Computer Vision Project

This project demonstrates a basic setup for a computer vision application using Flask. The following instructions guide you through the process of cloning, setting up, and running the project locally.

Table of Contents

• Cloning the Project
• Setup Instructions
  • Install Flask
  • Create Project Structure
  • Create Basic Flask App
  • Set Up Templates Folder
  • Create HTML File
  • Add Static Folder
  • Save Your Model
  • Link Model in app.py
• Running the Application

Cloning the Project

To clone this project, use the following command:

git clone https://github.com/Dhanush0000/Computer_vision.git

Setup Instructions

Install Flask

Install Flask Using pip

pip install flask

Create Project Structure

Create a main folder for your project:

mkdir flask_website
cd flask_website

Create Basic Flask app

Inside flask_website, create the main Flask application file:

touch app.py

In app.py, initialize a basic Flask app.

Set Up Templates Folder

Create a templates folder to store HTML files:

mkdir templates

Create HTML File

Add an HTML file for the homepage in the templates folder (e.g., index.html).

Add Static Folder

Create a static folder to contain CSS and JavaScript files:

mkdir static

Inside static, you can create css and js subfolders if needed.

It's only a if situation, index.html file has both css and js inline

Save Your Model

Create a modules folder where you’ll save your trained model:

mkdir modules

Save your model file in this folder.

Link Model in app.py

In app.py, provide the path to your saved model file for predictions. This will allow you to load the model and use it to predict classes.

Running the Application

Once everything is set up, run the Flask application using the command:

python app.py

Check the terminal for a local link to access the app in your browser. Open the link to view and interact with your computer vision project.