Update README.md

README.md

@@ -1,93 +1,112 @@
-NailVirtuoso: AI-Powered Virtual Nail Try-On: 
-NailVirtuoso is an AI-powered web application that allows users to virtually try on different nail colors. By uploading an image of their hand, a U-Net deep learning model performs precise nail segmentation, and the user can select any color to see it applied in real-time. This project was developed for the TensorForge '25 AI Buildathon.
-
-## Features
-AI-Powered Nail Segmentation: Utilizes a U-Net architecture for accurate nail detection.
-
-Virtual Color Try-On: Apply any selected color to the segmented nails on your uploaded photo.
-
-Web-Based Interface: Easy-to-use application accessible from any web browser.
-
-Containerized Deployment: Packaged with Docker for easy and reproducible setup.
-
-## Local Setup Instructions
-Follow these steps to set up and run the project on your local machine.
-
-### Prerequisites
-Python 3.9 or later
-
-pip for package management
-
-A virtual environment tool (like venv)
-
-### Installation
-Clone the repository:
-
-Bash
-
-git clone https://github.com/your-username/nailvirtuoso.git
-cd nailvirtuoso
-Create and activate a virtual environment:
-
-Bash
-
-# For Windows
-python -m venv venv
-.\venv\Scripts\activate
-
-# For macOS/Linux
-python3 -m venv venv
-source venv/bin/activate
-Install the required dependencies:
-
-Bash
-
-pip install -r requirements.txt
-Download the pre-trained model:
-Ensure your trained model file (nail_segmentation_model.pth) is located in the model/ directory.
-
-### Running the Application
-Start the Flask server:
-
-Bash
-
-python run.py
-Access the application:
-Open your web browser and navigate to http://127.0.0.1:5000.
-
-## Docker Deployment
-The easiest way to run this project is by using Docker.
-
-### Prerequisites
-Docker Desktop installed and running.
-
-### Quickstart with Docker
-Build the Docker image:
-From the project's root directory, run the following command:
-
-Bash
-
-docker build -t nail-virtuoso .
-Run the Docker container:
-This command will start the application and make it accessible on port 5001 of your local machine.
-
-Bash
-
-docker run -p 5001:5000 nail-virtuoso
-Access the application:
-Open your web browser and navigate to http://localhost:5001.
-
-## Project Structure
-.
-├── model/
-│   └── nail_segmentation_model.pth
-├── notebooks/
-│   └── data_exploration.ipynb
-├── scripts/
-│   └── Model Train Stage (it contain code bases and some samll amount of data I use to train models)
-|   └── Running_Stage (here is the working code bases)
-├── Dockerfile
-├── README.md
-├── requirements.txt
-└── run.py
+---
+title: NailVirtuoso Virtual Try On
+emoji: 💅
+colorFrom: pink
+colorTo: purple
+sdk: docker
+pinned: false
+app_port: 7860
+license: mit
+language:
+- en
+pipeline_tag: image-segmentation
+tags:
+- nails
+- fashion
+- virtual-try-on
+- unet
+---
+
+NailVirtuoso: AI-Powered Virtual Nail Try-On: 
+NailVirtuoso is an AI-powered web application that allows users to virtually try on different nail colors. By uploading an image of their hand, a U-Net deep learning model performs precise nail segmentation, and the user can select any color to see it applied in real-time. This project was developed for the TensorForge '25 AI Buildathon.
+
+## Features
+AI-Powered Nail Segmentation: Utilizes a U-Net architecture for accurate nail detection.
+
+Virtual Color Try-On: Apply any selected color to the segmented nails on your uploaded photo.
+
+Web-Based Interface: Easy-to-use application accessible from any web browser.
+
+Containerized Deployment: Packaged with Docker for easy and reproducible setup.
+
+## Local Setup Instructions
+Follow these steps to set up and run the project on your local machine.
+
+### Prerequisites
+Python 3.9 or later
+
+pip for package management
+
+A virtual environment tool (like venv)
+
+### Installation
+Clone the repository:
+
+Bash
+
+git clone https://github.com/your-username/nailvirtuoso.git
+cd nailvirtuoso
+Create and activate a virtual environment:
+
+Bash
+
+# For Windows
+python -m venv venv
+.\venv\Scripts\activate
+
+# For macOS/Linux
+python3 -m venv venv
+source venv/bin/activate
+Install the required dependencies:
+
+Bash
+
+pip install -r requirements.txt
+Download the pre-trained model:
+Ensure your trained model file (nail_segmentation_model.pth) is located in the model/ directory.
+
+### Running the Application
+Start the Flask server:
+
+Bash
+
+python run.py
+Access the application:
+Open your web browser and navigate to http://127.0.0.1:5000.
+
+## Docker Deployment
+The easiest way to run this project is by using Docker.
+
+### Prerequisites
+Docker Desktop installed and running.
+
+### Quickstart with Docker
+Build the Docker image:
+From the project's root directory, run the following command:
+
+Bash
+
+docker build -t nail-virtuoso .
+Run the Docker container:
+This command will start the application and make it accessible on port 5001 of your local machine.
+
+Bash
+
+docker run -p 5001:5000 nail-virtuoso
+Access the application:
+Open your web browser and navigate to http://localhost:5001.
+
+## Project Structure
+.
+├── model/
+│   └── nail_segmentation_model.pth
+├── notebooks/
+│   └── data_exploration.ipynb
+├── scripts/
+│   └── Model Train Stage (it contain code bases and some samll amount of data I use to train models)
+|   └── Running_Stage (here is the working code bases)
+├── Dockerfile
+├── README.md
+├── requirements.txt
+└── run.py
 └── Demo Video.mp4