Add: CoNeTTE streamlit application for inference on files.

app.py

@@ -1,4 +1,39 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from tempfile import NamedTemporaryFile
+
 import streamlit as st
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+from conette import CoNeTTEModel, conette
+
+
+@st.cache_resource
+def load_conette(*args, **kwargs) -> CoNeTTEModel:
+    return conette(*args, **kwargs)
+
+
+def main() -> None:
+    st.header("CoNeTTE model test")
+    audios = st.file_uploader(
+        "Upload an audio file",
+        type=["wav", "flac", "mp3", "ogg", "avi"],
+        accept_multiple_files=True,
+    )
+
+    if audios is not None and len(audios) > 0:
+        model = load_conette(model_kwds=dict(device="cpu"))
+
+        for audio in audios:
+            with NamedTemporaryFile() as temp:
+                temp.write(audio.getvalue())
+                fpath = temp.name
+                outputs = model(fpath)
+                cand = outputs["cands"][0]
+
+                st.write(f"Output for {audio.name}:")
+                st.write(" - ", cand)
+
+
+if __name__ == "__main__":
+    main()