Add music recommender domain demo

README.md

@@ -23,14 +23,52 @@ If you plan to run the app in a conda or virtual environment, make sure to set u
     pip install git+https://github.com/aolabsai/ao_arch git+https://github.com/aolabsai/ao_core
     ```
 
-3. Run the application with the following command:
+3. Run the YouTube recommender application with the following command:
 
     ```bash
-    streamlit run recommender.py
+    streamlit run main.py
     ```
 
 4. Once running, the app will be accessible at `localhost:8501`.
 
+### Music Domain Demo
+
+This fork adds a second recommender domain that applies the same AO Labs
+real-time training loop to music discovery. Instead of fetching YouTube videos,
+the demo recommends tracks from a local sample catalog using binary features for
+genre, tempo, energy, release recency, vocal/instrumental format, and listening
+context.
+
+Run the music recommender with:
+
+```bash
+streamlit run music_recommender.py
+```
+
+The music demo can run in two modes:
+
+- With `ao_core` and `ao_arch` installed, it creates a live AO Agent from
+  `arch__MusicRecommender.py` and trains it from the "Recommend more" and
+  "Stop recommending" feedback buttons.
+- Without those AO packages installed, it uses a deterministic local fallback so
+  the domain mapping, interface, and tests can still be exercised.
+
+The music-domain mapping lives in `music_domain.py`. Its unit tests can be run
+with:
+
+```bash
+python -m unittest discover -s tests
+```
+
+The sidebar can also load a custom CSV catalog, which makes the demo reusable
+for a real music dataset instead of only the bundled sample tracks. The CSV
+must include:
+
+```csv
+title,artist,genres,tempo_bpm,energy,release_year,vocal,summary
+City Lights,Example Artist,electronic;pop,118,8,2024,no,Bright synth pulse
+```
+
 
 ### Docker Installation
 
@@ -60,5 +98,3 @@ The recommender system works by loading a set of random video links. Once the us
 
 Fork the repository, make your changes, and submit a pull request for review. 
 
-
-

arch__MusicRecommender.py

@@ -0,0 +1,17 @@
+# -*- coding: utf-8 -*-
+"""
+AO architecture for the music-domain recommender demo.
+"""
+
+import ao_arch as ar
+
+
+description = "Music Recommender System"
+
+# genre + tempo + energy + recency + vocal/instrumental + listening context
+arch_i = [3, 2, 2, 1, 1, 2]
+arch_z = [10]
+arch_c = []
+connector_function = "full_conn"
+
+arch = ar.Arch(arch_i, arch_z, arch_c, connector_function, description)