ThoraxInsight

Introduction

This project is structured into three main parts, each fulfilling a critical role in the overall functionality of the application. Here’s a quick overview of each component:

1. Backend (Folder: back)

   • The backend of this application is developed using FastAPI, a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints. This component is responsible for handling all server-side logic, including API endpoints, database interactions, and server-side processing.

2. Frontend (Folder: front/thoraxinsight)

   • The frontend is built using React, a popular JavaScript library for building user interfaces. This part of the application handles the client-side rendering, providing a dynamic and interactive user experience. All the UI components, state management, and client-side routing are implemented here.

3. Neural Network Training (Folder: training)

   • This folder contains all the Python scripts necessary for training the neural network. It includes the data preprocessing scripts, model definition, training loop, and evaluation metrics. Additionally, the final trained model is exported and stored as a file, which is utilized by the backend for making predictions.

Below, you will find detailed instructions on how to set up and run each part of the project.

Step-by-Step Guide to Run the Project

Prerequisite: Clone the Repository

1. Clone the repository to your local machine:

   git clone https://github.com/AML4206-MINE20242/Proyecto_AML.git
   cd Proyecto_AML

Backend (Folder: back)

1. Navigate to the back directory:

   cd back

2. Create a virtual environment and activate it:

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

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the FastAPI server:

   uvicorn main:app --reload

   The backend server will be running at http://127.0.0.1:8000.

Frontend (Folder: front/thoraxinsight)

1. Navigate to the front/thoraxinsight directory:

   cd front/thoraxinsight

2. Install the required dependencies:

   npm install

3. Start the React development server:

   npm start

   The frontend will be running at http://localhost:3000.

Docker Deployment (Optional)

For a containerized deployment, you can use the provided Dockerfile for backend and frontend.

Backend and Worker

1. Navigate to the root directory of the project (if not already there):

   cd Proyecto_AML

2. Build the Docker image for the backend and worker:

   docker build -t proyecto_aml_backend_worker .

3. Run the Docker container for the backend and worker:

   docker run -p 8000:8000 proyecto_aml_backend_worker

Frontend

1. Navigate to the front directory:

   cd front

2. Build the Docker image for the frontend:

   docker build -t proyecto_aml_front .

3. Run the Docker container for the frontend:

   docker run -p 3000:3000 proyecto_aml_front

This will start both the backend service and the frontend inside Docker containers. Ensure the frontend is configured to communicate with the backend via the appropriate API endpoints.

By following these steps, you should have the entire project up and running, allowing you to interact with the backend API, use the frontend interface, and train the neural network model.

Model Training

The chosen model for deployment is DenseNet121 from Torch library. To train the model follow the next steps:

1. Navigate to the training directory:

   cd training

2. Enter to the dataset folder where CheXPert dataset will be located

   cd CheXpert-v1.0-small

3. Download the CheXPert database from the following link
4. Unzip the folder to obtain the dataset

   unzip CheXpert-v1.0-small.zip

5. Locate yourself at the level of the folder training

   cd ..

6. Run the main3.py

   python main3.py