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https://github.com/danibcorr/streamlit-sales-suite
📊 An all-encompassing web platform for data analysis and visualization, powered by Streamlit, complemented by an automated item classifier that streamlines the process of label assignment.
https://github.com/danibcorr/streamlit-sales-suite
data-analysis data-science data-scraping data-visualization deep-learning insights keras machine-learning mlflow mlops numpy pandas plotly python sales sales-analysis streamlit streamlit-dashboard tensorflow
Last synced: about 1 month ago
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📊 An all-encompassing web platform for data analysis and visualization, powered by Streamlit, complemented by an automated item classifier that streamlines the process of label assignment.
- Host: GitHub
- URL: https://github.com/danibcorr/streamlit-sales-suite
- Owner: danibcorr
- License: mit
- Created: 2024-04-12T19:38:55.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-08-27T15:22:36.000Z (4 months ago)
- Last Synced: 2024-08-27T16:56:05.730Z (4 months ago)
- Topics: data-analysis, data-science, data-scraping, data-visualization, deep-learning, insights, keras, machine-learning, mlflow, mlops, numpy, pandas, plotly, python, sales, sales-analysis, streamlit, streamlit-dashboard, tensorflow
- Language: Python
- Homepage:
- Size: 1.19 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 📊 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:**
```bash
git clone
```
2. **Install Poetry (if not already installed):**
```bash
pip install poetry
```
3. **Install Dependencies:**
Set up the Python virtual environment and install all necessary dependencies:
```bash
poetry install
```
4. **Acquire Training Images:**
Obtain the images required to train the classification model:
```bash
poetry run python ./src/data_acquisition.py
```
5. **Train the Classification Model:**
Train the CNN model with the acquired images:
```bash
poetry run python ./src/training_model.py
```
6. **Run the Application:**
Launch the complete project using Streamlit:
```bash
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](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#).
3. Restart Docker to apply the changes:
```bash
sudo systemctl restart docker
```
4. Verify the GPU setup with the following command:
```bash
sudo docker run --rm --gpus all nvidia/cuda:11.0.3-base-ubuntu20.04 nvidia-smi
```
5. Build the Docker image for the project:
```bash
sudo docker build -t SLS-TF-image -f tensorflow.dockerfile .
```
6. Run the Docker container:
```bash
sudo docker run --gpus all -p 8501:8501 --name SLS-TF-container SLS-TF-image
```