Add application file

app.py

@@ -0,0 +1,44 @@
+from transformers import pipeline
+import gradio as gr
+
+model_id = "artyomboyko/distilhubert-finetuned-gtzan"
+pipe = pipeline("audio-classification", model=model_id)
+
+def classify_audio(filepath):
+    preds = pipe(filepath)
+    outputs = {}
+    for p in preds:
+        outputs[p["label"]] = p["score"]
+    return outputs
+
+
+demo = gr.Blocks()
+
+title = "Classification of music"
+description = """
+This demo is designed to test the music classification. It is important to remember that music classification depends very much on the quality of the recording, for example, when classifying a recording from a 
+microphone with poor high frequencies, the song "Evanescence - bring me to life" may not be correctly defined as classical music. But if we transfer a recording of the same song as a file, it is correctly 
+identified as metal music. In addition, a real problem is caused by compositions in which there is a mixture of different styles of music, or modern styles of examples of which were not in the training dataset.
+"""
+
+mic_classify_audio = gr.Interface(
+    fn=classify_audio,
+    inputs=gr.Audio(source="microphone", type="filepath"),
+    outputs=gr.outputs.Label(),
+    title=title,
+    description=description,
+)
+
+file_classify_audio = gr.Interface(
+    fn=classify_audio,
+    inputs=gr.Audio(source="upload", type="filepath"),
+    outputs=gr.outputs.Label(),
+    #examples=[["./example.wav"]],
+    title=title,
+    description=description,
+)
+
+with demo:
+    gr.TabbedInterface([mic_classify_audio, file_classify_audio], ["Microphone", "Audio File"])
+
+demo.launch(debug=True)