Anime Image Generator

This project is a simple Flask web application that generates anime-style images using a Deep Convolutional Generative Adversarial Network (DCGAN). The application loads a pre-trained generator model, generates an image based on random noise, and serves the image via a web interface.

Features

• DCGAN Model Integration: The application uses a TensorFlow/Keras model (DCGEN.h5) to generate anime-style images.
• Flask Web Interface: Provides a simple web interface where users can generate images.
• Image Generation and Serving: Images are generated dynamically and served directly to the user.

Prerequisites

Before running the application, ensure you have the following installed:

• Python 3.x
• Flask
• TensorFlow
• Pillow (PIL)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/anime-generator.git
   cd anime-generator

2. Install the required Python packages:

   pip install Flask tensorflow pillow

3. Ensure the pre-trained generator model DCGEN.h5 is located in the root directory of the project.

4. Create a static folder in the project directory to store the generated images:

   mkdir static

Usage

1. Run the Flask application:

   python app.py

2. Open your web browser and go to http://127.0.0.1:5000/ to access the application.

3. Click on the "Generate" button to create and view a new anime-style image.

File Structure

• app.py: The main Flask application script.
• DCGEN.h5: The pre-trained DCGAN generator model.
• templates/index.html: HTML file for the web interface.
• static/: Directory where generated images are stored temporarily.

How It Works

1. Loading the Model: The pre-trained generator model (DCGEN.h5) is loaded when the Flask application starts.

2. Generating an Image: When a user navigates to the /generate route, the application generates random noise, feeds it into the DCGAN generator, and produces an image.

3. Serving the Image: The generated image is saved as generated_image.png in the static/ directory and is served to the user as a PNG file.

Troubleshooting

• Model Not Found: Ensure the DCGEN.h5 file is in the correct directory.
• Port Issues: If the default port 5000 is in use, you can specify a different port by modifying the app.run() line in app.py.

License

This project is licensed under the MIT License. See the LICENSE file for more details.