LLM_Class 📎

Welcome to LLM_Class 🌐, Crafted with python and Hugging Face 🐍, by DefinetlyNotAI 🤗. This comprehensive guide is here to equip you with everything you need to use LLM_Class effectively.

LLM_Class

The LLM_Class is a Python class designed to provide financial market insights through various functionalities including retrieving stock news, fetching historical stock data, generating text based on prompts, and analyzing the sentiment of given texts as well as act like a basic language model. It leverages external libraries such as requests, BeautifulSoup, yfinance, and transformers from Hugging Face to perform these tasks efficiently.

Prerequisites

Before you begin, ensure you have met the following requirements:

• You have installed Python 3.8 or later.
• You have installed the required Python packages from the requirements.ps1 file.
• You are using windows
• You have at least 6GB of Storage available
• You have a dedicated nvidea GPU supporting CUDA 11.8

Installation

If you plan to modify or extend the functionality of the LLM_Class, clone the repository and install the required packages locally.

git clone https://github.com/DefinetlyNotAI/LLM_Class.git
cd LLM_Class
.\requirements.ps1

Usage

Initialization

First, import the LLM_Class class and create an instance of it:

from LLM import LLM

llm = LLM()

Getting Stock News

To retrieve the latest news headlines for a specific stock ticker, use the get_stock_news method:

news_headlines = llm.get_stock_news('AAPL')
print("News Headlines:", news_headlines)

This is the most unsupported feature of the LLM_Class.

Fetching Historical Stock Data

To fetch historical stock data for a given ticker symbol over the past year, use the get_stock_data method:

stock_data = llm.get_stock_data('AAPL')
print(stock_data)

Generating Text

To generate text based on a given prompt using a customizable text generation pipeline, use the generate_text method:

generated_text = llm.generate_text(prompt="What is the future of AI in finance?")
print(generated_text)

Analyzing Sentiment

To analyze the sentiment of a given text, use the analyze_sentiment method:

sentiment = llm.analyze_sentiment(text="The stock market is volatile today.")
print(sentiment)

Contributing

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

But don't hesitate to open an Issue if you have any questions or encounter any issues.

Most importantly, don't forget to follow us! and read our Contributing Guide.

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Shahm Najeeb - Nirt_12023@outlook.com

Project Link: https://github.com/DefinetlyNotAI/LLM_Class