argendirast

Sir Radix — Conversational Media Companion Merge

This merge adds a Conversational Media Companion layer to Sir Radix. It re-uses the existing emotional telemetry, memory substrate, relational model, session bridge, and sensorium, and aims them at shared media experiences.

Files in this merge

How to apply

Option A — run the patch script (recommended):

cd /path/to/sir-radix
python merge-media-companion/apply_media_companion_patch.py

Option B — manual copy:

cp merge-media-companion/daemon/media_companion_mixin.py daemon/
cp merge-media-companion/daemon/db_mixin.py daemon/
cp merge-media-companion/daemon/heartbeat_mixin.py daemon/
cp merge-media-companion/daemon_core.py .
cp merge-media-companion/app.py .

Then restart the daemon / Gradio surface.

Design choices

• Sparse by default: samples media state every 60 s; minimum 30 s between voluntary comments.
• Verbosity levels: quiet, normal, chatty, pause-only.
• Mute: operator can mute voluntary comments for N minutes.
• Manual override: if auto-detection fails, set media manually in the UI.
• Opinionated: taste register tracks valence/confidence per artist/song/genre/etc. and updates with every sample.
• Session bridge: each distinct media title starts a media_session; retrospective is written on session end.
• No copyrighted content in APIs: sensing uses local window titles, manual input, and optional metadata. No audio/video is sent to LLM APIs.

Test checklist

1. Sensing
   • Start playing music (Spotify, browser, VLC, etc.)
   • Open Companion tab → does it show the title? If not, set manually and verify.
2. Sparseness
   • Start heartbeat. Does Radix comment at most every ~30–60 s?
   • Switch to pause-only — does it stay silent until you use "Ask"?
3. Memory
   • After a few minutes, query media_reaction_log and taste_register in SQLite.
   • Is the same artist/title accumulating a valence score?
4. Disagreement
   • Tell Radix you love a track he dislikes (or vice versa).
   • In a later session, does he reference the disagreement?
5. Mute / verbosity
   • Hit Mute → verify no voluntary comments until expiry.
   • Set quiet → comments should only happen on valence swing.
6. Debrief
   • Use "Ask Radix about the current media" while media is active.
   • Does he answer with an opinion, no plot summary/spoiler?
7. Session resume
   • Stop daemon, restart, return to same album/movie.
   • Does he pick up the conversation?

Known limitations / next steps

• Audio detection: _audio_is_active() is a placeholder. A future local VAD/loopback energy detector can prevent talking over dialogue/lyrics.
• Player metadata: currently uses active window title heuristics. Add Spotify Web API, MPRIS, or MPV socket for richer metadata.
• Acoustic fingerprinting: not included; keep local if added.
• Game/video frames: not sent to LLM by default. Add local frame captioning if desired.
• Cross-media pattern memory: taste register is table-based. Future work can add vector similarity for "this reminds me of...".

Copyright note

Radix observes your licensed/local media or metadata only. It does not scrape, record, or stream copyrighted content for external reuse. Keep frame/audio capture local and do not send full copyrighted streams through LLM APIs.

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Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

• Try ML Intern: https://smolagents-ml-intern.hf.space
• Source code: https://github.com/huggingface/ml-intern

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = 'sir-radix367/argendirast'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.