35 lines
1.4 KiB
Markdown
35 lines
1.4 KiB
Markdown
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## 1. Project Scaffolding
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- [x] 1.1 Create FastAPI project structure (`app/`, `api/`, `core/`, `services/`, `schemas/`)
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- [x] 1.2 Use uv handle with dependencies (fastapi, uvicorn, opencv-python, python-multipart, pydantic, httpx)
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- [x] 1.3 Create `app/main.py` with FastAPI app initialization
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- [x] 1.4 Create `app/core/config.py` with Pydantic Settings
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## 2. Image OCR API
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- [x] 2.1 Create request/response schemas in `app/schemas/image.py`
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- [x] 2.2 Implement image preprocessing service with OpenCV padding (`app/services/image_processor.py`)
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- [x] 2.3 Implement DocLayout-YOLO wrapper (`app/services/layout_detector.py`)
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- [x] 2.4 Implement PaddleOCR-VL client (`app/services/ocr_service.py`)
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- [x] 2.5 Create image OCR endpoint (`app/api/v1/endpoints/image.py`)
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- [x] 2.6 Wire up router and test endpoint
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## 3. Markdown to DOCX API
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- [x] 3.1 Create request/response schemas in `app/schemas/convert.py`
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- [x] 3.2 Integrate markdown_2_docx library (`app/services/docx_converter.py`)
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- [x] 3.3 Create conversion endpoint (`app/api/v1/endpoints/convert.py`)
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- [x] 3.4 Wire up router and test endpoint
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## 4. Deployment
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- [x] 4.1 Create Dockerfile with CUDA base image for RTX 5080
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- [x] 4.2 Create docker-compose.yml (optional, for local development)
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- [x] 4.3 Document deployment steps in README
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## 5. Validation
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- [ ] 5.1 Test image OCR endpoint with sample images
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- [ ] 5.2 Test markdown to DOCX conversion
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- [ ] 5.3 Verify Docker build and GPU access
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