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# generator/templates_basic.py
import random
from typing import List, Dict, Any
from .meaning_model import StructuredMeaning, Emotion, Context
from .encoder import encode_term
def generate_emotional_expression_samples(
dictionaries: Dict[str, Any],
count: int,
source_language: str = "en",
) -> List[Dict[str, Any]]:
"""
Generate simple 'I feel X about Y' style samples.
"""
emotions = dictionaries.get("emotions", [])
times = dictionaries.get("context_time", [])
results = []
if not emotions:
raise ValueError("No 'emotions.json' loaded in dictionaries.")
if not times:
times = [{"id": "context.time.unspecified"}]
for _ in range(count):
emo = random.choice(emotions)
time_ctx = random.choice(times)
emo_type = emo.get("id", "emotion.unknown")
intensity = round(random.uniform(0.3, 0.9), 2)
time_id = time_ctx.get("id", "context.time.unspecified")
input_text = f"I feel {emo_type} about {time_id}."
meaning = StructuredMeaning(
actor="actor.self",
action="action.express_emotion",
object=None,
modifiers=[],
emotion=Emotion(type=emo_type, intensity=intensity),
context=Context(time=time_id),
intent="intent.express_emotion",
meta={
"source_language": source_language,
"confidence": 0.95,
},
)
emo_enc = encode_term(emo_type, dictionaries)
time_enc = encode_term(time_id, dictionaries)
glyphic_human = (
f"SELF.EMOTION[{emo_enc['human']}:{intensity}] "
f"CONTEXT.TIME[{time_enc['human']}] "
f"INTENT.express"
)
glyphic_compact = (
f"SELF{{EMO:{emo_enc['compact']}@{intensity}}} "
f"CTX{{TIME:{time_enc['compact']}}} "
f"INT{{EXP}}"
)
glyphic_tokens = (
f"<SELF> "
f"<EMO:{emo_enc['compact']}:{intensity}> "
f"<CTX:TIME:{time_enc['compact']}> "
f"<INT:EXP>"
)
results.append(
{
"input_text": input_text,
"glyphic_output_human": glyphic_human,
"glyphic_output_compact": glyphic_compact,
"glyphic_output_tokens": glyphic_tokens,
"structured_meaning": meaning.to_dict(),
}
)
return results
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