This project is developed using RShiny, which supports interaction through multiple forms of input, including text, word & ppt. The chatbot leverages artificial intelligence to provide dynamic responses based on the input received.
- Text Input: Users can ask questions or provide commands via text, and the chatbot will generate intelligent responses.
- File Recognition: By uploading images, the chatbot can analyze and describe the content using AI-driven image recognition techniques.
- RShiny Integration: The chatbot is built using RShiny, which provides a robust, interactive user interface for seamless interactions in a web application environment.
- AI Type: Users can change the AI model type like conversation, coding etc. based on their need.
- Multiple Models: Multiple AI models available which make user more comfortable and let them decide which they want to use.
This project demonstrates how AI models can be integrated with RShiny to create an engaging, multimodal experience, suitable for various applications such as customer service, education, and virtual assistance.
To run these examples, you'll need:
- R (version 3.5 or above recommended)
- Shiny package installed
- Intermediate knowledge of R programming
- Familiarity with LLM
The code divied into multiple parts. Here's a quick overview of the files -
- ui.R : Contains the UI section of the app
- server.R : Contains all the calculations
- global.R : contiains the app environment details
- helper.R : All the LLM related functions defined here
We welcome contributions! If you have additional sample codes or improvements, please:
- Fork this repository.
- Create a feature branch:
git checkout -b feature/your-feature-name- Commit your changes and push the branch:
git push origin feature/your-feature-name- Open a Pull Request.
Make sure your code follows the repository's style and is well-documented.