# Quiz Builder

This project implements a Quiz Builder using various technologies such as Google Gemini, Vertex AI API, embeddings, Google Service Account, Langchain, PDF loader, and Streamlit. The project is part of the challenges provided by Radical AI, where contributions have been made to implement certain steps.

## Description

The Quiz Builder is designed to generate quizzes based on input documents and topics provided by the user. It utilizes machine learning models for text embeddings and leverages Google's Gemini and Vertex AI API for document processing and quiz generation. The project also incorporates Streamlit for the user interface to make it interactive and easy to use.

## Tasks

By implemenging the script files, the following tasks will be covered:

1. Document processing using Google Gemini.
2. Text embeddings with Langchain.
3. Integration of Google Service Account for authentication.
4. PDF loader to ingest documents.
5. Building a user interface with Streamlit.
6. Generating quizzes based on user input topics.
7. Providing explanations for quiz answers.
8. Navigation controls for the quiz interface.
9. Error handling and validation.
10. Packaging and deployment considerations.


## Usage

To run the Quiz Builder:

1. Install the necessary dependencies listed in `requirements.txt`.
2. Run the Streamlit application by executing `streamlit run xxx.py` in the terminal where xxx is the name of the py file you want to run.
3. Follow the instructions provided in the user interface to interact with the Quiz Builder.

## License

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

## Acknowledgments

This project is based on [mission-quizify](https://github.com/radicalxdev/mission-quizify), developed by [radicalxdev]. We thank them for providing the foundation for this project.

- [Radical AI](https://www.radicalai.org/) - For providing the challenges and inspiration for this project.
- [Streamlit](https://streamlit.io/) - For the user-friendly interface development.
- [Google Cloud Platform](https://cloud.google.com/) - For the various APIs and services used in this project.
- [Langchain](https://langchain.com/) - For providing text embeddings technology.