https://github.com/jarif87/dog-breed-classification-app
Predict dog breeds from images using a pre-trained TensorFlow model with a simple Streamlit web app.
https://github.com/jarif87/dog-breed-classification-app
app dog-breed-classifier image opencv-python python streamlit
Last synced: 2 months ago
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Predict dog breeds from images using a pre-trained TensorFlow model with a simple Streamlit web app.
- Host: GitHub
- URL: https://github.com/jarif87/dog-breed-classification-app
- Owner: jarif87
- Created: 2025-05-14T15:05:32.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-14T15:07:22.000Z (about 1 year ago)
- Last Synced: 2025-06-12T04:40:15.334Z (about 1 year ago)
- Topics: app, dog-breed-classifier, image, opencv-python, python, streamlit
- Language: Jupyter Notebook
- Homepage:
- Size: 5.27 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Dog Breed Prediction Web App
- This is a Dog Breed Prediction app built using Streamlit and TensorFlow. The app allows you to predict the breed of a dog based on an uploaded image through a simple terminal interface.
## Features
- Dog Breed Prediction: Upload an image of a dog, and the app will predict its breed using a trained model.
- Model: A pre-trained TensorFlow model is used for dog breed classification.
- Supported Formats: PNG, JPG, and JPEG image formats.
# Technologies Used
* Streamlit: To build the web interface for the application.
* TensorFlow: For loading the pre-trained model and performing predictions.
* OpenCV: To handle image processing for input images.
# Setup Instructions
1. **Clone the Repository:**
**First, clone this repository to your local machine using Git.**
```
git clone https://github.com/yourusername/dog-breed-prediction.git
cd dog-breed-prediction
```
2. **Set Up Python Environment**
**To avoid conflicts with other projects, it’s a good practice to set up a virtual environment.**
```
python3 -m venv env
source env/bin/activate # On Windows, use `env\Scripts\activate`
```
3. **Install Required Libraries**
**Install the required Python libraries using pip.**
```
pip install -r requirements.txt
```
4. **Place the Pre-trained Model**
**Make sure you have the pre-trained model file (my_model.keras) in the project directory. If you don't have it, either train the model or download a suitable pre-trained model for the dog breeds.**
5. **Run the Streamlit App**
**Start the Streamlit app in the terminal by running:**
```
streamlit run app.py
```
# Usage
* Upload Dog Image: Click on the "Choose an image..." button to upload an image of a dog.
* Predict: After uploading, click the "Predict" button to get the breed prediction.
* Result: The breed name will be displayed in the app as the predicted breed.