CrewAI Machine Learning Assistant

Overview

The CrewAI Machine Learning Assistant is a Streamlit application designed to kickstart your machine learning projects. It leverages a team of AI agents to guide you through the initial steps of defining, assessing, and solving machine learning problems.

Features

• Problem Definition: Clarify the machine learning problem you want to solve, identifying the type of problem (e.g., classification, regression) and any specific requirements.

• Data Assessment: Evaluate the data provided by the user, assessing its quality, suitability for the problem, and suggesting preprocessing steps if necessary.

• Model Recommendation: Suggest the most suitable machine learning models based on the problem definition and data assessment, providing reasons for each recommendation.

• Starter Code Generation: Generate starter Python code for the project, including data loading, model definition, and a basic training loop, based on findings from the problem definitions, data assessment, and model recommendation.

Usage

Setup

1. Create a virtual environment (recommended)

   python -m venv crewai_venv

2. Activate the virtual environment

   On Linux/macOS:

   source crewai_venv/bin/activate

   On Windows:

   crewai_venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Configure your Groq API key

   Create a .streamlit/secrets.toml file in the project directory:

   mkdir -p .streamlit

   Add your Groq API key to .streamlit/secrets.toml:

   GROQ_API_KEY = "your-groq-api-key-here"

   Get your API key from https://console.groq.com/keys

5. Run the Streamlit app

   streamlit run app.py

   The app will open in your browser at http://localhost:8501

Using the App

1. Use the sidebar to customize the model selection.

2. Describe your machine learning problem in the provided text input.

3. Optionally, upload a sample .csv of your data.

4. The app will provide a clear problem definition, data assessment, model recommendations, and starter Python code.