Update app.py

app.py

@@ -7,7 +7,7 @@ import gradio as gr
 
 device = 0 if torch.cuda.is_available() else "cpu"
 
-MODEL_ID = "jvalero/wav2vec2-base-vinyl_condition"  
+MODEL_ID = "jvalero/wav2vec2-base-music_genre_classifier"  
 
 pipe = pipeline(
     task="audio-classification",
@@ -16,7 +16,7 @@ pipe = pipeline(
     device=device,
 )
 
-def get_vinyl_condition(filepath):
+def get_edm(filepath):
     output = pipe(
         filepath,
         max_new_tokens=256,
@@ -33,16 +33,16 @@ demo = gr.Blocks()
 demo = gr.Blocks()
 
 file_transcribe = gr.Interface(
-    fn=get_vinyl_condition,
+    fn=get_edm,
     inputs=[
         gr.Audio(sources="upload", label="Audio file", type="filepath"),
     ],
     outputs="label",
     title="Vinyl Condition Classificator",
     description=(
-        "Get your vinyl condition based on the goldmine grading standard! Demo uses the"
+        "Get the genre of your song! Demo uses the"
         f" checkpoint [{MODEL_ID}](https://huggingface.co/{MODEL_ID}) and 🤗 Transformers to get the condition of audio files"
-        " of arbitrary length."
+        " of arbitrary length. \nThe audio will be classified into one of the following: ['drumbass', 'dubtechno', 'dupstep', 'hardcore_breaks', 'house', 'psytrance', 'techno', 'ukgarage']"
     ),
     examples=[
         ["./example.mp3"],