44 lines
1.6 KiB
Python
44 lines
1.6 KiB
Python
from .base import BaseLanguageAgent
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from ..normalizers import normalize_es_text
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from ..system_strings import build_contextual_system_vocabulary, build_system_vocabulary
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class SpanishAgent(BaseLanguageAgent):
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locale = "es"
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tone = "clear and business-focused"
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preferred_formality = "formal"
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vocabulary_map = {
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**build_system_vocabulary(
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"es",
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(
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"plan_badge",
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"response_time",
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"without_commitment",
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"transparent_label",
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"transparent_investment",
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),
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),
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}
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_system_contextual = build_contextual_system_vocabulary("es", ("plan_badge", "transparent_label"))
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contextual_vocabulary_map = {
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"badge": {**_system_contextual.get("badge", {})},
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"label": {**_system_contextual.get("label", {})},
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"metric": {**_system_contextual.get("metric", {})},
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"stat": {**_system_contextual.get("stat", {})},
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"title": {**_system_contextual.get("title", {})},
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"heading": {**_system_contextual.get("heading", {})},
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"rendered": {**_system_contextual.get("rendered", {})},
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}
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cta_defaults = {
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"starter": "Reservar llamada inicial",
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"business": "Reservar llamada comercial",
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"support": "Solicitar soporte",
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"service": "Mostrar los servicios",
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"project": "Inicia tu proyecto",
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"quote": "Solicitar propuesta",
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"contact": "Planificar la reunión inicial",
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}
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def post_cleanup_text(self, text: str, field_path: str = "") -> str:
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return normalize_es_text(text, field_path=field_path)
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