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