CIFAR-10 Image Classifier with a Web UI

This repository contains a complete project for building, training, and deploying a Convolutional Neural Network (CNN) to classify images from the CIFAR-10 dataset. The project includes the training notebook and a user-friendly web interface built with Gradio.

Project Features

• Model Training: A Jupyter Notebook (train_classifier.ipynb) that details the entire process of loading, preprocessing, building, training, and evaluating the CNN. The final model achieves 77.77% accuracy on the test set.
• Interactive Web App: A Gradio application (app.py) that loads the pre-trained model and allows users to upload their own images for instant classification.
• Robust CNN Architecture: The model uses multiple convolutional blocks with MaxPooling and Dropout layers to effectively learn features and prevent overfitting.

Tech Stack

• TensorFlow / Keras: For building and training the deep learning model.
• Gradio: For creating the interactive web UI.
• Python, NumPy, Matplotlib

How to Run the Application

1. Get the Code

First, get the code by cloning the repository or downloading it as a ZIP file.

2. Create a Virtual Environment

It is highly recommended to use a virtual environment to keep your project dependencies isolated.

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

3. Install Dependencies

Install all the required packages using the requirements.txt file:

pip install -r requirements.txt

4. Run the Application

Launch the Gradio app by running the following command:

python app.py

The application will be available at a local URL (usually http://127.0.0.1:7860).