apps/gradio_app.py

import os
import gradio as gr
import json
from gradio_app.inference import run_inference, run_setup_script

def create_app():
    # Run setup script at startup
    setup_output = run_setup_script()
    # Load CSS file
    CSS = open("apps/gradio_app/static/styles.css", "r").read()
    
    with gr.Blocks(css=CSS) as app:
        gr.HTML('<script src="file=apps/gradio_app/static/scripts.js"></script>')
        gr.Markdown(
            """
            # Text to Video Ghibli style
            Generate videos using the `zeroscope_v2_576w` model with Studio Ghibli style LoRA.
            """
        )

        with gr.Row(elem_classes="row-container"):
            with gr.Column(elem_classes="column-container"):
                model_path = gr.Dropdown(
                    label="Base Model", 
                    choices=["./ckpts/zeroscope_v2_576w"],
                    value="./ckpts/zeroscope_v2_576w"
                )
                checkpoint_folder = gr.Dropdown(
                    label="LoRA folder", 
                    choices=["./ckpts/zeroscope_v2_576w-Ghibli-LoRA"],
                    value="./ckpts/zeroscope_v2_576w-Ghibli-LoRA"
                )
                prompt = gr.Textbox(
                    label="Prompt", 
                    value="Studio Ghibli style. Two women walk down coastal village path toward sea, passing colorful houses, sailboats visible."
                )
                negative_prompt = gr.Textbox(
                    label="Negative Prompt", 
                    value="ugly, noise, fragment, blur, static video"
                )
                
                # Video Dimensions & Timing
                with gr.Row(elem_classes="slider-row"):
                    with gr.Group(elem_classes="slider-group"):
                        gr.Markdown("### Video Dimensions & Timing")
                        width = gr.Slider(label="Width", minimum=256, maximum=1024, step=8, value=512)
                        height = gr.Slider(label="Height", minimum=256, maximum=1024, step=8, value=512)
                        num_frames = gr.Slider(label="Number of Frames", minimum=8, maximum=64, step=1, value=16)
                        fps = gr.Slider(label="FPS", minimum=10, maximum=60, step=1, value=16)
                        seed = gr.Number(label="Seed", value=100)
                
                generate_btn = gr.Button("Generate Video", elem_classes="generate-btn")

            with gr.Column(elem_classes="column-container"):
                video_output = gr.Video(label="Generated Video")
                log_output = gr.Textbox(label="Logs", lines=3, max_lines=20)
                
                # Model Parameters
                with gr.Row(elem_classes="slider-row"):
                    with gr.Group(elem_classes="slider-group"):
                        gr.Markdown("### Model Parameters")
                        num_steps = gr.Slider(label="Number of Steps", minimum=10, maximum=100, step=1, value=50)
                        guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=50.0, step=0.1, value=30.0)
                        lora_rank = gr.Slider(label="LoRA Rank", minimum=16, maximum=128, step=8, value=96)
                        lora_scale = gr.Slider(label="LoRA Scale", minimum=0.1, maximum=1.0, step=0.1, value=0.7)
                        noise_prior = gr.Slider(label="Noise Prior", minimum=0.0, maximum=1.0, step=0.01, value=0.1)

        # Example Buttons Section
        gr.Markdown("## Example Configurations")
        example_base_path = "apps/assets/examples/zeroscope_v2_576w-Ghibli-LoRA"
        example_buttons = []
        configs = []

        for i in range(1, 5):
            example_dir = os.path.join(example_base_path, str(i))
            config_path = os.path.join(example_dir, "config.json")
            if os.path.exists(config_path):
                with open(config_path, "r") as f:
                    config = json.load(f)
                video_path = os.path.join(example_dir, config["video"])
                if os.path.exists(video_path):
                    configs.append((config, video_path))
                    example_buttons.append(gr.Button(f"Load Example {i}"))

        def create_example_fn(config, video_path):
            def load_example():
                return [
                    "./ckpts/zeroscope_v2_576w",  # model_path
                    "./ckpts/zeroscope_v2_576w-Ghibli-LoRA",  # checkpoint_folder
                    config.get("prompt", ""),
                    config.get("negative-prompt", ""),
                    config.get("width", 512),
                    config.get("height", 512),
                    config.get("num-frames", 16),
                    config.get("num-steps", 50),
                    config.get("guidance_scale", 30.0),
                    config.get("fps", 16),
                    config.get("lora_rank", 96),
                    config.get("lora_scale", 0.7),
                    config.get("noise_prior", 0.1),
                    config.get("seed", 100),
                    video_path,  # video_output
                    f"Loaded example with prompt: {config.get('prompt', '')}"  # log_output
                ]
            return load_example

        for btn, (config, video_path) in zip(example_buttons, configs):
            btn.click(
                fn=create_example_fn(config, video_path),
                inputs=[],
                outputs=[
                    model_path, checkpoint_folder, prompt, negative_prompt,
                    width, height, num_frames, num_steps, guidance_scale,
                    fps, lora_rank, lora_scale, noise_prior, seed,
                    video_output, log_output
                ]
            )

        generate_btn.click(
            fn=run_inference,
            inputs=[
                model_path, checkpoint_folder, prompt, negative_prompt,
                width, height, num_frames, num_steps, guidance_scale,
                fps, lora_rank, lora_scale, noise_prior, seed
            ],
            outputs=[video_output, log_output]
        )

        gr.Markdown("""
                    This repository is trained from [![GitHub Repo](https://img.shields.io/badge/GitHub-danhtran2mind%2FMotionDirector-blue?style=flat)](https://github.com/danhtran2mind/MotionDirector), a fork of [![GitHub Repo](https://img.shields.io/badge/GitHub-showlab%2FMotionDirector-blue?style=flat)](https://github.com/showlab/MotionDirector), with numerous bug fixes and rewritten code for improved performance and stability.
                    """)
    return app

if __name__ == "__main__":
    app = create_app()
    app.launch()