35 lines
1.4 KiB
Python
35 lines
1.4 KiB
Python
from .base import BaseLanguageAgent
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from ..normalizers import normalize_en_text
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from ..system_strings import build_contextual_system_vocabulary, build_system_vocabulary
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class EnglishAgent(BaseLanguageAgent):
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locale = "en"
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tone = "business-friendly and direct"
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preferred_formality = "neutral"
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vocabulary_map = {
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**build_system_vocabulary("en", ("plan_badge", "services_badge", "transparent_label", "transparent_investment")),
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}
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_system_contextual = build_contextual_system_vocabulary("en", ("plan_badge", "services_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": "Book starter call",
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"business": "Book business call",
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"support": "View support",
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"service": "View services",
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"project": "Start your project",
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"quote": "Request a quote",
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"contact": "Book intro call",
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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_en_text(text, field_path=field_path)
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