Llama-Agent Spam Classifier

Overview

This project utilizes CrewAI to classify incoming emails and generate appropriate responses based on their importance. It employs a two-agent system powered by the Llama-3.3-70B-Versatile model via Groq API.

Features

• Email Classification: Assigns emails one of three categories: Important, Casual, or Spam.
• Automated Responses: Generates appropriate responses based on classification.
  • Important Emails: Formal response.
  • Casual Emails: Informal response.
  • Spam Emails: Ignored.
• Multi-Agent Collaboration: Uses CrewAI framework to coordinate tasks between agents.

Installation

Prerequisites

Ensure you have the following installed:

• Python 3.8+
• pip

Setup

1. Clone the repository:

   git clone https://github.com/RuvenGuna94/Llama-agent-spam-classifier.git
   cd Llama-agent-spam-classifier

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate   # On Windows use: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Set up environment variables:
   • Create a .env file in the root directory.
   • Add the following line to it:

     GROQ_API_KEY=your_api_key_here
     

Usage

1. Run the script:

   python main.py

2. The agents will classify an email and generate a response accordingly.

Code Structure

• email_agent.py: Core script defining agents and their tasks.
• .env: Stores environment variables.
• requirements.txt: Lists dependencies.

Example Output

Agent: Email Classifier
Classification: Casual

Agent: Email Responder
Response: "Noted! Thanks for the update."

Technologies Used

• CrewAI: Agent-based workflow framework
• Llama 3: Large language model from Groq
• Python: Primary programming language

Future Improvements

• Extend classification to include more categories.
• Integrate with an email API (e.g., Gmail API) for real-time processing.
• Store classified emails in a database.

License

This project is licensed under the MIT License.

Author

RuvenGuna94