checkbox update

app.py

@@ -1,12 +1,76 @@
+# import os
+# import gradio as gr
+# from scipy.io.wavfile import write
+# import subprocess
+# import torch
+
+# from audio_separator import Separator  # Ensure this is correctly implemented
+
+# def inference(audio):
+#     os.makedirs("out", exist_ok=True)
+#     audio_path = 'test.wav'
+#     write(audio_path, audio[0], audio[1])
+#     device = 'cuda' if torch.cuda.is_available() else 'cpu'
+#     if device=='cuda':
+#         use_cuda=True
+#         print(f"Using device: {device}")
+#     else:
+#         use_cuda=False
+#         print(f"Using device: {device}")
+#     try:
+        
+#         # Using subprocess.run for better control
+#         command = f"python3 -m demucs.separate -n htdemucs_6s -d {device} {audio_path} -o out"
+#         process = subprocess.run(command, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+#         print("Demucs script output:", process.stdout.decode())
+#     except subprocess.CalledProcessError as e:
+#         print("Error in Demucs script:", e.stderr.decode())
+#         return None
+
+#     try:
+#         # Separating the stems using your custom separator
+#         separator = Separator("./out/htdemucs_6s/test/vocals.wav", model_name='UVR_MDXNET_KARA_2', use_cuda=use_cuda, output_format='mp3')
+#         primary_stem_path, secondary_stem_path = separator.separate()
+#     except Exception as e:
+#         print("Error in custom separation:", str(e))
+#         return None
+
+#     # Collecting all file paths
+#     files = [f"./out/htdemucs_6s/test/{stem}.wav" for stem in ["vocals", "bass", "drums", "other", "piano", "guitar"]]
+#     files.extend([secondary_stem_path,primary_stem_path ])
+
+#     # Check if files exist
+#     existing_files = [file for file in files if os.path.isfile(file)]
+#     if not existing_files:
+#         print("No files were created.")
+#         return None
+
+#     return existing_files
+
+# # Gradio Interface
+# title = "Source Separation Demo"
+# description = "Music Source Separation in the Waveform Domain. To use it, simply upload your audio."
+# gr.Interface(
+#     inference, 
+#     gr.components.Audio(type="numpy", label="Input"), 
+#     [gr.components.Audio(type="filepath", label=stem) for stem in ["Full Vocals","Bass", "Drums", "Other", "Piano", "Guitar", "Lead Vocals", "Backing Vocals" ]],
+#     title=title,
+#     description=description,
+# ).launch()
+
+
+
+
 import os
 import gradio as gr
 from scipy.io.wavfile import write
 import subprocess
 import torch
 
-from audio_separator import Separator  # Ensure this is correctly implemented
+# Assuming audio_separator is available in your environment
+from audio_separator import Separator  
 
-def inference(audio):
+def inference(audio, vocals, bass, drums, other, piano, guitar, lead_vocals, backing_vocals):
     os.makedirs("out", exist_ok=True)
     audio_path = 'test.wav'
     write(audio_path, audio[0], audio[1])
@@ -16,45 +80,49 @@ def inference(audio):
         print(f"Using device: {device}")
     else:
         use_cuda=False
-        print(f"Using device: {device}")
+
     try:
-        
-        # Using subprocess.run for better control
         command = f"python3 -m demucs.separate -n htdemucs_6s -d {device} {audio_path} -o out"
         process = subprocess.run(command, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
         print("Demucs script output:", process.stdout.decode())
     except subprocess.CalledProcessError as e:
         print("Error in Demucs script:", e.stderr.decode())
-        return None
+        return None, None, None, None, None, None, None, None
 
     try:
-        # Separating the stems using your custom separator
-        separator = Separator("./out/htdemucs_6s/test/vocals.wav", model_name='UVR_MDXNET_KARA_2', use_cuda=use_cuda, output_format='mp3')
+        separator = Separator("./out/htdemucs_6s/test/vocals.wav", model_name='UVR_MDXNET_KARA_2', use_cuda=device=='cuda', output_format='wav')
         primary_stem_path, secondary_stem_path = separator.separate()
     except Exception as e:
         print("Error in custom separation:", str(e))
-        return None
+        return None, None, None, None, None, None, None, None
 
-    # Collecting all file paths
-    files = [f"./out/htdemucs_6s/test/{stem}.wav" for stem in ["vocals", "bass", "drums", "other", "piano", "guitar"]]
-    files.extend([secondary_stem_path,primary_stem_path ])
+    stem_paths = {
+        "vocals": "./out/htdemucs_6s/test/vocals.wav" if vocals else None,
+        "bass": "./out/htdemucs_6s/test/bass.wav" if bass else None,
+        "drums": "./out/htdemucs_6s/test/drums.wav" if drums else None,
+        "other": "./out/htdemucs_6s/test/other.wav" if other else None,
+        "piano": "./out/htdemucs_6s/test/piano.wav" if piano else None,
+        "guitar": "./out/htdemucs_6s/test/guitar.wav" if guitar else None,
+        "lead_vocals": primary_stem_path if lead_vocals else None,
+        "backing_vocals": secondary_stem_path if backing_vocals else None
+    }
 
-    # Check if files exist
-    existing_files = [file for file in files if os.path.isfile(file)]
-    if not existing_files:
-        print("No files were created.")
-        return None
+    return [stem_paths[stem] for stem in stem_paths]
 
-    return existing_files
+# Define checkboxes for each stem
+checkbox_labels = ["Full Vocals", "Bass", "Drums", "Other", "Piano", "Guitar", "Lead Vocals", "Backing Vocals"]
+checkboxes = [gr.components.Checkbox(label=label) for label in checkbox_labels]
 
 # Gradio Interface
 title = "Source Separation Demo"
-description = "Music Source Separation in the Waveform Domain. To use it, simply upload your audio."
-gr.Interface(
+description = "Music Source Separation in the Waveform Domain. Upload your audio to begin."
+iface = gr.Interface(
     inference, 
-    gr.components.Audio(type="numpy", label="Input"), 
-    [gr.components.Audio(type="filepath", label=stem) for stem in ["Full Vocals","Bass", "Drums", "Other", "Piano", "Guitar", "Lead Vocals", "Backing Vocals" ]],
+    [gr.components.Audio(type="numpy", label="Input")] + checkboxes,
+    [gr.components.Audio(type="filepath", label=label) for label in checkbox_labels],
     title=title,
     description=description,
-).launch()
+  
+)
 
+iface.launch(debug=True)