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68b18cf 05f68f1 68b18cf 05f68f1 68b18cf 05f68f1 68b18cf 05f68f1 68b18cf 05f68f1 68b18cf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 | """
Compressor — collapses all sensory streams into one compact state.
vision + motion + audio + diarization + retrieval → one compact state
"""
import json
import time
from .stream_state import SessionState, sha256_text
def compress_state(state: SessionState) -> dict:
"""Compress rolling sensory state into a compact observer input.
This is the shadow-dimensionality reduction step:
many sensory streams → one dense state vector.
"""
frames = list(state.frames)
recent_frames = frames[-4:] if frames else []
# Visual compression
visual = {
"frame_count": len(frames),
"avg_entropy": round(sum(f.entropy for f in recent_frames) / max(1, len(recent_frames)), 4),
"avg_motion": round(sum(f.motion_score for f in recent_frames) / max(1, len(recent_frames)), 4),
"last_frame_hash": state.last_frame_hash,
"frame_hashes": [f.sha256 for f in recent_frames],
}
# Audio compression — speaker-separated evidence ledger with full transcripts
audio = {
"speakers": {},
"chunk_count": len(state.audio_chunks),
"recent_transcripts": [],
}
for speaker, info in state.speakers.items():
audio["speakers"][speaker] = {
"transcript": info["transcript"][-500:],
"confidence": info["confidence"],
"chunk_count": info["chunk_count"],
}
# Include last 5 audio chunks as raw evidence
recent_chunks = list(state.audio_chunks)[-5:]
for c in recent_chunks:
audio["recent_transcripts"].append({
"speaker": c.speaker,
"text": c.transcript,
"ts": round(c.ts, 2),
})
# Intent — inferred by LLM, not typed by user
intent = {
"mode": state.mode,
"note": "Intent must be inferred from camera + audio evidence, not from typed instructions.",
}
# Compressed state
compact = {
"timestamp": time.time(),
"session_id": state.session_id,
"visual": visual,
"audio": audio,
"intent": intent,
"motion_score": state.motion_score,
"novelty_score": state.novelty_score,
"state_hash": "",
}
compact["state_hash"] = sha256_text(json.dumps(compact, sort_keys=True, default=str))
return compact
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