create app

app.py

@@ -0,0 +1,91 @@
+import streamlit as st
+from qdrant_client import QdrantClient
+from transformers import pipeline
+from audiocraft.models import MusicGen
+import os
+# import baseten
+
+st.title("Music Recommendation App")
+st.subheader("A :red[Generative AI]-to-Real Music Approach")
+
+st.markdown("""
+The purpose of this app is to help creative people explore the possibilities of Generative AI in the music
+domain, while comparing their creations to music made by people with all sorts of instruments.  
+
+There are several moving parts to this app and the most important ones are `transformers`, `audiocraft`, and 
+Qdrant for our vector database.
+""")
+
+client     = QdrantClient(
+    "https://394294d5-30bb-4958-ad1a-15a3561edce5.us-east-1-0.aws.cloud.qdrant.io:6333", 
+    api_key=os.environ['QDRANT_API_KEY'],
+)
+
+# classifier = baseten.deployed_model_id('20awxxq')
+classifier = pipeline("audio-classification", model="ramonpzg/wav2musicgenre")#.to(device)
+model      = MusicGen.get_pretrained('small')
+
+val1 = st.slider("How many seconds?", 5.0, 30.0, value=5.0, step=0.5)
+
+model.set_generation_params(
+    use_sampling=True,
+    top_k=250,
+    duration=val1
+)
+
+music_prompt = st.text_input(
+    label="Music Prompt",
+    value="Fast-paced bachata in the style of Romeo Santos."
+)
+
+if st.button("Generate Some Music!"):
+    with st.spinner("Wait for it..."):
+        output = model.generate(descriptions=[music_prompt],progress=True)[0, 0, :].cpu().numpy()
+        st.success("Done! :)")
+
+    st.audio(output, sample_rate=32000)
+
+    genres = classifier(output)
+
+    if genres:
+        st.markdown("## Best Prediction")
+        col1, col2 = st.columns(2, gap="small")
+        col1.subheader(genres[0]['label'])
+        col2.metric(label="Score", value=f"{genres[0]['score']*100:.2f}%")
+
+        st.markdown("### Other Predictions")
+        col3, col4 = st.columns(2, gap="small")
+        for idx, genre in enumerate(genres[1:]):
+            if idx % 2 == 0:
+                col3.metric(label=genre['label'], value=f"{genre['score']*100:.2f}%")
+            else:
+                col4.metric(label=genre['label'], value=f"{genre['score']*100:.2f}%")
+
+    features = classifier.feature_extractor(output)
+
+    with torch.no_grad():
+        vectr = classifier.model(**features, output_hidden_states=True).hidden_states[-1].mean(dim=1)[0]
+
+    # vectr = embedding[0]
+
+    results = client.search(
+        collection_name="music_vectors",
+        query_vector=vectr,
+        limit=10
+    )
+
+    st.markdown("## Real Recommendations")
+
+    col5, col6 = st.columns(2)
+
+    for idx, result in enumerate(results):
+        if idx % 2 == 0:
+            col5.header(f"Genre: {result.payload['genre']}")
+            col5.markdown(f"### Artist: {result.payload['artist']}")
+            col5.markdown(f"#### Song name: {result.payload['name']}")
+            col5.audio(result.payload["urls"])
+        else:
+            col6.header(f"Genre: {result.payload['genre']}")
+            col6.markdown(f"### Artist: {result.payload['artist']}")
+            col6.markdown(f"#### Song name: {result.payload['name']}")
+            col6.audio(result.payload["urls"])