DEMO VIDEO: https://www.youtube.com/watch?v=dCS11zQHInw

TokenMyzer is a Streamlit-based application designed to optimize text input for AI language models. It provides text preprocessing options to reduce token usage and improve efficiency in AI interactions.

The TokenMyzer() function is available as a standalone Python function that can be easily integrated into existing AI applications.

1. Download the TokenMyzer.py file from the Streamlit app's sidebar.
2. Place the file in your project directory.
3. Import and use the function in your Python script:

from TokenMyzer import TokenMyzer

original_text = "This is a sample text that needs processing."
processed_text = TokenMyzer(original_text, clean=False, Tarzan=False, concise=False, all=True)
print(processed_text)

text (str): The input text to process. clean (bool): Apply text cleaning (remove whitespace, punctuation, and convert to lowercase). Tarzan (bool): Remove common words (articles, prepositions, etc.). concise (bool): Append "Be concise." to the end of the text. all (bool): Apply all processing options.

1. Clone the repository: Copygit clone https://github.com/yourusername/TokenMyzer.git cd TokenMyzer

2. Create a virtual environment: Copypython -m venv venv source venv/bin/activate # On Windows use venv\Scripts\activate

3. Install the required packages: Copypip install -r requirements.txt

4. Set up your Groq API key:

5. Create a .env file in the root directory Add your Groq API key: GROQ_API_KEY=your_api_key_here

The app requires a Groq API key to function. You can provide this key in one of three ways:

1. Environment Variable: Set the GROQ_API_KEY environment variable on your system or in a .env file in the project root.

2. Streamlit Cloud Secrets: When deploying to Streamlit Cloud, set the GROQ_API_KEY in the app's secrets management as described in the "Streamlit Cloud Deployment" section.

3. Manual Input: If no API key is found, the app will prompt you to enter it manually. This key will be saved for the current session and optionally stored in a .env file for future use.

Note: When entering the API key manually, ensure you're using a secure, private browsing session if on a shared computer.

• Copystreamlit run app.py

• Open your web browser and go to http://localhost:8501.

Select a model from the dropdown menu. Enter your text in the input area. Choose preprocessing options:

Tarzanifier: Removes common words. Clean-Up: Cleans and standardizes text. Be Concise: Appends "Be concise." to the request.

Click "Submit" to process your text and send it to the API. View the token usage information in the sidebar. Download the standalone TokenMyzer function using the button in the sidebar.

TokenMyzer/
├── .gitignore
├── requirements.txt
├── README.md
├── app.py
├── config/
│ └── config.py
├── src/
│ ├── api/
│ │ └── groq_api.py
│ ├── ui/
│ │ └── streamlit_ui.py
│ ├── utils/
│ │ ├── common_words.txt
│ │ ├── helpers.py
│ │ └── text_processors.py
│ └── downloadable/
│ └── tokenmyzer_function.py
└── tests/
└── test_groq_api.py

###Contributing Contributions to TokenMyzer are welcome! Please feel free to submit a Pull Request.

##License This project is licensed under the MIT License - see the LICENSE file for details. ©2024 J. Gravelle : [email protected] : https://j.gravelle.us This README provides a comprehensive guide for both the standalone TokenMyzer function and the full Streamlit application. It includes the updated directory structure, setup instructions, usage guidelines, and information about contributing to the project. You may want to add or modify sections based on specific details of your project, such as adding a "Features" section or expanding on the "Contributing" guidelines.