ok herewego

app.py

@@ -3,10 +3,24 @@ import spaces
 import torchaudio
 from audiocraft.models import MusicGen
 from audiocraft.data.audio import audio_write
+import tempfile
+import os
+import logging
+import torch
+from pydub import AudioSegment
+import io
 import random
 
+# Check if CUDA is available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+def preprocess_audio(waveform):
+    waveform_np = waveform.cpu().squeeze().numpy()
+    return torch.from_numpy(waveform_np).unsqueeze(0).to(device)
+
+# Test with a wrapper function
 @spaces.GPU
-def generate_drum_sample():
+def _generate_drum_sample_internal():
     model = MusicGen.get_pretrained('pharoAIsanders420/micro-musicgen-jungle')
     model.set_generation_params(duration=10)
     wav = model.generate_unconditional(1).squeeze(0)
@@ -18,21 +32,257 @@ def generate_drum_sample():
 
     return filename_with_extension
 
-# Use gr.Interface instead of gr.Blocks - this bypasses the click handler issue
-demo = gr.Interface(
-    fn=generate_drum_sample,
-    inputs=None,  # No inputs for generation function
-    outputs=gr.Audio(label="Generated Drum Sample", type="filepath"),
-    title="🎰 Micro Slot Machine",
-    description="Generate jungle drums using Aaron's micro-musicgen model",
-    article="""
-    ## musicgen_micro
-    musicgen micro is an experimental series of models by aaron abebe. they are incredibly fast, and extra insane.
-    [Aaron's GitHub](https://github.com/aaronabebe/) | [Model on HuggingFace](https://huggingface.co/pharoAIsanders420/micro-musicgen-jungle)
-    """,
-    examples=[],  # You can add examples here if needed
-    cache_examples=False
-)
+# Regular function wrapper (no @spaces.GPU on this one)
+def generate_drum_sample():
+    return _generate_drum_sample_internal()
+
+
+
+# @spaces.GPU
+# def continue_drum_sample(existing_audio_path):
+#     if existing_audio_path is None:
+#         return None
+        
+#     existing_audio, sr = torchaudio.load(existing_audio_path)
+#     existing_audio = existing_audio.to(device)
+    
+#     prompt_duration = 2
+#     output_duration = 10
+
+#     num_samples = int(prompt_duration * sr)
+#     if existing_audio.shape[1] < num_samples:
+#         raise ValueError("The existing audio is too short for the specified prompt duration.")
+
+#     start_sample = existing_audio.shape[1] - num_samples
+#     prompt_waveform = existing_audio[..., start_sample:]
+
+#     model = MusicGen.get_pretrained('pharoAIsanders420/micro-musicgen-jungle')
+#     model.set_generation_params(duration=output_duration)
+
+#     output = model.generate_continuation(prompt_waveform, prompt_sample_rate=sr, progress=True)
+#     output = output.to(device)
+
+#     if output.dim() == 3:
+#         output = output.squeeze(0)
+
+#     if output.dim() == 1:
+#         output = output.unsqueeze(0)
+
+#     combined_audio = torch.cat((existing_audio, output), dim=1)
+#     combined_audio = combined_audio.cpu()
+
+#     combined_file_path = f'./continued_jungle_{random.randint(1000, 9999)}.wav'
+#     torchaudio.save(combined_file_path, combined_audio, sr)
+
+#     return combined_file_path
+
+# @spaces.GPU
+# def generate_music(wav_filename, prompt_duration, musicgen_model, output_duration):
+#     if wav_filename is None:
+#         return None
+        
+#     song, sr = torchaudio.load(wav_filename)
+#     song = song.to(device)
+
+#     model_name = musicgen_model.split(" ")[0]
+#     model_continue = MusicGen.get_pretrained(model_name)
+
+#     model_continue.set_generation_params(
+#         use_sampling=True,
+#         top_k=250,
+#         top_p=0.0,
+#         temperature=1.0,
+#         duration=output_duration,
+#         cfg_coef=3
+#     )
+
+#     prompt_waveform = song[..., :int(prompt_duration * sr)]
+#     prompt_waveform = preprocess_audio(prompt_waveform)
+    
+#     output = model_continue.generate_continuation(prompt_waveform, prompt_sample_rate=sr, progress=True)
+#     output = output.cpu()
+    
+#     if len(output.size()) > 2:
+#         output = output.squeeze()
+    
+#     filename_without_extension = f'continued_music'
+#     filename_with_extension = f'{filename_without_extension}.wav'
+#     audio_write(filename_without_extension, output, model_continue.sample_rate, strategy="loudness", loudness_compressor=True)
+
+#     return filename_with_extension
+
+# @spaces.GPU
+# def continue_music(input_audio_path, prompt_duration, musicgen_model, output_duration):
+#     if input_audio_path is None:
+#         return None
+        
+#     song, sr = torchaudio.load(input_audio_path)
+#     song = song.to(device)
+
+#     model_continue = MusicGen.get_pretrained(musicgen_model.split(" ")[0])
+#     model_continue.set_generation_params(
+#         use_sampling=True,
+#         top_k=250,
+#         top_p=0.0,
+#         temperature=1.0,
+#         duration=output_duration,
+#         cfg_coef=3
+#     )
+
+#     original_audio = AudioSegment.from_mp3(input_audio_path)
+#     current_audio = original_audio
+
+#     file_paths_for_cleanup = []
+
+#     for i in range(1):
+#         num_samples = int(prompt_duration * sr)
+#         if current_audio.duration_seconds * 1000 < prompt_duration * 1000:
+#             raise ValueError("The prompt_duration is longer than the current audio length.")
+
+#         start_time = current_audio.duration_seconds * 1000 - prompt_duration * 1000
+#         prompt_audio = current_audio[start_time:]
+
+#         prompt_bytes = prompt_audio.export(format="wav").read()
+#         prompt_waveform, _ = torchaudio.load(io.BytesIO(prompt_bytes))
+#         prompt_waveform = prompt_waveform.to(device)
+
+#         prompt_waveform = preprocess_audio(prompt_waveform)
+
+#         output = model_continue.generate_continuation(prompt_waveform, prompt_sample_rate=sr, progress=True)
+#         output = output.cpu()
+
+#         if len(output.size()) > 2:
+#             output = output.squeeze()
+
+#         filename_without_extension = f'continue_{i}'
+#         filename_with_extension = f'{filename_without_extension}.wav'
+#         correct_filename_extension = f'{filename_without_extension}.wav.wav'
+
+#         audio_write(filename_with_extension, output, model_continue.sample_rate, strategy="loudness", loudness_compressor=True)
+#         generated_audio_segment = AudioSegment.from_wav(correct_filename_extension)
+
+#         current_audio = current_audio[:start_time] + generated_audio_segment
+
+#         file_paths_for_cleanup.append(correct_filename_extension)
+
+#     combined_audio_filename = f"combined_audio_{random.randint(1, 10000)}.mp3"
+#     current_audio.export(combined_audio_filename, format="mp3")
+
+#     for file_path in file_paths_for_cleanup:
+#         os.remove(file_path)
+
+#     return combined_audio_filename
+
+# Define the expandable sections
+musicgen_micro_blurb = """
+## musicgen_micro
+musicgen micro is an experimental series of models by aaron abebe. they are incredibly fast, and extra insane. this one does goated jungle drums. we're very excited about these.
+[<img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" alt="GitHub" width="20" style="vertical-align:middle"> aaron's github](https://github.com/aaronabebe/)
+[<img src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" alt="Hugging Face" width="20" style="vertical-align:middle"> musicgen-micro on huggingface](https://huggingface.co/pharoAIsanders420/micro-musicgen-jungle)
+"""
+
+musicgen_blurb = """
+## musicgen
+musicgen is a transformer-based music model that generates audio. It can also do something called a continuation, which was initially meant to extend musicgen outputs beyond 30 seconds. it can be used with any input audio to produce surprising results.
+[<img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" alt="GitHub" width="20" style="vertical-align:middle"> audiocraft github](https://github.com/facebookresearch/audiocraft)
+visit https://thecollabagepatch.com/infinitepolo.mp3 or https://thecollabagepatch.com/audiocraft.mp3 to hear continuations in action.
+see also https://youtube.com/@thecollabagepatch 
+"""
+
+finetunes_blurb = """
+## fine-tuned models
+the fine-tunes hosted on the huggingface hub are provided collectively by the musicgen discord community. thanks to vanya, mj, hoenn, septicDNB and of course, lyra.
+[<img src="https://cdn.iconscout.com/icon/free/png-256/discord-3691244-3073764.png" alt="Discord" width="20" style="vertical-align:middle"> musicgen discord](https://discord.gg/93kX8rGZ)
+[<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" style="vertical-align:middle"> fine-tuning colab notebook by lyra](https://colab.research.google.com/drive/13tbcC3A42KlaUZ21qvUXd25SFLu8WIvb)
+"""
+
+fine_tunes_info = """
+## thepatch/vanya_ai_dnb_0.1
+thepatch/vanya_ai_dnb_0.1 was trained by vanya. [vanya's Twitter](https://twitter.com/@veryVANYA) 🔗 - it treats almost all input audio as the beginning of a buildup to a dnb drop (can do downtempo well)
+
+## thepatch/bleeps-medium
+thepatch/bleeps-medium was trained by kevin and lyra [lyra's Twitter](https://twitter.com/@_lyraaaa_) 🔗 - it is a medium model. it's more melodic and ambient sometimes than vanya's, but there's a 50/50 chance it gets real heavy with the edm vibes. It can be amazing at turning your chords into pads, and is a good percussionist.
+
+## thepatch/budots_remix
+thepatch/budots_remix was trained by MJ BERSABEph. budots is a dope niche genre from the philippines apparently. this one will often do fascinating, demonic, kinds of vocal chopping. warning: it tends to speed up and slow down tempo, which makes it hard to use in a daw.
+
+## thepatch/hoenn_lofi
+thepatch/hoenn_lofi is a large fine-tune by hoenn. [hoenn's Twitter](https://twitter.com/@eschatolocation) 🔗 - this model is a large boi, and it shows. even tho it is trained to do lo-fi, its ability to run with your melodies and not ruin them is unparalleled among the fine-tunes so far.
+
+## thepatch/PhonkV2
+thepatch/PhonkV2 was trained by MJ BERSABEph. there are multiple versions in the discord.
+
+## foureyednymph/musicgen-sza-sos-small
+foureyednymph/musicgen-sza-sos-small was just trained by foureyednymph. We're all about to find out if it does continuations well.
+"""
+
+# Create the Gradio interface
+with gr.Blocks() as iface:
+    gr.Markdown("# the-micro-slot-machine")
+    gr.Markdown("two ai's jamming. warning: outputs will be very strange, likely stupid, and possibly rad.")
+    gr.Markdown("this is an even weirder slot machine than the other one. on the left, you get to generate some state of the art lo-fi jungle drums at incredible speed thanks to aaron's new class of model, and if you want you can have it continue its own output. Then, you can either press the generate_music button to use the first 5 seconds as a prompt, or you can re-upload the audio into the continue_music section to have a fine-tune continue from the end of the jungle drum output, however long and insane it is. think of this as a very weird relay race and you're winning.")
+
+    with gr.Accordion("more info", open=False):
+        gr.Markdown(musicgen_micro_blurb)
+        gr.Markdown(musicgen_blurb)
+        gr.Markdown(finetunes_blurb)
+
+    with gr.Accordion("fine-tunes info", open=False):
+        gr.Markdown(fine_tunes_info)
+
+    with gr.Row():
+        with gr.Column():
+            generate_button = gr.Button("Generate Drum Sample")
+            drum_audio = gr.Audio(
+                label="Generated Drum Sample", 
+                type="filepath", 
+                interactive=True, 
+                show_download_button=True
+            )
+            continue_drum_sample_button = gr.Button("Continue Drum Sample")
+
+        with gr.Column():
+            prompt_duration = gr.Dropdown(
+                label="Prompt Duration (seconds)", 
+                choices=list(range(1, 11)), 
+                value=5
+            )
+            output_duration = gr.Slider(
+                label="Output Duration (seconds)", 
+                minimum=10, 
+                maximum=30, 
+                step=1, 
+                value=20
+            )
+            musicgen_model = gr.Dropdown(
+                label="MusicGen Model", 
+                choices=[
+                    "thepatch/vanya_ai_dnb_0.1 (small)",
+                    "thepatch/budots_remix (small)",
+                    "thepatch/PhonkV2 (small)",
+                    "thepatch/bleeps-medium (medium)",
+                    "thepatch/hoenn_lofi (large)",
+                    "foureyednymph/musicgen-sza-sos-small (small)"
+                ], 
+                value="thepatch/vanya_ai_dnb_0.1 (small)"
+            )
+            generate_music_button = gr.Button("Generate Music")
+            output_audio = gr.Audio(label="Generated Music", type="filepath")
+            continue_button = gr.Button("Continue Generating Music")
+            continue_output_audio = gr.Audio(label="Continued Music Output", type="filepath")
+
+    # Hidden component to provide dummy input
+    hidden_trigger = gr.Textbox(value="generate", visible=False)
+
+    # Fixed click handlers - use hidden input for generate_drum_sample
+    # Normal click connection
+    generate_button.click(
+        lambda: generate_drum_sample(),
+        outputs=[drum_audio]
+    )
+    # continue_drum_sample_button.click(continue_drum_sample, inputs=[drum_audio], outputs=[drum_audio])
+    # generate_music_button.click(generate_music, inputs=[drum_audio, prompt_duration, musicgen_model, output_duration], outputs=[output_audio])
+    # continue_button.click(continue_music, inputs=[output_audio, prompt_duration, musicgen_model, output_duration], outputs=continue_output_audio)
 
 if __name__ == "__main__":
-    demo.launch()
+    iface.launch()