Student Assessment Feedback System (Prototype for Brockport ACM Project)

Description

This project is a Student Assessment Feedback System that uses AI to provide personalized feedback on student answers. It leverages OpenAI's GPT model and ChromaDB for efficient content storage and retrieval.

Features

• Embeds and stores module content using ChromaDB
• Loads questions and answers from a JSON file
• Collects student answers through a Streamlit interface
• Generates AI-powered feedback based on student responses and relevant content

Installation

1. Clone the repository:

   git clone https://github.com/your-username/student-assessment-feedback-system.git
   cd student-assessment-feedback-system
   

2. Install the ODBC Driver: If you're on macOS, you need to install the ODBC driver to connect to Azure SQL Database. Run the following commands in your terminal:

   brew info unixodbc
   brew tap microsoft/mssql-release https://github.com/Microsoft/homebrew-mssql-release
   brew update
   brew install msodbcsql18
   

3. Create a virtual environment and activate it:

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

4. Install the required dependencies:

   pip install -r requirements.txt
   

5. Set up your environment variables: Create a .env file in the project root and add your OpenAI API key:

   OPENAI_API_KEY=your_api_key_here
   AWS_USERNAME=your_db_username
   AWS_PASSWORD=your_db_password
   DATABASE_HOST_AWS=aws_host_url
   DB_PORT=5432  # Default PostgreSQL port
   DB_NAME=your_db_name
   

Usage

1. Prepare your module content: Place your PDF file in the appropriate directory and update the module_content_fp variable in the script.

2. Prepare your questions and answers: Create a JSON file with questions and answers, and update the questions_fp variable in the script.

3. Run the Streamlit app:

   streamlit run app.py
   

4. Open the provided URL in your web browser to access the Student Assessment Feedback System.