()
 ```python
 import os
 
-from dotenv import load_dotenv
-
 from swarms import Agent, OpenAIChat, RecursiveWorkflow, Task
 
-# Load environment variables from .env file
-load_dotenv()
-
 # Load environment variables
 llm = OpenAIChat(openai_api_key=os.getenv("OPENAI_API_KEY"))
 agent = Agent(llm=llm, max_loops=1)
@@ -407,12 +383,8 @@ Plug-and-Play Integration: The structure provides a seamless integration with va
 ```python
 import os
 
-from dotenv import load_dotenv
-
 from swarms import Anthropic, Gemini, Mixtral, ModelParallelizer, OpenAIChat
 
-load_dotenv()
-
 # API Keys
 anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
 openai_api_key = os.getenv("OPENAI_API_KEY")
@@ -449,17 +421,12 @@ A Plug in and play conversational agent with `GPT4`, `Mixytral`, or any of our m
 ```python
 import os
 
-from dotenv import load_dotenv
-
 from swarms import Conversation, OpenAIChat
 
 conv = Conversation(
     time_enabled=True,
 )
 
-# Load the environment variables
-load_dotenv()
-
 # Get the API key from the environment
 api_key = os.environ.get("OPENAI_API_KEY")
 
@@ -502,14 +469,9 @@ interactive_conversation(llm)
 ```python
 import os
 
-from dotenv import load_dotenv
-
 # Import the OpenAIChat model and the Agent struct
 from swarms import Agent, OpenAIChat, SwarmNetwork
 
-# Load the environment variables
-load_dotenv()
-
 # Get the API key from the environment
 api_key = os.environ.get("OPENAI_API_KEY")
 
@@ -559,14 +521,8 @@ print(out)
 ```python
 import os
 
-from dotenv import load_dotenv
-
 from swarms.structs import Agent, OpenAIChat, Task
 
-# Load the environment variables
-load_dotenv()
-
-
 # Define a function to be used as the action
 def my_action():
     print("Action executed")
@@ -626,14 +582,10 @@ print(f"Task result: {task.result}")
 ```python
 import os
 
-from dotenv import load_dotenv
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from pydantic import BaseModel
 from swarms import BlocksList, Gemini, GPT4VisionAPI, Mixtral, OpenAI, ToolAgent
 
-# Load the environment variables
-load_dotenv()
-
 # Get the environment variables
 openai_api_key = os.getenv("OPENAI_API_KEY")
 gemini_api_key = os.getenv("GEMINI_API_KEY")
@@ -774,8 +726,6 @@ Here's a production grade swarm ready for real-world deployment in a factory and
 ```python
 import os
 
-from dotenv import load_dotenv
-
 from swarms.models import GPT4VisionAPI
 from swarms.prompts.logistics import (
     Efficiency_Agent_Prompt,
@@ -788,8 +738,6 @@ from swarms.prompts.logistics import (
 )
 from swarms.structs import Agent
 
-# Load ENV
-load_dotenv()
 api_key = os.getenv("OPENAI_API_KEY")
 
 # GPT4VisionAPI
@@ -885,14 +833,9 @@ Run the agent with multiple modalities useful for various real-world tasks in ma
 # Description: This is an example of how to use the Agent class to run a multi-modal workflow
 import os
 
-from dotenv import load_dotenv
-
 from swarms.models.gpt4_vision_api import GPT4VisionAPI
 from swarms.structs import Agent
 
-# Load the environment variables
-load_dotenv()
-
 # Get the API key from the environment
 api_key = os.environ.get("OPENAI_API_KEY")
 

docs/applications/discord.md

@@ -12,7 +12,7 @@ This code provides a structure for a Discord bot with advanced features such as
 
 1. Ensure that the necessary libraries are installed:
 ```bash
-pip install discord.py python-dotenv dalle3 invoke openai
+pip install discord.py dalle3 invoke openai
 ```
 
 2. Create a `.env` file in the same directory as your bot script and add the following:

docs/examples/revgpt.md

@@ -35,23 +35,16 @@ The abstraction provided in `revgpt.py` is designed to simplify your interaction
 1. **Import the Necessary Modules:**
 
 ```python
-from dotenv import load_dotenv
 from revgpt import AbstractChatGPT
 ```
 
-2. **Load Environment Variables:**
+2. **Initialize the ChatGPT Abstract Class:**
-
-```python
-load_dotenv()
-```
-
-3. **Initialize the ChatGPT Abstract Class:**
 
 ```python
 chat = AbstractChatGPT(api_key=os.getenv("ACCESS_TOKEN"), **config)
 ```
 
-4. **Start Interacting with ChatGPT:**
+3. **Start Interacting with ChatGPT:**
 
 ```python
 response = chat.ask("Hello, ChatGPT!")

docs/swarms/agents/omni_agent.md

@@ -41,14 +41,9 @@ Initialize the `OmniModalAgent` and communicate with it:
 ```python
 import os
 
-from dotenv import load_dotenv
-
 from swarms.agents.omni_modal_agent import OmniModalAgent, OpenAIChat
 from swarms.models import OpenAIChat
 
-# Load the environment variables
-load_dotenv()
-
 # Get the API key from the environment
 api_key = os.environ.get("OPENAI_API_KEY")
 

docs/swarms/memory/azure_openai.md

@@ -10,7 +10,7 @@ Before we begin, it's essential to have the following prerequisites in place:
 1. **Python**: You'll need to have Python installed on your system. This guide assumes you're using Python 3.6 or later.
 2. **Azure Subscription**: You'll need an active Azure subscription to access Azure OpenAI services.
 3. **Azure OpenAI Resource**: Create an Azure OpenAI resource in your Azure subscription.
-4. **Python Packages**: Install the required Python packages, including `python-dotenv` and `swarms`.
+4. **Python Packages**: Install the required Python packages, including `swarms`.
 
 ## Setting up the Environment:
 To kick things off, we'll set up our development environment and install the necessary dependencies.
@@ -27,10 +27,10 @@ python -m venv myenv
 source myenv/bin/activate  # On Windows, use `myenv\Scripts\activate`
 ```
 
-3. **Install Required Packages**: Install the `python-dotenv` and `swarms` packages using pip.
+3. **Install Required Packages**: Install the `swarms` packages using pip.
 
 ```
-pip install python-dotenv swarms
+pip install swarms
 ```
 
 4. **Create a `.env` File**: In the root directory of your project, create a new file called `.env`. This file will store your Azure OpenAI credentials and configuration settings.
@@ -50,12 +50,8 @@ Now that we've set up our environment, let's dive into the code that connects to
 
 ```python
 import os
-from dotenv import load_dotenv
 from swarms import AzureOpenAI
 
-# Load the environment variables
-load_dotenv()
-
 # Create an instance of the AzureOpenAI class
 model = AzureOpenAI(
     azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
@@ -68,11 +64,9 @@ model = AzureOpenAI(
 
 ## Let's break down this code:
 
-1. **Import Statements**: We import the necessary modules, including `os` for interacting with the operating system, `load_dotenv` from `python-dotenv` to load environment variables, and `AzureOpenAI` from `swarms` to interact with the Azure OpenAI service.
+1. **Import Statements**: We import the necessary modules, including `os` for interacting with the operating system, `AzureOpenAI` from `swarms` to interact with the Azure OpenAI service.
-
-2. **Load Environment Variables**: We use `load_dotenv()` to load the environment variables stored in the `.env` file we created earlier.
 
-3. **Create AzureOpenAI Instance**: We create an instance of the `AzureOpenAI` class by passing in the required configuration parameters:
+2. **Create AzureOpenAI Instance**: We create an instance of the `AzureOpenAI` class by passing in the required configuration parameters:
    - `azure_endpoint`: The endpoint URL for your Azure OpenAI resource.
    - `deployment_name`: The name of the deployment you want to use.
    - `openai_api_version`: The version of the OpenAI API you want to use.

docs/swarms/models/gemini.md

@@ -15,7 +15,6 @@ Before using Gemini, ensure that you have the required dependencies installed. Y
 ```bash
 pip install swarms
 pip install google-generativeai
-pip install python-dotenv
 ```
 
 ### Class: Gemini

playground/agents/multi_modal_auto_agent_example.py

@@ -1,13 +1,8 @@
 # Description: This is an example of how to use the Agent class to run a multi-modal workflow
 import os
 
-from dotenv import load_dotenv
-
 from swarms import Agent, GPT4VisionAPI
 
-# Load the environment variables
-load_dotenv()
-
 # Get the API key from the environment
 api_key = os.environ.get("OPENAI_API_KEY")
 

playground/agents/perimeter_defense_agent.py

@@ -1,13 +1,9 @@
 import os
 
-from dotenv import load_dotenv
-
 import swarms.prompts.security_team as stsp
 from swarms.models import GPT4VisionAPI
 from swarms.structs import Agent
 
-# Load environment variables and initialize the Vision API
-load_dotenv()
 api_key = os.getenv("OPENAI_API_KEY")
 
 llm = GPT4VisionAPI(openai_api_key=api_key)

playground/agents/simple_agent_example.py

@@ -1,7 +1,5 @@
 import os
 
-from dotenv import load_dotenv
-
 from swarms import (
     Conversation,
     OpenAIChat,
@@ -15,9 +13,6 @@ conv = Conversation(
     time_enabled=True,
 )
 
-# Load the environment variables
-load_dotenv()
-
 # Get the API key from the environment
 api_key = os.environ.get("OPENAI_API_KEY")
 

playground/agents/worker_example.py

@@ -1,14 +1,9 @@
 # Importing necessary modules
 import os
 
-from dotenv import load_dotenv
-
 from swarms import OpenAIChat
 from swarms.agents.worker_agent import Worker
 
-# Loading environment variables from .env file
-load_dotenv()
-
 # Retrieving the OpenAI API key from environment variables
 api_key = os.getenv("OPENAI_API_KEY")
 

playground/demos/accountant_team/account_team2_example.py

@@ -1,7 +1,5 @@
 import os
 
-from dotenv import load_dotenv
-
 from swarms.models import Anthropic, OpenAIChat
 from swarms.prompts.accountant_swarm_prompts import (
     DECISION_MAKING_PROMPT,
@@ -12,7 +10,6 @@ from swarms.structs import Agent
 from swarms.utils.pdf_to_text import pdf_to_text
 
 # Environment variables
-load_dotenv()
 anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
 openai_api_key = os.getenv("OPENAI_API_KEY")
 

playground/demos/ad_gen/ad_gen_example.py

@@ -1,13 +1,10 @@
 import os
 import random
 
-from dotenv import load_dotenv
-
 from swarms.models import OpenAIChat
 from swarms.models.stable_diffusion import StableDiffusion
 from swarms.structs import Agent
 
-load_dotenv()
 openai_api_key = os.getenv("OPENAI_API_KEY")
 stability_api_key = os.getenv("STABILITY_API_KEY")
 

playground/demos/ai_acceleerated_learning/main.py

@@ -2,15 +2,11 @@ import concurrent
 import csv
 from swarms import Agent, OpenAIChat
 from swarms.memory import ChromaDB
-from dotenv import load_dotenv
 from swarms.utils.parse_code import extract_code_from_markdown
 from swarms.utils.file_processing import create_file
 from swarms.utils.loguru_logger import logger
 
 
-# Load ENV
-load_dotenv()
-
 # Gemini
 gemini = OpenAIChat()
 

playground/demos/ai_research_team/main_example.py

@@ -1,7 +1,5 @@
 import os
 
-from dotenv import load_dotenv
-
 from swarms.models import Anthropic, OpenAIChat
 from swarms.prompts.ai_research_team import (
     PAPER_IMPLEMENTOR_AGENT_PROMPT,
@@ -12,7 +10,6 @@ from swarms.utils.pdf_to_text import pdf_to_text
 
 # Base llms
 # Environment variables
-load_dotenv()
 anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
 openai_api_key = os.getenv("OPENAI_API_KEY")
 

playground/demos/autoswarm/autoswarm.py

@@ -1,11 +1,9 @@
 import os
-from dotenv import load_dotenv
 from swarms.models import OpenAIChat
 from swarms.structs import Agent
 import swarms.prompts.autoswarm as sdsp
 
 # Load environment variables and initialize the OpenAI Chat model
-load_dotenv()
 api_key = os.getenv("OPENAI_API_KEY")
 llm = OpenAIChat(model_name="gpt-4", openai_api_key=api_key)
 

playground/demos/developer_swarm/main_example.py

@@ -17,13 +17,10 @@ Documentation agent -> Tests agent
 
 import os
 
-from dotenv import load_dotenv
-
 from swarms.models import OpenAIChat
 from swarms.prompts.programming import DOCUMENTATION_SOP, TEST_SOP
 from swarms.structs import Agent
 
-load_dotenv()
 api_key = os.getenv("OPENAI_API_KEY")
 
 

playground/demos/education/education_example.py

@@ -1,13 +1,10 @@
 import os
 
-from dotenv import load_dotenv
-
 import swarms.prompts.education as edu_prompts
 from swarms import Agent, SequentialWorkflow
 from swarms.models import OpenAIChat
 
 # Load environment variables
-load_dotenv()
 api_key = os.getenv("OPENAI_API_KEY")
 stability_api_key = os.getenv("STABILITY_API_KEY")
 

playground/demos/gemini_benchmarking/gemini_chat_example.py

@@ -1,12 +1,8 @@
 import os
 
-from dotenv import load_dotenv
-
 from swarms.models.gemini import Gemini
 from swarms.prompts.react import react_prompt
 
-load_dotenv()
-
 api_key = os.environ["GEMINI_API_KEY"]
 
 # Establish the prompt and image

playground/demos/gemini_benchmarking/gemini_react_example.py

@@ -1,12 +1,8 @@
 import os
 
-from dotenv import load_dotenv
-
 from swarms.models.gemini import Gemini
 from swarms.prompts.react import react_prompt
 
-load_dotenv()
-
 api_key = os.environ["GEMINI_API_KEY"]
 
 # Establish the prompt and image

playground/demos/gemini_benchmarking/gemini_vcot_example.py

@@ -1,13 +1,8 @@
 import os
 
-from dotenv import load_dotenv
-
 from swarms.models import Gemini
 from swarms.prompts.visual_cot import VISUAL_CHAIN_OF_THOUGHT
 
-# Load the environment variables
-load_dotenv()
-
 # Get the API key from the environment
 api_key = os.environ.get("GEMINI_API_KEY")
 

playground/demos/grupa/app_example.py

@@ -1,6 +1,5 @@
 import os
 
-from dotenv import load_dotenv
 from termcolor import colored
 
 from swarms.models import OpenAIChat
@@ -8,9 +7,6 @@ from swarms.prompts.code_interpreter import CODE_INTERPRETER
 from swarms.prompts.programming import DOCUMENTATION_SOP, TEST_SOP
 from swarms.structs import Agent
 
-load_dotenv()
-
-
 FEATURE = (
     "Implement an all-new signup system in typescript using supabase"
 )

playground/demos/langchain_example/langchain_example.py

@@ -1,13 +1,9 @@
 import os
 
-from dotenv import load_dotenv
 from langchain.llms import OpenAIChat
 
 from swarms import Agent
 
-# Loading environment variables from .env file
-load_dotenv()
-
 # Initialize the model
 llm = OpenAIChat(
     openai_api_key=os.getenv("OPENAI_API_KEY"),

playground/demos/llm_with_conversation/main_example.py

@@ -1,13 +1,8 @@
 import os
 
-from dotenv import load_dotenv
-
 # Import the OpenAIChat model and the Agent struct
 from swarms.models import OpenAIChat
 
-# Load the environment variables
-load_dotenv()
-
 # Get the API key from the environment
 api_key = os.environ.get("OPENAI_API_KEY")
 

playground/demos/logistics/logistics_example.py

@@ -1,7 +1,5 @@
 import os
 
-from dotenv import load_dotenv
-
 from swarms.models import GPT4VisionAPI
 from swarms.prompts.logistics import (
     Efficiency_Agent_Prompt,
@@ -15,8 +13,6 @@ from swarms.prompts.logistics import (
 from swarms.structs import Agent
 
 # from swarms.utils.banana_wrapper import banana
-
-load_dotenv()
 api_key = os.getenv("OPENAI_API_KEY")
 
 # GPT4VisionAPI or llama

playground/demos/multi_modal_chain_of_thought/vcot_example.py

@@ -1,14 +1,10 @@
 import os
 
-from dotenv import load_dotenv
 
 from swarms.models.gpt4_vision_api import GPT4VisionAPI
 from swarms.prompts.visual_cot import VISUAL_CHAIN_OF_THOUGHT
 from swarms.structs import Agent
 
-# Load the environment variables
-load_dotenv()
-
 # Get the API key from the environment
 api_key = os.environ.get("OPENAI_API_KEY")
 

playground/demos/multimodal_tot/idea2img_example.py

@@ -2,7 +2,6 @@ import datetime
 import os
 
 import streamlit as st
-from dotenv import load_dotenv
 
 from swarms.models import OpenAIChat
 from swarms.models.gpt4_vision_api import GPT4VisionAPI
@@ -10,7 +9,6 @@ from swarms.models.stable_diffusion import StableDiffusion
 from swarms.structs import Agent
 
 # Load environment variables
-load_dotenv()
 openai_api_key = os.getenv("OPENAI_API_KEY")
 stability_api_key = os.getenv("STABLE_API_KEY")
 
@@ -137,7 +135,6 @@ class Idea2Image(Agent):
 
 
 # Load environment variables and initialize the models
-load_dotenv()
 openai_api_key = os.getenv("OPENAI_API_KEY")
 stability_api_key = os.getenv("STABLE_API_KEY")
 vision_api = GPT4VisionAPI(api_key=openai_api_key)

playground/demos/multimodal_tot/main_example.py

@@ -17,15 +17,11 @@ task: Generate an image of a swarm of bees -> Image generator -> GPT4V evaluates
 
 import os
 
-from dotenv import load_dotenv
 from termcolor import colored
 
 from swarms.models.gpt4_vision_api import GPT4VisionAPI
 from swarms.models.stable_diffusion import StableDiffusion
 
-# Load the environment variables
-load_dotenv()
-
 # Get the API key from the environment
 api_key = os.environ.get("OPENAI_API_KEY")
 stable_api_key = os.environ.get("STABLE_API_KEY")

playground/demos/nutrition/nutrition_example.py

@@ -2,13 +2,10 @@ import base64
 import os
 
 import requests
-from dotenv import load_dotenv
 
 from swarms.models import OpenAIChat
 from swarms.structs import Agent
 
-# Load environment variables
-load_dotenv()
 openai_api_key = os.getenv("OPENAI_API_KEY")
 
 # Define prompts for various tasks

playground/demos/optimize_llm_stack/vortex_example.py

@@ -1,14 +1,10 @@
 import os
 
-from dotenv import load_dotenv
-
 from swarms.models import OpenAIChat
 from swarms.structs import Agent
 
 # import modal
 
-load_dotenv()
-
 # Model
 llm = OpenAIChat(
     openai_api_key=os.getenv("OPENAI_API_KEY"),

playground/demos/personal_assistant/better_communication_example.py

@@ -3,14 +3,10 @@ import time
 
 import pygame
 import speech_recognition as sr
-from dotenv import load_dotenv
 from playsound import playsound
 
 from swarms import OpenAIChat, OpenAITTS
 
-# Load the environment variables
-load_dotenv()
-
 # Get the API key from the environment
 openai_api_key = os.environ.get("OPENAI_API_KEY")
 

playground/demos/personal_stylist/personal_stylist_example.py

@@ -1,7 +1,5 @@
 import os
 
-from dotenv import load_dotenv
-
 from swarms.models import GPT4VisionAPI
 from swarms.prompts.personal_stylist import (
     ACCESSORIES_STYLIST_AGENT_PROMPT,
@@ -12,8 +10,6 @@ from swarms.prompts.personal_stylist import (
 )
 from swarms.structs import Agent
 
-# Load environment variables
-load_dotenv()
 api_key = os.getenv("OPENAI_API_KEY")
 
 # Initialize GPT4VisionAPI

playground/demos/security_team/security_team_example.py

@@ -1,14 +1,11 @@
 import os
 
-from dotenv import load_dotenv
 from termcolor import colored
 
 import swarms.prompts.security_team as stsp
 from swarms.models import GPT4VisionAPI
 from swarms.structs import Agent
 
-# Load environment variables and initialize the Vision API
-load_dotenv()
 api_key = os.getenv("OPENAI_API_KEY")
 
 llm = GPT4VisionAPI(openai_api_key=api_key)

playground/demos/swarm_hackathon/main.py

@@ -3,15 +3,10 @@ import csv
 import os
 from swarms import Gemini, Agent
 from swarms.memory import ChromaDB
-from dotenv import load_dotenv
 from swarms.utils.parse_code import extract_code_from_markdown
 from swarms.utils.file_processing import create_file
 from swarms.utils.loguru_logger import logger
 
-# Load ENV
-load_dotenv()
-
-
 gemini = Gemini(
     model_name="gemini-pro",
     gemini_api_key=os.getenv("GEMINI_API_KEY"),

playground/demos/swarm_of_mma_manufacturing/main_example.py

@@ -16,13 +16,11 @@ health security agent -> quality control agent -> productivity agent -> safety a
 
 import os
 
-from dotenv import load_dotenv
 from termcolor import colored
 
 from swarms.models import GPT4VisionAPI
 from swarms.structs import Agent
 
-load_dotenv()
 api_key = os.getenv("OPENAI_API_KEY")
 
 # GPT4VisionAPI

playground/demos/urban_planning/urban_planning_example.py

@@ -1,13 +1,9 @@
 import os
 
-from dotenv import load_dotenv
-
 import swarms.prompts.urban_planning as upp
 from swarms.models import GPT4VisionAPI, OpenAIChat
 from swarms.structs import Agent, SequentialWorkflow
 
-# Load environment variables
-load_dotenv()
 api_key = os.getenv("OPENAI_API_KEY")
 stability_api_key = os.getenv("STABILITY_API_KEY")
 

playground/demos/visuo/text_to_sql_agent_example.py

@@ -1,12 +1,8 @@
 import os
 
-from dotenv import load_dotenv
-
 # Import the OpenAIChat model and the Agent struct
 from swarms import Agent, HuggingfaceLLM
 
-# Load the environment variables
-load_dotenv()
 
 # Get the API key from the environment
 api_key = os.environ.get("OPENAI_API_KEY")

playground/demos/xray/xray_example.py

@@ -1,7 +1,5 @@
 import os
 
-from dotenv import load_dotenv
-
 from swarms.models import GPT4VisionAPI, OpenAIChat
 from swarms.prompts.xray_swarm_prompt import (
     TREATMENT_PLAN_PROMPT,
@@ -9,8 +7,6 @@ from swarms.prompts.xray_swarm_prompt import (
 )
 from swarms.structs.agent import Agent
 
-# Load environment variables
-load_dotenv()
 openai_api_key = os.getenv("OPENAI_API_KEY")
 
 # Function to analyze an X-ray image

playground/examples/example_agent.py

@@ -1,14 +1,9 @@
 import os
 import sys
 
-from dotenv import load_dotenv
-
 # Import the OpenAIChat model and the Agent struct
 from swarms import OpenAIChat, Agent
 
-# Load the environment variables
-load_dotenv()
-
 # Get the API key from the environment
 api_key = os.environ.get("OPENAI_API_KEY")
 

playground/examples/example_concurrentworkflow.py

@@ -1,10 +1,6 @@
 import os
-from dotenv import load_dotenv
 from swarms import OpenAIChat, Task, ConcurrentWorkflow, Agent
 
-# Load environment variables from .env file
-load_dotenv()
-
 # Load environment variables
 llm = OpenAIChat(openai_api_key=os.getenv("OPENAI_API_KEY"))
 agent = Agent(llm=llm, max_loops=1)

playground/examples/example_logistics.py

@@ -1,6 +1,5 @@
 from swarms.structs import Agent
 import os
-from dotenv import load_dotenv
 from swarms.models import GPT4VisionAPI
 from swarms.prompts.logistics import (
     Health_Security_Agent_Prompt,
@@ -12,8 +11,6 @@ from swarms.prompts.logistics import (
     Efficiency_Agent_Prompt,
 )
 
-# Load ENV
-load_dotenv()
 api_key = os.getenv("OPENAI_API_KEY")
 
 # GPT4VisionAPI

playground/examples/example_recursiveworkflow.py

@@ -1,9 +1,6 @@
 import os
-from dotenv import load_dotenv
 from swarms import OpenAIChat, Task, RecursiveWorkflow, Agent
 
-# Load environment variables from .env file
-load_dotenv()
 
 # Load environment variables
 llm = OpenAIChat(openai_api_key=os.getenv("OPENAI_API_KEY"))

playground/examples/example_sequentialworkflow.py

@@ -1,8 +1,5 @@
 import os
 from swarms import OpenAIChat, Agent, SequentialWorkflow
-from dotenv import load_dotenv
-
-load_dotenv()
 
 # Load the environment variables
 api_key = os.getenv("OPENAI_API_KEY")

playground/examples/example_simple_conversation_agent.py

@@ -1,7 +1,5 @@
 import os
 
-from dotenv import load_dotenv
-
 from swarms import (
     OpenAIChat,
     Conversation,
@@ -11,9 +9,6 @@ conv = Conversation(
     time_enabled=True,
 )
 
-# Load the environment variables
-load_dotenv()
-
 # Get the API key from the environment
 api_key = os.environ.get("OPENAI_API_KEY")
 

playground/examples/example_swarmnetwork.py

@@ -1,13 +1,8 @@
 import os
 
-from dotenv import load_dotenv
-
 # Import the OpenAIChat model and the Agent struct
 from swarms import OpenAIChat, Agent, SwarmNetwork
 
-# Load the environment variables
-load_dotenv()
-
 # Get the API key from the environment
 api_key = os.environ.get("OPENAI_API_KEY")
 

playground/examples/example_task.py

@@ -1,12 +1,7 @@
 import os
 
-from dotenv import load_dotenv
-
 from swarms.structs import Agent, OpenAIChat, Task
 
-# Load the environment variables
-load_dotenv()
-
 
 # Define a function to be used as the action
 def my_action():

playground/examples/example_worker.py

@@ -1,11 +1,7 @@
 # Importing necessary modules
 import os
-from dotenv import load_dotenv
 from swarms import Worker, OpenAIChat, tool
 
-# Loading environment variables from .env file
-load_dotenv()
-
 # Retrieving the OpenAI API key from environment variables
 api_key = os.getenv("OPENAI_API_KEY")
 

playground/models/azure_openai_example.py

@@ -1,10 +1,6 @@
 import os
-from dotenv import load_dotenv
 from swarms import AzureOpenAI
 
-# Load the environment variables
-load_dotenv()
-
 # Create an instance of the AzureOpenAI class
 model = AzureOpenAI(
     azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),

playground/models/gemini_example.py

@@ -1,11 +1,7 @@
 import os
 
-from dotenv import load_dotenv
-
 from swarms.models.gemini import Gemini
 
-load_dotenv()
-
 api_key = os.environ["GEMINI_API_KEY"]
 
 # Initialize the model

playground/models/tts_speech_example.py

@@ -1,11 +1,7 @@
 import os
 
-from dotenv import load_dotenv
-
 from swarms import OpenAITTS
 
-load_dotenv()
-
 tts = OpenAITTS(
     model_name="tts-1-1106",
     voice="onyx",

playground/structs/agent_with_longterm_memory.py

@@ -1,13 +1,8 @@
 import os
 
-from dotenv import load_dotenv
-
 # Import the OpenAIChat model and the Agent struct
 from swarms import Agent, ChromaDB, OpenAIChat
 
-# Load the environment variables
-load_dotenv()
-
 # Get the API key from the environment
 api_key = os.environ.get("OPENAI_API_KEY")
 

playground/structs/agent_with_tools_example.py

@@ -10,16 +10,10 @@ tool decorated func [search_api] -> agent which parses the docs of the tool func
 
 import os
 
-from dotenv import load_dotenv
-
 # Import the OpenAIChat model and the Agent struct
 from swarms import Agent, OpenAIChat
 from swarms.tools.tool import tool
 
-# Load the environment variables
-load_dotenv()
-
-
 # Define a tool
 @tool
 def search_api(query: str, description: str):

playground/structs/autoscaler_example.py

@@ -1,15 +1,10 @@
 import os
 
-from dotenv import load_dotenv
-
 # Import the OpenAIChat model and the Agent struct
 from swarms.models import OpenAIChat
 from swarms.structs import Agent
 from swarms.structs.autoscaler import AutoScaler
 
-# Load the environment variables
-load_dotenv()
-
 # Get the API key from the environment
 api_key = os.environ.get("OPENAI_API_KEY")
 

playground/structs/company_example.py

@@ -1,12 +1,8 @@
 import os
 
-from dotenv import load_dotenv
-
 from swarms import Agent, OpenAIChat
 from swarms.structs.company import Company
 
-load_dotenv()
-
 llm = OpenAIChat(
     openai_api_key=os.getenv("OPENAI_API_KEY"), max_tokens=4000
 )

playground/structs/concurrent_workflow_example.py

@@ -1,12 +1,7 @@
 import os
 
-from dotenv import load_dotenv
-
 from swarms import Agent, ConcurrentWorkflow, OpenAIChat, Task
 
-# Load environment variables from .env file
-load_dotenv()
-
 # Load environment variables
 llm = OpenAIChat(openai_api_key=os.getenv("OPENAI_API_KEY"))
 agent = Agent(llm=llm, max_loops=1)

playground/structs/godmode_example.py

@@ -1,12 +1,8 @@
 import os
 
-from dotenv import load_dotenv
-
 from swarms import ModelParallelizer
 from swarms.models import Anthropic, Gemini, Mixtral, OpenAIChat
 
-load_dotenv()
-
 # API Keys
 anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
 openai_api_key = os.getenv("OPENAI_API_KEY")

playground/structs/hierarchical_swarm.py

@@ -1,11 +1,7 @@
 import os
 from swarms import OpenAIChat, Agent
-from dotenv import load_dotenv
 
 
-# Load environment variables
-load_dotenv()
-
 # Create a chat instance
 llm = OpenAIChat(
     api_key=os.getenv("OPENAI_API_KEY"),

playground/structs/kyle_hackathon.py

@@ -1,17 +1,11 @@
 import os
 
-from dotenv import load_dotenv
-
 from swarms import Agent, OpenAIChat
 from swarms.agents.multion_agent import MultiOnAgent
 from swarms.memory.chroma_db import ChromaDB
 from swarms.tools.tool import tool
 from swarms.utils.code_interpreter import SubprocessCodeInterpreter
 
-# Load the environment variables
-load_dotenv()
-
-
 # Memory
 chroma_db = ChromaDB()
 

playground/structs/multi_modal_rag_agent.py

@@ -1,16 +1,11 @@
 # Importing necessary modules
 import os
 
-from dotenv import load_dotenv
-
 from swarms import Agent, OpenAIChat
 from swarms.memory.chroma_db import ChromaDB
 from swarms.prompts.visual_cot import VISUAL_CHAIN_OF_THOUGHT
 from swarms.tools.tool import tool
 
-# Loading environment variables from .env file
-load_dotenv()
-
 # Getting the Gemini API key from environment variables
 gemini_api_key = os.getenv("GEMINI_API_KEY")
 openai_api_key = os.getenv("OPENAI_API_KEY")

playground/structs/multi_process_workflow.py

@@ -1,10 +1,6 @@
 import os
 from swarms import Gemini, Agent
 from swarms.structs.multi_process_workflow import MultiProcessWorkflow
-from dotenv import load_dotenv
-
-# Load the environment variables
-load_dotenv()
 
 # Gemini API key
 api_key = os.getenv("GEMINI_API_KEY")

playground/structs/recursive_example.py

@@ -1,12 +1,8 @@
 import os
 
-from dotenv import load_dotenv
 
 from swarms import Agent, OpenAIChat, RecursiveWorkflow, Task
 
-# Load environment variables from .env file
-load_dotenv()
-
 # Load environment variables
 llm = OpenAIChat(openai_api_key=os.getenv("OPENAI_API_KEY"))
 agent = Agent(llm=llm, max_loops=1)

playground/structs/task_example.py

@@ -1,12 +1,7 @@
 import os
 
-from dotenv import load_dotenv
-
 from swarms.structs import Agent, OpenAIChat, Task
 
-# Load the environment variables
-load_dotenv()
-
 
 # Define a function to be used as the action
 def my_action():

playground/tools/agent_with_tools_example.py

@@ -1,18 +1,12 @@
 import os
 
-from dotenv import load_dotenv
-
 from swarms import Agent, OpenAIChat
 from swarms.tools.tool import tool
 
-load_dotenv()
-
 api_key = os.environ.get("OPENAI_API_KEY")
 
-
 llm = OpenAIChat(api_key=api_key)
 
-
 @tool
 def search_api(query: str) -> str:
     """Search API

scripts/auto_tests_docs/auto_docs.py

@@ -3,8 +3,6 @@ import inspect
 import os
 import threading
 
-from dotenv import load_dotenv
-
 from scripts.auto_tests_docs.docs import DOCUMENTATION_WRITER_SOP
 from swarms import OpenAIChat
 from swarms.structs.majority_voting import MajorityVoting
@@ -12,12 +10,6 @@ from swarms.structs.stackoverflow_swarm import StackOverflowSwarm
 from swarms.structs.task_queue_base import TaskQueueBase
 from swarms.structs.tool_json_schema import JSON
 
-##########
-
-
-####################
-load_dotenv()
-
 api_key = os.getenv("OPENAI_API_KEY")
 
 model = OpenAIChat(

scripts/auto_tests_docs/auto_docs_functions.py

@@ -3,13 +3,9 @@ import os
 import sys
 import threading
 
-from dotenv import load_dotenv
-
 from scripts.auto_tests_docs.docs import DOCUMENTATION_WRITER_SOP
 from swarms import OpenAIChat
 
-load_dotenv()
-
 api_key = os.getenv("OPENAI_API_KEY")
 
 model = OpenAIChat(

scripts/auto_tests_docs/auto_docs_omni.py

@@ -2,18 +2,9 @@ import inspect
 import os
 import threading
 
-from dotenv import load_dotenv
-
 from scripts.auto_tests_docs.docs import DOCUMENTATION_WRITER_SOP
 from swarms import OpenAIChat
 
-###########
-
-
-###############
-
-load_dotenv()
-
 api_key = os.getenv("OPENAI_API_KEY")
 
 model = OpenAIChat(

scripts/auto_tests_docs/auto_tests.py

@@ -3,19 +3,13 @@ import os
 import re
 import threading
 
-########
-from dotenv import load_dotenv
-
 from scripts.auto_tests_docs.docs import TEST_WRITER_SOP_PROMPT
 from swarms import OpenAIChat
 
-#########
 from swarms.memory.dict_internal_memory import DictInternalMemory
 from swarms.memory.dict_shared_memory import DictSharedMemory
 from swarms.memory.lanchain_chroma import LangchainChromaVectorMemory
 
-load_dotenv()
-
 api_key = os.getenv("OPENAI_API_KEY")
 
 model = OpenAIChat(

scripts/auto_tests_docs/auto_tests_functions.py

@@ -3,14 +3,10 @@ import os
 import sys
 import threading
 
-from dotenv import load_dotenv
-
 from scripts.auto_tests_docs.docs import TEST_WRITER_SOP_PROMPT
 from swarms import OpenAIChat
 from swarms.utils.parse_code import extract_code_from_markdown
 
-load_dotenv()
-
 api_key = os.getenv("OPENAI_API_KEY")
 
 model = OpenAIChat(

scripts/auto_tests_docs/docs.py

@@ -148,7 +148,7 @@ def TEST_WRITER_SOP_PROMPT(
 
    8. **Environment Variables and Secret Handling**:
       - Store secrets and configurations in environment variables.
-      - Use libraries like `python-decouple` or `python-dotenv` to load environment variables.
+      - Use libraries like `python-decouple` to load environment variables.
       - For tests, mock or set environment variables temporarily within the test environment.
 
    9. **Grouping and Marking Tests**:

swarm_network_example.py

@@ -8,10 +8,6 @@ from swarms import (
     TogetherLLM,
 )
 from swarms.memory import ChromaDB
-from dotenv import load_dotenv
-
-# load the environment variables
-load_dotenv()
 
 # Initialize the ChromaDB
 memory = ChromaDB()

swarms/memory/chroma_db.py

@@ -5,16 +5,11 @@ from typing import Callable, List, Optional
 
 import chromadb
 import numpy as np
-from dotenv import load_dotenv
 
 from swarms.utils.data_to_text import data_to_text
 from swarms.utils.markdown_message import display_markdown_message
 from swarms.memory.base_vectordb import AbstractVectorDatabase
 
-# Load environment variables
-load_dotenv()
-
-
 # Results storage using local ChromaDB
 class ChromaDB(AbstractVectorDatabase):
     """

swarms/models/dalle3.py

@@ -10,14 +10,11 @@ import backoff
 import openai
 import requests
 from cachetools import TTLCache
-from dotenv import load_dotenv
 from openai import OpenAI
 from PIL import Image
 from pydantic import field_validator
 from termcolor import colored
 
-load_dotenv()
-
 # Configure Logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)

swarms/models/gemini.py

@@ -2,7 +2,6 @@ import os
 import subprocess as sp
 from pathlib import Path
 
-from dotenv import load_dotenv
 from PIL import Image
 
 from swarms.models.base_multimodal_model import BaseMultiModalModel
@@ -17,9 +16,6 @@ except ImportError as error:
     sp.run(["pip", "install", "--upgrade", "google-generativeai"])
 
 
-load_dotenv()
-
-
 # Helpers
 def get_gemini_api_key_env():
     """Get the Gemini API key from the environment

swarms/models/gpt4_vision_api.py

@@ -6,7 +6,6 @@ from typing import Optional
 
 import aiohttp
 import requests
-from dotenv import load_dotenv
 from termcolor import colored
 
 from swarms.models.base_multimodal_model import BaseMultiModalModel
@@ -21,7 +20,6 @@ except ImportError:
     raise ImportError
 
 # Load environment variables
-load_dotenv()
 openai_api_key = os.getenv("OPENAI_API_KEY")
 
 

swarms/models/openai_tts.py

@@ -3,7 +3,6 @@ import subprocess
 import sys
 
 import requests
-from dotenv import load_dotenv
 
 from swarms.models.base_llm import AbstractLLM
 
@@ -15,11 +14,6 @@ except ImportError as error:
         [sys.executable, "-m", "pip", "install", "pyaudio"]
     )
 
-
-# Load .env file
-load_dotenv()
-
-
 # OpenAI API Key env
 def openai_api_key_env():
     openai_api_key = os.getenv("OPENAI_API_KEY")

swarms/models/stable_diffusion.py

@@ -5,9 +5,6 @@ import uuid
 from typing import List
 
 import requests
-from dotenv import load_dotenv
-
-load_dotenv()
 
 stable_api_key = os.environ.get("STABLE_API_KEY")
 

swarms/models/together.py

@@ -3,14 +3,9 @@ import os
 from typing import Optional
 
 import requests
-from dotenv import load_dotenv
 
 from swarms.models.base_llm import AbstractLLM
 
-# Load environment variables
-load_dotenv()
-
-
 def together_api_key_env():
     """Get the API key from the environment."""
     return os.getenv("TOGETHER_API_KEY")

swarms/prompts/autoswarm.py

@@ -48,13 +48,11 @@ copy/paste to vscode and run it without issue. Here are some tips to consider:
 Output Format: A complete Python script that is ready for copy/paste to GitHub and demo execution. It should be formatted with complete logic, proper indentation, clear variable names, and comments.
 Here is an example of a a working swarm script that you can use as a rough template for the logic:
 import os
-from dotenv import load_dotenv
 from swarms.models import OpenAIChat
 from swarms.structs import Agent
 import swarms.prompts.swarm_daddy as sdsp
 
 # Load environment variables and initialize the OpenAI Chat model
-load_dotenv()
 api_key = os.getenv("OPENAI_API_KEY")
 llm = OpenAIChat(model_name = "gpt-4", openai_api_key=api_key)
 

swarms/prompts/programming.py

@@ -39,7 +39,7 @@ and thorough, use the guide below to create the tests, make the tests as thoroug
 
 8. **Environment Variables and Secret Handling**:
    - Store secrets and configurations in environment variables.
-   - Use libraries like `python-decouple` or `python-dotenv` to load environment variables.
+   - Use libraries like `python-decouple`to load environment variables.
    - For tests, mock or set environment variables temporarily within the test environment.
 
 9. **Grouping and Marking Tests**:

swarms/prompts/tests.py

@@ -42,7 +42,7 @@ def TEST_WRITER_SOP_PROMPT(
 
    8. **Environment Variables and Secret Handling**:
       - Store secrets and configurations in environment variables.
-      - Use libraries like `python-decouple` or `python-dotenv` to load environment variables.
+      - Use libraries like `python-decouple` to load environment variables.
       - For tests, mock or set environment variables temporarily within the test environment.
 
    9. **Grouping and Marking Tests**:

swarms/structs/autoscaler.py

@@ -66,13 +66,10 @@ class AutoScaler(BaseStructure):
 
     Examples:
     >>> import os
-    >>> from dotenv import load_dotenv
     >>> # Import the OpenAIChat model and the Agent struct
     >>> from swarms.models import OpenAIChat
     >>> from swarms.structs import Agent
     >>> from swarms.structs.autoscaler import AutoScaler
-    >>> # Load the environment variables
-    >>> load_dotenv()
     >>> # Get the API key from the environment
     >>> api_key = os.environ.get("OPENAI_API_KEY")
     >>> # Initialize the language model

swarms/telemetry/sentry_active.py

@@ -1,9 +1,6 @@
 import os
-from dotenv import load_dotenv
 import sentry_sdk
 
-load_dotenv()
-
 os.environ["USE_TELEMETRY"] = "True"
 
 use_telementry = os.getenv("USE_TELEMETRY")

tests/memory/test_pq_db.py

@@ -1,12 +1,8 @@
 import os
 from unittest.mock import patch
 
-from dotenv import load_dotenv
-
 from swarms.memory.pg import PostgresDB
 
-load_dotenv()
-
 PSG_CONNECTION_STRING = os.getenv("PSG_CONNECTION_STRING")
 
 

tests/models/test_cohere.py

@@ -2,12 +2,9 @@ import os
 from unittest.mock import Mock, patch
 
 import pytest
-from dotenv import load_dotenv
 
 from swarms.models.cohere_chat import BaseCohere, Cohere
 
-# Load the environment variables
-load_dotenv()
 api_key = os.getenv("COHERE_API_KEY")
 
 

tests/models/test_elevenlab.py

@@ -2,15 +2,12 @@ import os
 from unittest.mock import mock_open, patch
 
 import pytest
-from dotenv import load_dotenv
 
 from swarms.models.eleven_labs import (
     ElevenLabsModel,
     ElevenLabsText2SpeechTool,
 )
 
-load_dotenv()
-
 # Define some test data
 SAMPLE_TEXT = "Hello, this is a test."
 API_KEY = os.environ.get("ELEVEN_API_KEY")

tests/models/test_gpt4_vision_api.py

@@ -4,13 +4,10 @@ from unittest.mock import AsyncMock, Mock, mock_open, patch
 
 import pytest
 from aiohttp import ClientResponseError
-from dotenv import load_dotenv
 from requests.exceptions import RequestException
 
 from swarms.models.gpt4_vision_api import GPT4VisionAPI
 
-load_dotenv()
-
 custom_api_key = os.environ.get("OPENAI_API_KEY")
 img = "images/swarms.jpeg"
 

tests/structs/test_agent.py

@@ -4,14 +4,11 @@ from unittest import mock
 from unittest.mock import MagicMock, patch
 
 import pytest
-from dotenv import load_dotenv
 
 from swarms.models import OpenAIChat
 from swarms.structs.agent import Agent, stop_when_repeats
 from swarms.utils.logger import logger
 
-load_dotenv()
-
 openai_api_key = os.getenv("OPENAI_API_KEY")
 
 

tests/structs/test_autoscaler.py

@@ -2,14 +2,11 @@ import os
 from unittest.mock import MagicMock, patch
 
 import pytest
-from dotenv import load_dotenv
 
 from swarms.models import OpenAIChat
 from swarms.structs import Agent
 from swarms.structs.autoscaler import AutoScaler
 
-load_dotenv()
-
 api_key = os.environ.get("OPENAI_API_KEY")
 llm = OpenAIChat(
     temperature=0.5,

tests/structs/test_base_workflow.py

@@ -2,13 +2,10 @@ import json
 import os
 
 import pytest
-from dotenv import load_dotenv
 
 from swarms.models import OpenAIChat
 from swarms.structs import BaseWorkflow
 
-load_dotenv()
-
 api_key = os.environ.get("OPENAI_API_KEY")
 
 

tests/structs/test_task.py

@@ -3,7 +3,6 @@ from datetime import timedelta
 from unittest.mock import Mock
 
 import pytest
-from dotenv import load_dotenv
 
 from swarms.models.gpt4_vision_api import GPT4VisionAPI
 from swarms.prompts.multi_modal_autonomous_instruction_prompt import (
@@ -12,8 +11,6 @@ from swarms.prompts.multi_modal_autonomous_instruction_prompt import (
 from swarms.structs.agent import Agent
 from swarms.structs.task import Task
 
-load_dotenv()
-
 
 @pytest.fixture
 def llm():

tests/test_upload_tests_to_issues.py

@@ -2,9 +2,7 @@ import os
 import subprocess
 
 import requests
-from dotenv import load_dotenv
 
-load_dotenv
 
 # Constants
 GITHUB_USERNAME = os.getenv("GITHUB_USERNAME")

tool_agent_with_llm.py

@@ -1,14 +1,10 @@
 import os
 
-from dotenv import load_dotenv
 from pydantic import BaseModel, Field
 
 from swarms import OpenAIChat, ToolAgent
 from swarms.utils.json_utils import base_model_to_json
 
-# Load the environment variables
-load_dotenv()
-
 # Initialize the OpenAIChat class
 chat = OpenAIChat(
     api_key=os.getenv("OPENAI_API"),