Llama3-Groq-Streamlit Chat Application

This project showcases a chat application built with Streamlit that integrates Llama3 models via the Groq platform. It's designed to offer speedy real-time AI-driven chat functionalities, making the most of Llama3's capabilities within an interactive web interface.

demo.mp4

Features

• Llama3 Integration: Leverages the Llama3 and other major models, such as Mixtral and Gemma, to provide intelligent and context-aware responses.
• Groq Platform: Utilizes Groq's powerful computation capabilities to ensure fast and efficient model responses.
• Streamlit Interface: Offers a user-friendly web interface that allows users to interact with the AI dynamically.
• Real-Time Responses: Engineered to handle user inputs and deliver AI responses in real time.

Technologies Used

• Python 3.x
• Streamlit
• Groq API
• dotenv for environment management

Getting Started

Prerequisites

Ensure you have Python 3.6 or higher installed on your system. Streamlit and other required packages will be installed via the requirements file.

Installation

1. Clone the repository:

   git clone https://github.com/Op27/llama3-groq-streamlit.git

2. Navigate to the project directory:

   cd llama3-groq-streamlit

3. Install the required Python packages:

   pip install -r requirements.txt

Configuration

Obtaining a Groq API Key

To use this application, you'll need an API key from Groq. Visit the Groq API documentation to learn how to obtain one.

Setting Up Your Environment

Once you have your API key, you need to set it in your environment:

• Rename .env.example to .env.
• Open the .env file and replace YOUR_API_KEY_HERE with your Groq API key.

This step is crucial for the application to interact with Groq's services securely.

Running the Application

To run the application, use the following command: bash streamlit run main.py

License

This project is licensed under the MIT License - see the LICENSE file for details.