CarVision

CarVision is a web application that enables users to upload images of cars and uses machine learning to identify the car model. The application is built with Flask, Tailwind CSS, and TensorFlow.

Features

• Image Upload: Users can upload car images for analysis.
• Image Cropping: Provides the option to crop the image for better prediction accuracy.
• Model Prediction: Utilizes a TensorFlow model to predict and display the most likely car models.

Getting Started

These instructions will guide you through setting up a copy of the project on your local machine for development and testing purposes.

Prerequisites

Before you begin, ensure you have the following installed:

• Python 3.8 or higher
• pip (Python package installer)
• npm (Node.js package manager)

Installation

1. Clone the Repository

   git clone https://github.com/KeenanS04/CarVision.git

2. Navigate to the Project Directory

   cd CarVision

3. Set Up a Python Virtual Environment (Optional)

   python -m venv car_vision
   source car_vision/bin/activate  # On Windows use `car_vision\Scripts\activate`

4. Install Python Dependencies

   pip install -r requirements.txt

5. Install npm Packages

   npm install

6. Build Tailwind CSS File

   npx tailwindcss -i ./app/static/css/tailwind.css -o ./app/static/css/style.css --minify

7. Start the Flask Application

   flask run

   or

   python run.py

   Now, the application should be running on http://localhost:5000.

Built With

• Flask - The web framework used.
• Tailwind CSS - For styling the application.
• TensorFlow - Used for making model predictions.

Note

The machine learning model is a work in progress, and prediction accuracy is expected to improve over time.