Update app.py

app.py

@@ -1,63 +1,59 @@
-import streamlit as st
-import numpy as np
-import torch
-import transformers
-from packaging.version import parse
-import sys
-import io
-import importlib.metadata as importlib_metadata
-import soundfile as sf
-import importlib.metadata as importlib_metadata
-
-loading_kwargs = {}
-if parse(importlib_metadata.version("transformers")) >= parse("4.40.0"):
-    loading_kwargs["attn_implementation"] = "eager"
-
-def generate(prompt):
-    model = transformers.MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small", torchscript=True, return_dict=False, **loading_kwargs)
-    sample_length = 8  
-    n_tokens = sample_length * model.config.audio_encoder.frame_rate + 3
-    sampling_rate = model.config.audio_encoder.sampling_rate
-    processor = transformers.AutoProcessor.from_pretrained("facebook/musicgen-small")
-    inputs = processor(
-        text=[
-            prompt,
-        ],
-        padding=True,
-        return_tensors="pt",
-    )
-    audio_values = model.generate(**inputs, do_sample=True, guidance_scale=3, max_new_tokens=n_tokens)
-    waveform = audio_values[0].cpu().squeeze() * 2**15
-    audio_buffer = io.BytesIO()
-    sf.write(audio_buffer, waveform.numpy().astype(np.int16), sampling_rate, format='WAV')
-    audio_buffer.seek(0)
-    return audio_buffer
-
-st.title("Music Generator")
-
-text_prompt = st.text_input("Text Prompt", "")
-
-examples = [
-    "80s pop track with bassy drums and synth",
-    "Earthy tones, environmentally conscious, ukulele-infused, harmonic, breezy, easygoing, organic instrumentation, gentle grooves",
-    "90s rock song with loud guitars and heavy drums",
-    "Heartful EDM with beautiful synths and chords",
-]
-
-st.sidebar.title("Examples")
-for example in examples:
-    if st.sidebar.button(example):
-        text_prompt = example
-        st.experimental_rerun()
-
-if st.button("Generate Audio"):
-    if text_prompt:
-        with st.spinner("Generating audio..."):
-            audio_output = generate(text_prompt)
-            st.audio(audio_output, format='audio/wav')
-    else:
-        st.warning("Please enter a text prompt.")
-
-# Debugging
-if st.checkbox("Show debug info"):
+import streamlit as st
+import numpy as np
+import torch
+import transformers
+from packaging.version import parse
+import sys
+import io
+import importlib.metadata as importlib_metadata
+import soundfile as sf
+import importlib.metadata as importlib_metadata
+
+loading_kwargs = {}
+if parse(importlib_metadata.version("transformers")) >= parse("4.40.0"):
+    loading_kwargs["attn_implementation"] = "eager"
+
+def generate(prompt):
+    model = transformers.MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small", torchscript=True, return_dict=False, **loading_kwargs)
+    sample_length = 8  
+    n_tokens = sample_length * model.config.audio_encoder.frame_rate + 3
+    sampling_rate = model.config.audio_encoder.sampling_rate
+    processor = transformers.AutoProcessor.from_pretrained("facebook/musicgen-small")
+    inputs = processor(
+        text=[
+            prompt,
+        ],
+        padding=True,
+        return_tensors="pt",
+    )
+    audio_values = model.generate(**inputs, do_sample=True, guidance_scale=3, max_new_tokens=n_tokens)
+    waveform = audio_values[0].cpu().squeeze() * 2**15
+    audio_buffer = io.BytesIO()
+    sf.write(audio_buffer, waveform.numpy().astype(np.int16), sampling_rate, format='WAV')
+    audio_buffer.seek(0)
+    return audio_buffer
+
+st.title("Music Generator")
+
+text_prompt = st.text_input("Text Prompt", "")
+
+examples = [
+    "80s pop track with bassy drums and synth",
+    "Earthy tones, environmentally conscious, ukulele-infused, harmonic, breezy, easygoing, organic instrumentation, gentle grooves",
+    "90s rock song with loud guitars and heavy drums",
+    "Heartful EDM with beautiful synths and chords",
+]
+
+st.sidebar.title("Examples")
+selected_example = st.sidebar.radio("Select an example", examples)
+
+if st.button("Generate Audio"):
+    if selected_example or text_prompt:
+        with st.spinner("Generating audio..."):
+            audio_output = generate(selected_example)
+            st.audio(audio_output, format='audio/wav')
+    else:
+        st.warning("Please select or enter a text prompt.")
+
+if st.checkbox("Show debug info"):
     st.write("Text Prompt:", text_prompt)