app.py

import gradio as gr
from pipeline import run_pipeline

def infer(file):
    # file is a tempfile-like object from Gradio
    return run_pipeline(file.name, out_style="json")

demo = gr.Interface(
    fn=infer,
    inputs=gr.Audio(sources=["upload", "microphone"], type="filepath"),  # accepts mp3/mp4/wav; ffmpeg handles it
    outputs=gr.Code(label="Result (JSON)"),
    title="Aphasia Classification",
    description="Upload audio/video; pipeline: ffmpeg → .cha → JSON → model → result."
)

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