Create app.py

app.py

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+import os
+import gradio as gr
+import tempfile
+import shutil
+from audio_separator.separator import Separator
+import logging
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+def separate_audio(input_file, model_name, progress=gr.Progress()):
+    if input_file is None:
+        return None, None
+    
+    # 1. Use a temporary directory for outputs to keep HF Spaces clean
+    # HF Spaces has a writable /tmp directory which is ideal for ephemeral files
+    temp_output_dir = tempfile.mkdtemp()
+    
+    try:
+        progress(0, desc="Initializing Separator...")
+        # Initialize separator pointing to the temp directory
+        separator = Separator(output_dir=temp_output_dir)
+        
+        progress(0.2, desc=f"Loading model: {model_name}...")
+        separator.load_model(model_filename=model_name)
+        
+        progress(0.4, desc="Separating audio (this may take a minute)...")
+        # Run separation
+        output_files = separator.separate(input_file)
+        
+        vocals = None
+        instrumental = None
+        
+        progress(0.9, desc="Finalizing files...")
+        for file in output_files:
+            # Construct full path from the temp directory
+            full_path = os.path.join(temp_output_dir, file)
+            if "Vocals" in file:
+                vocals = full_path
+            elif "Instrumental" in file:
+                instrumental = full_path
+        
+        return instrumental, vocals
+
+    except Exception as e:
+        logger.error(f"Separation failed: {e}")
+        return None, None
+
+# --- Gradio Interface ---
+
+# Using a custom theme and better layout
+with gr.Blocks(theme=gr.themes.Soft(), title="AI Audio Separator") as demo:
+    gr.Markdown("""
+    # 🎵 AI Audio Separator
+    Upload a song to split it into **Vocals** and **Instrumental** using state-of-the-art AI models.
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            # Input with waveform visualization enabled
+            input_audio = gr.Audio(
+                label="Upload Audio", 
+                type="filepath", 
+                waveform_options=gr.WaveformOptions(
+                    waveform_color="#9ca3af",
+                    waveform_progress_color="#3b82f6",
+                    show_controls=True,
+                )
+            )
+            
+            model_dropdown = gr.Dropdown(
+                choices=[
+                    "model_bs_roformer_ep_317_sdr_12.9755.ckpt", 
+                    "UVR_MDXNET_KARA_2.onnx",
+                    "UVR-MDX-NET-Inst_HQ_4.onnx"
+                ],
+                value="model_bs_roformer_ep_317_sdr_12.9755.ckpt",
+                label="Select AI Model"
+            )
+            
+            separate_btn = gr.Button("🚀 Start Separation", variant="primary")
+            
+        with gr.Column(scale=1):
+            # Output components with waveforms for a better listening experience
+            output_inst = gr.Audio(
+                label="Instrumental (Output)", 
+                interactive=False,
+                waveform_options=gr.WaveformOptions(waveform_color="#f59e0b")
+            )
+            output_vocals = gr.Audio(
+                label="Vocals (Output)", 
+                interactive=False,
+                waveform_options=gr.WaveformOptions(waveform_color="#10b981")
+            )
+
+    # Click event with progress bar integration
+    separate_btn.click(
+        fn=separate_audio,
+        inputs=[input_audio, model_dropdown],
+        outputs=[output_inst, output_vocals]
+    )
+
+    gr.Markdown("---")
+    gr.Markdown("Note: High-quality models may take longer to process on CPU hardware.")
+
+if __name__ == "__main__":
+    demo.launch()