"""
Extract Grandma's facts from entity/facts.json, embed with MiniLM, save as substrate JSON.
"""
import json, sys
from sentence_transformers import SentenceTransformer

print("Loading MiniLM-L6-v2...")
model = SentenceTransformer('all-MiniLM-L6-v2')

print("Loading facts...")
with open(r'C:\Users\Forgemind\Desktop\Grandmas Hearth\entity\facts.json', 'r', encoding='utf-8') as f:
    data = json.load(f)

facts = []

# Identity
ident = data.get('identity', {})
for k, v in ident.items():
    if k.startswith('_'): continue
    facts.append(f"Grandma's {k}: {v}")

# Spine
for s in data.get('spine', []):
    facts.append(f"Spine rule — {s['rule']}: {s['meaning']}")

# Voice
voice = data.get('voice', {})
if voice.get('register'):
    facts.append(f"Grandma's voice register: {voice['register']}")
if voice.get('endearments'):
    facts.append(f"Grandma's endearments: {', '.join(voice['endearments'])}")
if voice.get('sense_palette'):
    facts.append(f"Grandma's sensory palette: {', '.join(voice['sense_palette'])}")
if voice.get('opener_pattern'):
    facts.append(f"Grandma's opener: {voice['opener_pattern']}")

# World
world = data.get('world', {})
if world.get('house'):
    facts.append(f"Grandma's home: {world['house']}")
if world.get('stations'):
    facts.append(f"The Hearthfold has these stations: {', '.join(world['stations'])}")
if world.get('cellar'):
    facts.append(f"The cellar: shelves of {world['cellar'].get('shelves', '')}. The door {world['cellar'].get('door', '')}")
if world.get('candle'):
    facts.append(f"The candle: {world['candle']}")
if world.get('mirror'):
    facts.append(f"The hallway mirror: {world['mirror']}")
if world.get('blanket'):
    facts.append(f"The blanket: {world['blanket']}")

# Entities
for name, ent in data.get('entities', {}).items():
    rel = ent.get('relation', '')
    facts.append(f"{name}: {rel}")
    for note in ent.get('notes', []):
        if note.strip():
            facts.append(f"About {name}: {note}")

# Remembered
for mem in data.get('remembered', []):
    if isinstance(mem, str):
        facts.append(mem)
    elif isinstance(mem, dict):
        facts.append(mem.get('value', mem.get('text', str(mem))))

print(f"Extracted {len(facts)} facts")

# Embed
print("Embedding...")
embeddings = model.encode(facts, show_progress_bar=True, normalize_embeddings=True)

# Build substrate JSON
substrate = {
    "model": "Xenova/all-MiniLM-L6-v2",
    "dim": 384,
    "facts": []
}
for i, (fact, vec) in enumerate(zip(facts, embeddings)):
    substrate["facts"].append({
        "key": fact[:80],
        "value": fact,
        "vec": [round(float(v), 6) for v in vec]
    })

out_path = r'C:\Users\Forgemind\Desktop\Grandmas Hearth\mamba_webgpu\grandma-substrate.json'
with open(out_path, 'w', encoding='utf-8') as f:
    json.dump(substrate, f)

print(f"Saved {len(substrate['facts'])} facts to {out_path}")
print(f"File size: {len(json.dumps(substrate)) / 1024:.0f} KB")