import gradio as gr

from modelscope.pipelines import pipeline
from modelscope.outputs import OutputKeys

pipe = pipeline(task='image-to-video', model='damo/Image-to-Video', model_revision='v1.1.0')

def infer (audio_in):
    
    # IMG_PATH: your image path (url or local file)
    IMG_PATH = audio_in
    output_video_path = pipe(IMG_PATH, output_video='output.mp4')[OutputKeys.OUTPUT_VIDEO]
    print(output_video_path)
    
    return output_video_path

css="""

#col-container {
    max-width: 780px; 
    margin-left: auto; 
    margin-right: auto;
}
img[src*='#center'] { 
    display: block;
    margin: auto;
}
.footer {
    margin-bottom: 45px;
    margin-top: 10px;
    text-align: center;
    border-bottom: 1px solid #e5e5e5;
}
.footer > p {
    font-size: .8rem;
    display: inline-block;
    padding: 0 10px;
    transform: translateY(10px);
    background: white;
}
.dark .footer {
    border-color: #303030;
}
.dark .footer > p {
    background: #0b0f19;
}

"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("""
        
            <h1 style="text-align: center;">
                MS Image2Video
            </h1>

            [![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm.svg#center)](https://huggingface.co/spaces/fffiloni/MS-Image2Video-cloning?duplicate=true)

        
        """)

        image_in = gr.Image(
            label = "Source Image",
            source = "upload", 
            type = "filepath"
        )

        submit_btn = gr.Button(
            "Submit"
        )

        video_out = gr.Video(
            label = "Video Result"
        )

        gr.HTML("""
        
            <div class="footer">
                <p>
                MS-Image2Video Demo by 🤗 <a href="https://twitter.com/fffiloni" target="_blank">Sylvain Filoni</a>
                </p>
            </div>

        """)

    submit_btn.click(
        fn = infer,
        inputs = [
            audio_in
        ],
        outputs = [
            video_out
        ]
    )

demo.queue(max_size=20).launch()