---
title: Eden Multimodal
emoji: 🏆
colorFrom: blue
colorTo: gray
sdk: gradio
sdk_version: 5.1.0
app_file: app.py
pinned: true
license: mit
---

# Eden Multimodal 
[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces) [![Gradio](https://img.shields.io/badge/Gradio-5.1.0-orange)](https://gradio.app/)

## Overview

Eden Multimodal is an innovative project that leverages the power of multimodal AI to create a unique and interactive experience. It processes and analyzes text, image, and audio inputs to provide comprehensive insights. This application is hosted on **Hugging Face Spaces** and utilizes the **Gradio** framework for its user interface.

## Features

- **Multimodal AI Processing**: Simultaneous handling of text, image, and audio data.
- **Interactive Interface**: User-friendly interface powered by Gradio.
- **Real-time Analysis**: Provides instant feedback and results.
- **Scalable and Extensible**: Modular code structure for easy expansion.

## Technical Details

### Project Structure

The project is organized as follows:
eden-multimodal /
  - app.py
  - models /
    - text_model.py
    - image_model.py
    - audio_model.py
  - utils /
    - preprocessor.py
    - postprocessor.py
  - requirements.txt
  - README.md

### Code Breakdown

**Key Components:**

- **Model Initialization**: Loads and prepares the text, image, and audio models located in the `models/` folder. These models are responsible for processing their respective data types.

- **Preprocessing Functions**: Contains functions from `utils/preprocessor.py` that clean and format user inputs before they are fed into the models. This ensures compatibility and improves model performance.

- **Main Processing Functions**: Defines functions that handle the core logic of the application. These functions take preprocessed inputs, pass them through the appropriate models, and generate outputs.

- **Postprocessing Functions**: Utilizes functions from `utils/postprocessor.py` to refine and format the model outputs, making them suitable for display in the user interface.

- **Gradio Interface Setup**: Configures the Gradio interface components, specifying input and output types for text, images, and audio. It also designs the layout and appearance of the web application.

- **User Interaction Handlers**: Implements callbacks and event handlers that respond to user inputs in real-time, ensuring a seamless interactive experience.

- **Application Launch Code**: Contains the `if __name__ == "__main__":` block that launches the Gradio app, allowing users to access the application via a web browser.

**Role of Key Modules:**

- **Projection Layer**: Although not explicitly named in `app.py`, if a projection layer is used within the models, it serves as a dimensionality reduction step, transforming high-dimensional data into a lower-dimensional space while preserving essential features. This is crucial for improving computational efficiency and focusing on the most relevant data aspects.

- **Integration with Models**: `app.py` acts as the orchestrator, integrating text, image, and audio models into a cohesive system. It ensures that each model receives the correct input and that their outputs are combined or presented appropriately.

- **Scalability Considerations**: The modular structure in `app.py` allows for easy addition of new modalities or models. By abstracting functionalities into separate functions and leveraging modules from `models/` and `utils/`, the code remains clean and maintainable.

**Summary of Functioning:**

- **Input Reception**: Accepts user inputs in the form of text, images, or audio through the Gradio interface.

- **Data Processing Pipeline**:
  1. **Preprocessing**: Cleans and prepares inputs.
  2. **Model Prediction**: Processes inputs using the appropriate modality-specific model.
  3. **Postprocessing**: Formats and refines the outputs.

- **Output Presentation**: Displays the results back to the user in an intuitive and informative manner.

Overall, `app.py` is the central hub of the Eden Multimodal application, managing the flow of data from user input to model processing and finally to output presentation.

## Installation and Usage

To run this project locally:

1. **Clone the repository:**
   ```bash
   git clone https://github.com/yourusername/eden-multimodal.git
   cd eden-multimodal
   ```

2. **Install the required dependencies:**
   ```bash
   pip install -r requirements.txt
   ```

3. **Run the application:**
   ```bash
   python app.py
   ```

4. **Access the application:**
   Open your web browser and navigate to `http://localhost:7860` to interact with the application.

## Deployment

This project is designed to be deployed on **Hugging Face Spaces**. The configuration specified in the YAML front matter of the `README.md` is used by Hugging Face to set up the environment and run the application.

**Steps to Deploy:**

1. **Push the repository to GitHub** (or another Git hosting service).
2. **Create a new Space on Hugging Face Spaces** and select Gradio as the SDK.
3. **Link your repository** to the new Space.
4. The application will automatically build and deploy using the provided configuration.

## Contributing

Contributions to Eden Multimodal are welcome! Please follow these steps:

1. **Fork the repository** to your own GitHub account.
2. **Create a new branch** for your feature or bug fix:
   ```bash
   git checkout -b feature/your-feature-name
   ```
3. **Commit your changes** with clear messages:
   ```bash
   git commit -m "Add feature X"
   ```
4. **Push to your branch:**
   ```bash
   git push origin feature/your-feature-name
   ```
5. **Create a Pull Request** on the main repository.

## License

[Specify your license here, e.g., MIT, GPL, etc.]

---

For more information on configuring Hugging Face Spaces, please refer to the [official documentation](https://huggingface.co/docs/hub/spaces-config-reference).