Update README.md

README.md

@@ -4,36 +4,63 @@ Utilizing Deep Learning for Object Recognition and Tracking in Aerial Images/Vid
 
 ## Get started with Flask API
 
-To run this application in a Docker container, follow these steps:
-
 1. Clone the repository to your local machine:
 
-   `git clone http://github.com/YassineOuhadi/World-Cup-Express-App.git`
+   `git clone https://github.com/YassineOuhadi/Real-Time-Object-Detection.git`
 
-1. Clone the repository to your local machine:
+1. Navigate to the root directory of the application:
 
-   ` python -m venv venv`
+   `cd flask-api`
 
-1. Clone the repository to your local machine:
+1. Install required packages:
 
    `pip install -U pip virtualenv`
+   `pip install flask`
+   `pip install ultralytics`
+   `pip install opencv-python`
 
-1. Clone the repository to your local machine:
+1. Create a virtual environment:
+
+   `python -m venv venv`
+
+1. Activate the virtual environment:
 
    `source venv/bin/activate`
 
-1. Clone the repository to your local machine:
+1. Run the Flask application:
 
-   `python -m pip install flask`
+   `python -m flask --app ./app.py run`
 
 1. Clone the repository to your local machine:
 
-   `pip install opencv-python
+   `pip install ultralytics
 
 1. Clone the repository to your local machine:
 
    `pip install opencv-python`
+
 1. Clone the repository to your local machine:
 
    `python -m flask --app ./app.py run`
 
+## Get started with React Interface
+
+To run this application in `http://localhost:3000/`, follow these steps:
+
+1. Navigate to the root directory of the application:
+
+   `cd react-app`
+
+1. Install dependencies:
+
+   `npm install`
+
+1. Install client dependencies:
+
+   `cd client`
+   `npm install`
+
+1. Run the application:
+
+   `cd ..`
+   `npm run dev`