readme.md

# 🐶 Dog Breed Classification Desktop App

This is a standalone desktop application built using **Tkinter** and **TensorFlow**, capable of identifying dog breeds from images. The app uses a trained `.h5` deep learning model and supports creating `.exe` and `.msi` installers via **cx_Freeze** or **PyInstaller**.

---

## 🚀 Features

- ✅ Offline desktop app (no Python installation required)
- 🖼️ Upload any dog image (JPG, PNG, JPEG)
- 🔍 Predicts dog breed with confidence score
- 📂 Simple folder structure and user-friendly GUI
- 🛠️ Create `.exe` or `.msi` installers with `cx_Freeze`
- ⚠️ Error handling for missing models or files

---

## 🧠 Model Info

- Trained on **31 dog breeds** including:
```

Akita Inu, German Shepherd, Labrador Retriever, Poodle, Pug, Golden Retriever,
Siberian Husky, Bulldog, Saint Bernard, Rottweiler, and more.

```
- Input Image Size: **224 x 224**
- Format: `dog_breed_classifier.h5` (Keras model)

---

## 📁 Folder Structure

```

DogBreedApp/
│
├── app.py                  # Main GUI application file
├── dog\_breed\_classifier.h5 # Trained TensorFlow model
├── class\_indices.json      # Label-to-classname mapping
├── setup.py                # cx\_Freeze build script
├── dog.ico                 # (optional) Icon file

````

---

## 🛠 Requirements

Install these Python packages:

```bash
pip install -r requirements.txt
````

**requirements.txt**

```txt
tensorflow
numpy
pillow
cx-Freeze
```

---

## 🖥️ Running the App

```bash
python app.py
```

---

## 📦 Creating Executable (.exe or .msi)

### ✅ Using cx\_Freeze

1. Create `setup.py`
2. Build:

```bash
python setup.py build
```

3. For MSI Installer:

```bash
python setup.py bdist_msi
```

This generates:

* `.exe` in `build/`
* `.msi` installer in `dist/`

---

## 🧊 Packaging Notes

* Use `resource_path()` for model/JSON so PyInstaller or cx\_Freeze can bundle files.
* Include `.h5` and `.json` in `include_files` in `setup.py`.
* Works without requiring Python installation.

---

## 📸 Screenshots

![Upload and Detect](screenshots/ui1.png)
![Prediction Result](screenshots/ui2.png)

---

## 📜 License

This project is open-source and available for educational and personal use.

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## 👨‍💻 Author

Developed by **Jayasimma D**

```

---

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