https://github.com/dd-se/ml-app

Predict unseen numbers with ML models trained on MNIST dataset.
https://github.com/dd-se/ml-app

opencv python scikit-learn streamlit

Last synced: about 1 year ago
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Predict unseen numbers with ML models trained on MNIST dataset.

• Host: GitHub
• URL: https://github.com/dd-se/ml-app
• Owner: dd-se
• License: mit
• Created: 2025-03-28T11:57:46.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-03-28T12:13:07.000Z (about 1 year ago)
• Last Synced: 2025-03-28T12:35:02.369Z (about 1 year ago)
• Topics: opencv, python, scikit-learn, streamlit
• Language: Jupyter Notebook
• Homepage:
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
## Prerequisites

Before running the app, ensure Python 3.10 or higher installed.

## Installation

1. Clone the repository:

    ```bash

    git clone https://github.com/dd-se/ml-app

    cd ml-app

    ```

2. Create a virtual environment (optional but recommended):

    ```bash

    python -m venv .venv

    source .venv/bin/activate  # On Windows: .venv\Scripts\activate

    ```

3. Install the required dependencies:

    ```bash

    pip install -r requirements.txt

    ```

## Running the App

1. Start the Streamlit app:

    ```bash

    streamlit run app.py

    ```

2. Open your browser and navigate to the URL provided in the terminal (usually `http://localhost:8501`).