Medical Image Gen

An AI-powered web application that generates realistic images of skin rashes from textual descriptions, empowering healthcare professionals to enhance patient education and communication. Integrated Streamlit for the intuitive user interface design.

UI

Tech Stack

• Frontend:
  • Streamlit - A framework for creating interactive web applications.
• Backend:
  • PyTorch - An open-source machine learning library used for model training and inference.
  • Diffusers - A library for diffusion models, used for generating images.
• Models:
  • Latent Diffusion Model - A generative model for producing high-quality images.
  • CLIP - A model for connecting textual descriptions with images, used for fine-tuning.
• Cloud Services:
  • Google Colab - An online platform for running Python code in the cloud.

Features

• Interactive Sidebar:
  • Image Height: Adjust the height of the generated image.
  • Image Width: Adjust the width of the generated image.
  • Random Seed: Set a random seed for reproducibility of the generated images.
  • Inference Steps: Control the number of steps for the image generation process to balance quality and computation time.
• Realistic Image Generation: Utilizes a latent diffusion model to generate high-quality images based on textual descriptions.
• Downloadable Results: Option to download the generated image in JPEG format.
• Custom Loader: An animated loader is displayed while the image is being generated, providing a better user experience.

Getting Started

Prerequisites

• Python 3.7 or higher
• Required Python packages (see requirements.txt)

To run the Streamlit app in Google Colab, follow these steps:

1. Open a New Colab Notebook

   Go to Google Colab and create a new notebook.

2. Run all cells to run the streamlit app

3. You will see a URL printed in the last cell output. Click on this URL to open your Streamlit app in a new tab.