Update app.py

app.py

@@ -1,64 +1,57 @@
 import gradio as gr
-import tempfile
 import os
-import requests
-
 from basic_pitch.inference import predict
-from basic_pitch import ICASSP_2022_MODEL_PATH
-import pretty_midi
 
-def audio_to_midi(audio_url: str, instrument: str):
+def convert_audio_to_midi(audio_filepath):
+    """
+    Takes an audio file path, runs Basic Pitch, and saves/returns a MIDI file.
+    """
+    if audio_filepath is None:
+        return None
+        
     try:
-        with tempfile.TemporaryDirectory() as tmp:
-            audio_path = os.path.join(tmp, "input.wav")
-
-            # Download audio
-            r = requests.get(audio_url, timeout=30)
-            r.raise_for_status()
-            with open(audio_path, "wb") as f:
-                f.write(r.content)
-
-            # Run Basic Pitch
-            _, midi_data, _ = predict(
-                audio_path,
-                model_or_model_path=ICASSP_2022_MODEL_PATH
-            )
-
-            notes = []
-            for inst in midi_data.instruments:
-                for n in inst.notes:
-                    notes.append({
-                        "pitch": int(n.pitch),
-                        "startTime": round(n.start, 4),
-                        "duration": round(n.end - n.start, 4),
-                        "velocity": int(n.velocity)
-                    })
-
-            return {
-                "instrument": instrument,
-                "notes": notes
-            }
-
+        # Run the basic pitch model on the audio file
+        # predict() returns model_output, midi_data, and note_events
+        _, midi_data, _ = predict(audio_filepath)
+        
+        # Define a temporary output path for the generated MIDI file
+        output_midi_path = "output.mid"
+        
+        # Save the MIDI data to the file
+        midi_data.write(output_midi_path)
+        
+        return output_midi_path
+        
     except Exception as e:
-        return {
-            "error": str(e),
-            "notes": []
-        }
-
-with gr.Blocks() as demo:
-    gr.Markdown("## Audio → MIDI API")
-    audio_input = gr.Textbox(label="Audio URL")
-    instr_input = gr.Textbox(label="Instrument")
-    output = gr.JSON()
+        print(f"Error during conversion: {e}")
+        return None
+
+# Build the Gradio Interface
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # 🎵 Basic Pitch: Audio to MIDI Converter
+        Upload an audio file (vocals, guitar, piano, etc.) and convert it into a MIDI file using Spotify's open-source **Basic Pitch** machine learning model.
+        """
+    )
     
-    btn = gr.Button("Convert")
-    btn.click(fn=audio_to_midi, inputs=[audio_input, instr_input], outputs=output)
+    with gr.Row():
+        with gr.Column():
+            # User uploads audio; Gradio passes the file path to our function
+            audio_input = gr.Audio(type="filepath", label="Upload Audio File (WAV, MP3, etc.)")
+            convert_btn = gr.Button("Convert to MIDI", variant="primary")
+            
+        with gr.Column():
+            # The output will be a downloadable file
+            midi_output = gr.File(label="Download Generated MIDI")
+
+    # Link the button click to the conversion function
+    convert_btn.click(
+        fn=convert_audio_to_midi,
+        inputs=audio_input,
+        outputs=midi_output
+    )
 
+# Launch the app
 if __name__ == "__main__":
-    demo.launch(
-        ssr_mode=False,
-        theme=gr.themes.Soft(
-            primary_hue="indigo",
-            secondary_hue="blue",
-        ),
-    )
+    demo.launch()