Introducing GRAS, the Grading Review Automation System—an AI-powered tool designed to help students refine their essays with ease. By comparing your writing to a rubric you provide, GRAS identifies key areas for improvement and offers targeted suggestions, ensuring you’re always on the path to better grades. With a unique, nature-themed user interface, the process feels intuitive and refreshing. Whether you're looking to fine-tune your arguments or enhance your writing style, GRAS makes the journey to academic excellence smoother than ever.

GRAS (Grading Review Automation System) is an AI-powered tool designed to assist students in refining their essays by comparing them against custom, user-defined rubrics. By offering detailed, personalized feedback on key areas for improvement, GRAS helps students enhance their writing, all within a calm, nature-themed user interface.

• AI-Powered Feedback: Uses machine learning to analyze essays and provide personalized feedback.
• Custom Rubrics: Allows users to input custom rubrics for assignment-specific feedback.
• Areas of Improvement: Identifies specific areas for enhancement, such as essay structure, argument clarity, or content depth.
• Actionable Suggestions: Offers clear, actionable recommendations for improving the quality of writing.
• Nature-Themed UI: Provides a refreshing, intuitive user interface inspired by nature to enhance the user experience.

1. Input Your Essay: Upload or paste your essay into the GRAS platform.
2. Provide a Rubric: Input the rubric you will be graded against to receive tailored feedback.
3. Receive Feedback: GRAS analyzes your essay and highlights areas that need improvement based on the rubric.
4. Refine and Improve: Use the actionable feedback to adjust your essay. You can resubmit it for further refinement.

To install and use GRAS on your local machine, follow these steps:

1. Clone the repository:

   git clone https://github.com/CoderLogy/GRAS.git

2. Navigate to the project directory:

   cd GRAS

3. Install the necessary dependencies:

   pip install -r requirements.txt

4. Run the application:

   python app.py

Once the application is running, you can upload or paste your essay into the provided interface. Add your rubric, and GRAS will instantly analyze the text, offering personalized feedback based on the rubric. Use this feedback to enhance your essay and resubmit as needed.

• Python for backend processing and AI-powered feedback.
• Machine Learning to provide intelligent, customized feedback.
• Flask (depending on your framework choice) to handle the web-based interface.
• Natural Language Processing (NLP) for text analysis.
• Frontend built with HTML, CSS, and JavaScript, with a nature-themed design.

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-branch

3. Make your changes and commit them:

   git commit -m "Add new feature"

4. Push to the branch:

   git push origin feature-branch

5. Open a Pull Request and describe the changes.

This project is licensed under the MIT License. See the LICENSE file for more details.