Update app.py

app.py

@@ -3,10 +3,17 @@ import torch
 from transformers import pipeline
 from transformers.pipelines.audio_utils import ffmpeg_read
 import gradio as gr
+import gradio as gr
 
 
-model_id = "wav2vec2-base-vinyl_condition"  
+MODEL_ID = "wav2vec2-base-vinyl_condition"  
 
+pipe = pipeline(
+    task="audio-classification",
+    model=MODEL_ID,
+    chunk_length_s=30,
+    device=device,
+)
 
 def get_vinyl_condition(filepath):
     output = pipe(
@@ -17,18 +24,35 @@ def get_vinyl_condition(filepath):
     )
     return output[0]["label"]
 
-import gradio as gr
 
 demo = gr.Blocks()
 
 
+
+demo = gr.Blocks()
+
 file_transcribe = gr.Interface(
     fn=get_vinyl_condition,
-    inputs=gr.Audio(sources="upload", type="filepath"),
-    outputs=gr.outputs.Textbox(),
+    inputs=[
+        gr.inputs.Audio(source="upload", optional=True, label="Audio file", type="filepath"),
+    ],
+    outputs="text",
+    layout="horizontal",
+    theme="huggingface",
+    title="Vinyl Demo: Get Vinyl Condition",
+    description=(
+        "Get your vinyl ocndition based on the golmine grading starndard! Demo uses the"
+        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to get the condition of audio files"
+        " of arbitrary length."
+    ),
+    examples=[
+        ["./example.flac"],
+    ],
+    cache_examples=True,
+    allow_flagging="never",
 )
 
 with demo:
     gr.TabbedInterface([file_transcribe], ["Transcribe Audio File"])
 
-demo.launch(enable_queue=True)
+demo.launch(enable_queue=True)