98fa16bfc3
Full-stack Amazon AI Transcreation Platform with: - FastAPI backend (async, PostgreSQL, Redis, Celery) with 11 DB tables - JWT auth (SSO-ready abstract provider pattern) - 6-agent pipeline orchestrator with deterministic modules - Next.js 14 frontend with Amazon branding (Ember fonts, orange/dark theme) - Job wizard, monitoring HUD, output review, admin screens - 154 TM/reference files imported, 12 locales configured - Docker Compose for all services Agents 2-5 (TM retrieval, ranker, transcreator, compliance) are stubs pending Phase 3 LLM integration. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
46 lines
927 B
Python
46 lines
927 B
Python
from datetime import datetime
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from uuid import UUID
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from pydantic import BaseModel
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from app.models.files import ReferenceFileType
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class TMFileResponse(BaseModel):
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id: UUID
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client_id: UUID
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locale_code: str
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channel: str
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filename: str
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file_path: str
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segment_count: int
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uploaded_by: UUID | None = None
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uploaded_at: datetime
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last_updated_at: datetime
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version: int
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model_config = {"from_attributes": True}
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class ReferenceFileResponse(BaseModel):
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id: UUID
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client_id: UUID
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file_type: ReferenceFileType
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locale_scope: str
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filename: str
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file_path: str
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uploaded_by: UUID | None = None
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uploaded_at: datetime
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last_updated_at: datetime
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version: int
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model_config = {"from_attributes": True}
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class FileUploadResponse(BaseModel):
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id: UUID
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filename: str
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file_path: str
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message: str
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model_config = {"from_attributes": True}
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