Create app.py

app.py

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+import gradio as gr
+from chord_extractor.extractors import Chordino
+import pandas as pd
+
+# Initialize the extractor
+# We do this outside the function so it only loads once when the app starts
+chordino = Chordino()
+
+def process_audio(audio_path):
+    if audio_path is None:
+        return None, "Please upload an audio file."
+    
+    try:
+        # Extract chords from the file
+        # chord-extractor handles the conversion internally
+        extracted_chords = chordino.extract(audio_path)
+        
+        # Format the data for the Gradio Dataframe
+        data = []
+        for c in extracted_chords:
+            # Rounding timestamp for better readability
+            data.append([round(c.timestamp, 2), c.chord])
+        
+        return data, f"Successfully extracted {len(data)} chord changes."
+    
+    except Exception as e:
+        return None, f"Error: {str(e)}"
+
+# Define the Gradio Interface
+with gr.Blocks(title="Guitar Chord Extractor") as demo:
+    gr.Markdown("# 🎸 Guitar Chord Extractor")
+    gr.Markdown("Upload an audio file (mp3, wav, flac) to extract the chord progression using Chordino.")
+    
+    with gr.Row():
+        with gr.Column():
+            audio_input = gr.Audio(label="Upload Audio", type="filepath")
+            submit_btn = gr.Button("Extract Chords", variant="primary")
+        
+        with gr.Column():
+            status_output = gr.Textbox(label="Status")
+            chord_output = gr.Dataframe(
+                headers=["Timestamp (s)", "Chord"],
+                datatype=["number", "string"],
+                label="Extracted Progression"
+            )
+
+    submit_btn.click(
+        fn=process_audio,
+        inputs=[audio_input],
+        outputs=[chord_output, status_output]
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()