Update app.py

app.py

@@ -1,15 +1,15 @@
 import gradio as gr
 import keras
 import librosa
-import hopsworks
+#import hopsworks
 import os
 import numpy as np
 import shutil
 from functions import log_mel_spectrogram, split_spectrogram, load_audio_file, image_transformer, save_spectrogram_as_png
 from datasets import load_dataset
 
-def empty_string():
-    return ""
+def return_input(input):
+    return input
 
 def create_image_folder(folder):
     try:
@@ -110,17 +110,30 @@ with gr.Blocks() as demo:
     
     The model has been trained to predict 8 top-level genres, that each encompasses a multitude of sub-genres.
     
-    Upload your favorite song and give it a try!
+    The top-level genres are:
+    
+    1. Electronic
+    2. Experimental
+    3. Folk
+    4. Hip-Hop
+    5. Intrumental
+    6. International
+    7. Pop
+    8. Rock
+    
+    Upload your favorite song or choose one of the example tracks and give it a try!
     """
         )
     with gr.Row():
         with gr.Column():
             audio = gr.Audio(sources="upload", type="filepath", label="Upload your song here", format="wav")
         with gr.Column():
-            answer_box = gr.Text(label="Answer appears here", interactive=False)
+            files = gr.FileExplorer(label="Example songs", file_count="single", root="examples", interactive=True)
+            files.change(fn=return_input, inputs=files, outputs=audio)
+    with gr.Row():
+        answer_box = gr.Text(label="Answer appears here", interactive=False)
     with gr.Row():
         submit_audio = gr.Button(value="Submit audio for prediction")
-        submit_audio.click(fn=empty_string, outputs=answer_box)
         submit_audio.click(fn=predict, inputs=audio, outputs=answer_box, trigger_mode="once")
         submit_audio.click(fn=delete_folder)