📊 Streamline Sales Suite

📄 Overview

Streamline Sales Suite is a comprehensive platform designed for data analysis and visualization, powered by Python and advanced deep learning techniques. This suite includes a Convolutional Neural Network (CNN) model tailored for item classification, enabling precise categorization and insightful data analysis.

🚀 Getting Started

To set up and use this repository, follow these steps:

1. Clone the Repository:

   git clone <repository_url>

2. Install Poetry (if not already installed):

   pip install poetry

3. Install Dependencies: Set up the Python virtual environment and install all necessary dependencies:

   poetry install

4. Acquire Training Images: Obtain the images required to train the classification model:

   poetry run python ./src/data_acquisition.py

5. Train the Classification Model: Train the CNN model with the acquired images:

   poetry run python ./src/training_model.py

6. Run the Application: Launch the complete project using Streamlit:

   poetry run streamlit run ./src/1_🏠_Home.py

7. Docker Deployment: A Dockerfile and Docker Compose are included for containerizing the application, which is particularly useful for deployment after the model is trained.

Note: The data analysis component relies on a private dataset and may not be functional without it. However, the project can be adapted to work with other datasets.

🔧 Technologies Used

• Streamlit: For building and deploying the interactive web application.
• Docker: To containerize the application for easy deployment.
• Poetry: For dependency management and virtual environment setup.
• Black: To maintain consistent code formatting.
• Pandas & NumPy: For handling data processing and ETL tasks.
• Loguru: For efficient logging and monitoring of application processes.
• TensorFlow & Keras: For developing and training the deep learning model.
• MLflow: For tracking and managing the machine learning lifecycle.
• OpenCV: For image processing tasks.

🌟 Contributing

Contributions are highly encouraged! Whether you have new tools, models, or techniques to share, your input is welcome. Please feel free to submit a pull request or open an issue to discuss your ideas.

🤖 License

This project is licensed under the MIT License, allowing you to freely use, modify, and distribute the code.

🖥️ GPU Support with Nvidia Container Toolkit

To utilize Nvidia GPUs within Docker, follow these steps:

1. Ensure that the Nvidia drivers are installed on your host machine.
2. Install the Nvidia Container Toolkit by following the official guide.
3. Restart Docker to apply the changes:

   sudo systemctl restart docker

4. Verify the GPU setup with the following command:

   sudo docker run --rm --gpus all nvidia/cuda:11.0.3-base-ubuntu20.04 nvidia-smi

5. Build the Docker image for the project:

   sudo docker build -t SLS-TF-image -f tensorflow.dockerfile .

6. Run the Docker container:

   sudo docker run --gpus all -p 8501:8501 --name SLS-TF-container SLS-TF-image