https://github.com/harshita2234/digit-prediction-streamlit-app

A Streamlit application for predicting handwritten digits using a trained machine learning model. This project includes the necessary code to run the Streamlit app and train the model using a Jupyter notebook.
https://github.com/harshita2234/digit-prediction-streamlit-app

jupyter-notebook prediction-model python

Last synced: about 24 hours ago
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A Streamlit application for predicting handwritten digits using a trained machine learning model. This project includes the necessary code to run the Streamlit app and train the model using a Jupyter notebook.

• Host: GitHub
• URL: https://github.com/harshita2234/digit-prediction-streamlit-app
• Owner: harshita2234
• License: mit
• Created: 2024-06-01T15:26:05.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-06-01T15:41:23.000Z (8 months ago)
• Last Synced: 2024-12-02T11:12:27.483Z (about 2 months ago)
• Topics: jupyter-notebook, prediction-model, python
• Language: Jupyter Notebook
• Homepage:
• Size: 24.8 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Digit Prediction Streamlit Web App

A Streamlit application for predicting handwritten digits using a trained machine learning model.







## Project Structure

- `app.py`: Streamlit app script.

- `Training-pipeline.ipynb`: Jupyter notebook for training the digit prediction model.

- `best_weights.pt`: Trained model weights.

- `utils.py`: Utility functions.

- `models.py`: Model definitions.

## Installation

1. Clone the repository:

   ```bash

   git clone https://github.com/harshita2234/Digit-Prediction-Streamlit-App.git

   cd Digit-Prediction-Streamlit-App

2. Install Required Packages:

   ```bash

   python3 -m pip install opencv-python torch numpy streamlit streamlit-drawable-canvas plotly torchvision pandas matplotlib tqdm

   #or

   python -m pip install opencv-python torch numpy streamlit streamlit-drawable-canvas plotly torchvision pandas matplotlib tqdm

3. Unzip the MNIST folder

## Run the App

To run the Streamlit app, navigate to the project directory and execute:

```bash

   streamlit run app.py