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LanguagePal Setup Guide

This guide will walk you through setting up LanguagePal on your local machine.

Prerequisites

  1. Python 3.11 or higher
  2. A Google Gemini API key (Pro 1.0 or Flash 2.0)
  3. Git (optional, for cloning the repository)

Installation Steps

1. Clone or Download the Repository

git clone https://github.com/BroskiJ/LanguageLearningChatbot.git
cd LanguageLearningChatbot

Alternatively, you can download the code as a ZIP file from GitHub and extract it.

2. Create a Virtual Environment (Recommended)

# Create a virtual environment
python -m venv venv

# Activate the virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

3. Install Required Packages

# Install from the packages.txt file
pip install -r packages.txt

4. Set Environment Variables

You'll need to set your Google Gemini API key as an environment variable:

On Windows:

set GEMINI_API_KEY=your_api_key_here
set SECRET_KEY=your_secret_key_here

On macOS/Linux:

export GEMINI_API_KEY=your_api_key_here
export SECRET_KEY=your_secret_key_here

For persistent settings:

Add these to your environment variables configuration or your shell's profile file.

5. Run the Application

python main.py

The application will be available at http://localhost:5000

Configuration Options

Database Configuration

By default, LanguagePal uses session storage for simplicity. If you want to use a database:

  1. Set the DATABASE_URL environment variable to your database connection string
  2. Uncomment and modify the database configuration in app.py as needed

Custom Port

To run on a different port:

python main.py --port 8080

Or modify the port in the main.py file.

Troubleshooting

API Key Issues

If you see errors related to the Gemini API key:

  • Verify that your API key is correct
  • Check that the environment variable is set correctly
  • Ensure your API key has the necessary permissions

Package Installation Problems

If you encounter package installation issues:

  • Make sure you're using Python 3.11+
  • Try installing packages one by one
  • Check for any system-specific requirements for packages like psycopg2-binary

Deployment

For deployment to production, we recommend:

  • Using gunicorn as a WSGI server
  • Setting up HTTPS with a reverse proxy like Nginx
  • Using environment variables for all sensitive information
  • Setting DEBUG=False in production

Example gunicorn command:

gunicorn --bind 0.0.0.0:5000 main:app