Update app.py

app.py

@@ -2,26 +2,21 @@
 import gradio as gr
 from transformers import pipeline
 
-# Pretrained model for audio classification
-MODEL = "superb/wav2vec2-base-superb-ks"  # keyword spotting (yes, no, up, down...)
+# Load pipeline (pretrained model)
+classifier = pipeline("audio-classification", model="superb/wav2vec2-base-superb-ks")
 
-classifier = pipeline("audio-classification", model=MODEL)
+def classify_audio(audio):
+    # audio is a tuple: (sample_rate, numpy array)
+    if audio is None:
+        return "No audio provided"
+    return classifier(audio)
 
-def classify_audio(audio_file):
-    # audio_file is a tuple: (sample_rate, numpy_array) if "numpy", or path if "filepath"
-    if isinstance(audio_file, str):  # filepath
-        return classifier(audio_file)
-    else:  # (sr, data)
-        sr, data = audio_file
-        return classifier({"array": data, "sampling_rate": sr})
-
-demo = gr.Interface(
-    fn=classify_audio,
-    inputs=gr.Audio(sources=["microphone", "upload"], type="filepath"),
-    outputs=gr.Label(),
-    title="🎵 Audio Classification",
-    description="Upload or record audio. Model: wav2vec2-base-superb-ks"
-)
+with gr.Blocks() as demo:
+    gr.Markdown("## 🎵 Audio Classification (Keyword Spotting)")
+    with gr.Row():
+        audio_input = gr.Audio(sources=["microphone", "upload"], type="numpy")
+        output = gr.JSON()
+    audio_input.change(classify_audio, audio_input, output)
 
 if __name__ == "__main__":
     demo.launch()