Update app.py

app.py

@@ -2,31 +2,54 @@ 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
+# Initialize the extractor once
 chordino = Chordino()
 
+# Minimum duration to consider a chord "real"
+MIN_DURATION = 0.5  # seconds
+
+def format_timestamp(ts):
+    """Convert seconds to mm:ss format"""
+    minutes = int(ts // 60)
+    seconds = int(ts % 60)
+    return f"{minutes}:{seconds:02}"
+
 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
+        # Extract chords
         extracted_chords = chordino.extract(audio_path)
         
-        # Format the data for the Gradio Dataframe
-        data = []
+        # Filter out very short chords and format timestamps
+        filtered_data = []
+        prev_timestamp = None
+        prev_chord = None
+
         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."
+            # Always include the first chord
+            if prev_timestamp is None:
+                filtered_data.append([format_timestamp(c.timestamp), c.chord])
+                prev_timestamp = c.timestamp
+                prev_chord = c.chord
+                continue
+
+            # Calculate duration since last chord
+            duration = c.timestamp - prev_timestamp
+
+            # Only include if chord lasted at least MIN_DURATION or chord changed
+            if duration >= MIN_DURATION or c.chord != prev_chord:
+                filtered_data.append([format_timestamp(c.timestamp), c.chord])
+                prev_timestamp = c.timestamp
+                prev_chord = c.chord
+
+        return filtered_data, f"Successfully extracted {len(filtered_data)} chord changes."
     
     except Exception as e:
         return None, f"Error: {str(e)}"
 
-# Define the Gradio Interface
+# Define 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.")
@@ -39,8 +62,8 @@ with gr.Blocks(title="Guitar Chord Extractor") as demo:
         with gr.Column():
             status_output = gr.Textbox(label="Status")
             chord_output = gr.Dataframe(
-                headers=["Timestamp (s)", "Chord"],
-                datatype=["number", "string"],
+                headers=["Timestamp", "Chord"],
+                datatype=["string", "string"],
                 label="Extracted Progression"
             )
 
@@ -52,5 +75,4 @@ with gr.Blocks(title="Guitar Chord Extractor") as demo:
 
 # Launch the app
 if __name__ == "__main__":
-    # server_name="0.0.0.0" is required for Hugging Face
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+    demo.launch(server_name="0.0.0.0", server_port=7860)