from __future__ import annotations

import gradio as gr

from codec_core import run_benchmark


def analyze(video):
    if not video:
        return "Upload a short video to begin.", []
    return run_benchmark(video)


with gr.Blocks(title="Simam Neural Codec Lab") as demo:
    gr.Markdown("# Simam Neural Codec Lab\nMeasure the trade-offs that matter for immersive video: quality, bitrate, latency, GPU cost, and decoder compatibility.")
    with gr.Row():
        video = gr.Video(label="Short source clip")
        report = gr.Markdown("Upload a clip, then run the benchmark.")
    run = gr.Button("Run codec comparison", variant="primary")
    gr.Examples([["examples/sample_codec.mp4"]], inputs=[video], label="Try the included synthetic fixture")
    outputs = gr.Files(label="Generated comparison files")
    run.click(analyze, inputs=video, outputs=[report, outputs])
    gr.Markdown("### Research boundary\nThis baseline uses host FFmpeg encoders. It is the control group for future learned-codec adapters; it does not claim neural compression superiority.")


if __name__ == "__main__":
    demo.launch()