---
license: afl-3.0
title: Spaces Readme
sdk: gradio
emoji: 🚀
colorFrom: yellow
---

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference


# Lighting DavidNet over Spaces


### Gradio Integration for Interactive Model Predictions:

Develop a Gradio interface that envelops the model for seamless interactive predictions. This setup includes the following user-driven functionalities:

**GradCAM Visualizations**:
- Users can opt to visualize GradCAM images.
- Flexibility to choose the number of GradCAM images for display.
- Selection of the specific layer for generating GradCAM visualizations.
- Ability to adjust opacity of the overlaid GradCAM images.

**Misclassified Images**:
- Users have the choice to review misclassified images.
- Option to determine the quantity of misclassified images to be presented.

**Image Upload**:
- Users can upload their own images for predictions.
- A set of 10 example images is available for experimentation.

**Class Preferences**:
- Users can specify the number of top predicted classes they want to see.
- A limit of 10 classes ensures a manageable display.

### Deployment on Hugging Face Spaces:

The Gradio application, featuring the integrated model, is deployed on Hugging Face Spaces. The Spaces README encompasses the following elements:

- A comprehensive overview of the Spaces app's functionality.
- Exclusion of any training-related code from the README.
- Inclusion of links to the Lightning codebase on GitHub, ensuring a clear separation of model training specifics from deployment details.

### GitHub Repository for Lightning Training Code:

The Lightning training code resides in a distinct GitHub repository. The repository's detailed README incorporates:

- A comprehensive log detailing the training progression.
- Graphs illustrating the training epochs' loss function.
- Showcasing of 10 instances of misclassified images, complete with actual and predicted labels.

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