updated image loading

app.py

@@ -60,6 +60,78 @@
 
 
 
+# import os
+# import gradio as gr
+# from scipy.io.wavfile import write
+# import subprocess
+# import torch
+
+# # Assuming audio_separator is available in your environment
+# from audio_separator import Separator  
+
+# 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])
+#     device = 'cuda' if torch.cuda.is_available() else 'cpu'
+#     print(f"Using device: {device}")
+#     if device=='cuda':
+#         use_cuda=True
+#         print(f"Using device: {device}")
+#     else:
+#         use_cuda=False
+#         print(f"Using device: {device}")
+
+#     try:
+#         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 [gr.Audio(visible=False)] * 8
+
+#     try:
+#         separator = Separator("./out/htdemucs_6s/test/vocals.wav", model_name='UVR_MDXNET_KARA_2', use_cuda=device==use_cuda, output_format='wav')
+#         primary_stem_path, secondary_stem_path = separator.separate()
+#     except Exception as e:
+#         print("Error in custom separation:", str(e))
+#         return [gr.Audio(visible=False)] * 8
+
+#     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
+#     }
+
+#     return [gr.Audio(stem_paths[stem], visible=bool(stem_paths[stem])) for stem in stem_paths]
+
+# # 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. Upload your audio to begin."
+# iface = gr.Interface(
+#     inference, 
+#     [gr.components.Audio(type="numpy", label="Input")] + checkboxes,
+#     [gr.Audio(label=label, visible=False) for label in checkbox_labels],
+#     title=title,
+#     description=description,
+    
+# )
+
+# iface.launch()
+
+
+
+
+
 import os
 import gradio as gr
 from scipy.io.wavfile import write
@@ -70,17 +142,15 @@ import torch
 from audio_separator import Separator  
 
 def inference(audio, vocals, bass, drums, other, piano, guitar, lead_vocals, backing_vocals):
+    # Initially, show the loading GIF
+    loading_gif_path = "7RwF.gif"  
+    transparent_img_path = "images.png" 
+
     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'
     print(f"Using device: {device}")
-    if device=='cuda':
-        use_cuda=True
-        print(f"Using device: {device}")
-    else:
-        use_cuda=False
-        print(f"Using device: {device}")
 
     try:
         command = f"python3 -m demucs.separate -n htdemucs_6s -d {device} {audio_path} -o out"
@@ -88,14 +158,14 @@ def inference(audio, vocals, bass, drums, other, piano, guitar, lead_vocals, bac
         print("Demucs script output:", process.stdout.decode())
     except subprocess.CalledProcessError as e:
         print("Error in Demucs script:", e.stderr.decode())
-        return [gr.Audio(visible=False)] * 8
+        return [gr.Audio(visible=False)] * 8 + [loading_gif_path]
 
     try:
-        separator = Separator("./out/htdemucs_6s/test/vocals.wav", model_name='UVR_MDXNET_KARA_2', use_cuda=device==use_cuda, output_format='wav')
+        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 [gr.Audio(visible=False)] * 8
+        return [gr.Audio(visible=False)] * 8 + [loading_gif_path]
 
     stem_paths = {
         "vocals": "./out/htdemucs_6s/test/vocals.wav" if vocals else None,
@@ -108,7 +178,8 @@ def inference(audio, vocals, bass, drums, other, piano, guitar, lead_vocals, bac
         "backing_vocals": secondary_stem_path if backing_vocals else None
     }
 
-    return [gr.Audio(stem_paths[stem], visible=bool(stem_paths[stem])) for stem in stem_paths]
+    # Once processing is done, hide the GIF by returning a transparent image
+    return [gr.Audio(stem_paths[stem], visible=bool(stem_paths[stem])) for stem in stem_paths] + [transparent_img_path]
 
 # Define checkboxes for each stem
 checkbox_labels = ["Full Vocals", "Bass", "Drums", "Other", "Piano", "Guitar", "Lead Vocals", "Backing Vocals"]
@@ -120,11 +191,9 @@ description = "Music Source Separation in the Waveform Domain. Upload your audio
 iface = gr.Interface(
     inference, 
     [gr.components.Audio(type="numpy", label="Input")] + checkboxes,
-    [gr.Audio(label=label, visible=False) for label in checkbox_labels],
+    [gr.Audio(label=label, visible=False) for label in checkbox_labels] + [gr.Image()],
     title=title,
     description=description,
-    
 )
 
 iface.launch()
-