https://github.com/guillermo-villar/ai-project
Personal AI development project - Digit detection with Python & Tensorflow
https://github.com/guillermo-villar/ai-project
ai ml python tensorflow
Last synced: 2 months ago
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Personal AI development project - Digit detection with Python & Tensorflow
- Host: GitHub
- URL: https://github.com/guillermo-villar/ai-project
- Owner: Guillermo-villar
- Created: 2024-12-28T12:11:23.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-28T19:35:31.000Z (over 1 year ago)
- Last Synced: 2025-02-07T02:38:39.442Z (over 1 year ago)
- Topics: ai, ml, python, tensorflow
- Language: Python
- Homepage:
- Size: 1.05 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
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# Guillermo-villar
=======
# Personal AI development proyect - Digit detection in Python
This proyect uses Tensorflow to create a Machine Learning pipeline able to detect hand written digits, learning from a MINST digit dataset. The graphical interface uses Tkinter.
## Description
This repository contains a digit classification model based on the MNIST dataset. The model is trained to recognize handwritten digits from 0 to 9. The application includes a graphical interface built with Python that allows the user to draw a digit and predict its value using the trained model.
## Characteristics
- Digit classification model trained with TensorFlow.
- Python graphical interface using Tkinter to draw handwritten digits.
- Real-time predictions using the trained model.
- Visualization of results, displaying the predicted digit and its probability.
## Instalation
To test and try this code on your own machine, follow along the instructions:
1. Clone the repository:
```bash
git clone https://github.com/your-username/digit-prediction-ia.git
cd digit-prediction-ia
```
2. Create and activate a virtual environment:
```bash
python -m venv venv
source venv/bin/activate # On Linux/Mac
venv\Scripts\activate # On Windows
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
## Usage
1. Train the prediction model by running the following command:
```bash
python train.py
```
2. Launch the graphical interface to draw and predict a digit:
```bash
python app.py
```
3. The app will allow you to draw a hand written digit and test the model with your own hands!
## Proyect structure
**ML_pipeline.py** : This file will hold all Machine Learning logic, as well as the code for the training of the model and the function used to predict
**App.py** : This file will hold the graphical interface and house the actual running of my app.
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