Weather Swarm - Pytorch
Project description
Baron Weather
Overview
Baron Weather is a sophisticated toolset designed to enable real-time querying of weather data using the Baron API. It utilizes a swarm of autonomous agents to handle concurrent data requests, optimizing for efficiency and accuracy in weather data retrieval and analysis.
Features
Baron Weather includes the following key features:
- Real-time Weather Data Access: Instantly fetch and analyze weather conditions using the Baron API.
- Autonomous Agents: A swarm system for handling multiple concurrent API queries efficiently.
- Data Visualization: Tools for visualizing complex meteorological data for easier interpretation.
Prerequisites
Before you begin, ensure you have met the following requirements:
- Python 3.10 or newer
- git installed on your machine
- Install packages like swarms
Installation
There are 2 methods, git cloning which allows you to modify the codebase or pip install for simple usage:
Pip
pip3 install -U weather-swarm
Cloning the Repository
To get started with Baron Weather, clone the repository to your local machine using:
git clone https://github.com/baronservices/weatherman_agent.git
cd weatherman_agent
Setting Up the Environment
Create a Python virtual environment to manage dependencies:
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
Installing Dependencies
Install the necessary Python packages via pip:
pip install -r requirements.txt
Usage
To start querying the Baron Weather API using the autonomous agents, run:
python main.py
API
python3 api.py
Llama3
from swarms import llama3Hosted
# Example usage
llama3 = llama3Hosted(
model="meta-llama/Meta-Llama-3-8B-Instruct",
temperature=0.8,
max_tokens=1000,
system_prompt="You are a helpful assistant.",
)
completion_generator = llama3.run(
"create an essay on how to bake chicken"
)
print(completion_generator)
Documentation
Contributing
Contributions to Baron Weather are welcome and appreciated. Here's how you can contribute:
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/YourAmazingFeature
) - Commit your Changes (
git commit -m 'Add some YourAmazingFeature'
) - Push to the Branch (
git push origin feature/YourAmazingFeature
) - Open a Pull Request
Tests
To run tests run the following:
pytest
Contact
Project Maintainer - Kye Gomez - GitHub Profile
Todo
- Implement the parser and the function calling mapping to execute the functions
- Then, implement the API server wrapping the hiearchical swarm
- Then, Deploy on the server 24/7
- Temperature and forecast of tomorrow
Requirements
- Simple
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for weather_swarm-0.0.7-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d7807f4bf06110e8007db3340c933b5ae7ff65669c58a6e2c33763051f061158 |
|
MD5 | cb136963c2dc2429d680d1e21fc2e0d9 |
|
BLAKE2b-256 | 46db8f74ef7edca88a87782a65e840571b1ce54d495f85c6d3229ebb447d052f |