| 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() | |