Files
roa2web-service-auto/backend/modules/data_entry/schemas/ocr.py
Marius Mutu c5e051ad80 feat: Migrate to ultrathin monolith architecture
Consolidate 3 separate applications (reports-app, data-entry-app, telegram-bot) into a unified
architecture with single backend and frontend:

Backend Changes:
- Unified FastAPI backend at backend/ with modular structure
- Modules: reports, data_entry, telegram in backend/modules/
- Centralized config.py and main.py with all routers registered
- Single worker mode (--workers 1) for Telegram bot compatibility
- Shared Oracle connection pool and JWT authentication
- Unified requirements.txt and environment configuration

Frontend Changes:
- Single Vue.js SPA with module-based routing
- Unified frontend at src/ with modules in src/modules/{reports,data-entry}/
- Shared components and stores in src/shared/
- Error boundaries for module isolation
- Dual API proxy in Vite for module communication

Infrastructure:
- New unified startup scripts: start-prod.sh, start-test.sh, start-backend.sh
- Environment templates: .env.dev.example, .env.test.example, .env.prod.example
- Updated deployment scripts for Windows IIS
- Simplified SSH tunnel management

Documentation:
- Comprehensive CLAUDE.md with architecture overview
- Module-specific docs in docs/{data-entry,telegram}/
- Architecture decision records in docs/ARCHITECTURE-DECISIONS.md
- Deployment guides consolidated in deployment/windows/docs/

This migration reduces complexity, improves maintainability, and enables easier
deployment while maintaining all existing functionality.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
2025-12-29 23:48:14 +02:00

123 lines
5.6 KiB
Python

"""Pydantic schemas for OCR API."""
from datetime import date
from decimal import Decimal
from typing import Optional, List
from pydantic import BaseModel, Field
class TvaEntry(BaseModel):
"""Single TVA entry with code, percentage and amount."""
code: Optional[str] = Field(default=None, description="TVA code: A, B, C, D")
percent: int = Field(description="TVA percentage: 0, 5, 9, 19, 21")
amount: Decimal = Field(description="TVA amount for this rate")
class PaymentMethod(BaseModel):
"""Payment method entry from OCR."""
method: str = Field(description="CARD or NUMERAR")
amount: Decimal = Field(description="Amount paid")
class ExtractionData(BaseModel):
"""Extracted receipt data from OCR."""
receipt_type: str = Field(default='bon_fiscal', description="Receipt type: bon_fiscal or chitanta")
receipt_number: Optional[str] = Field(default=None, description="Receipt number")
receipt_series: Optional[str] = Field(default=None, description="Receipt series")
receipt_date: Optional[date] = Field(default=None, description="Receipt date")
amount: Optional[Decimal] = Field(default=None, description="Total amount")
partner_name: Optional[str] = Field(default=None, description="Vendor/partner name")
cui: Optional[str] = Field(default=None, description="CUI (fiscal identification code)")
description: Optional[str] = Field(default=None, description="Optional description")
# Additional extracted fields - Multiple TVA entries support
tva_entries: List[TvaEntry] = Field(default=[], description="List of TVA entries by rate (A, B, C, D)")
tva_total: Optional[Decimal] = Field(default=None, description="Total TVA amount")
address: Optional[str] = Field(default=None, description="Vendor address")
items_count: Optional[int] = Field(default=None, description="Number of items/articles")
# Payment methods extracted from receipt
payment_methods: List[PaymentMethod] = Field(default=[], description="Payment methods from receipt (CARD, NUMERAR)")
suggested_payment_mode: Optional[str] = Field(default=None, description="Auto-suggested payment mode based on OCR (casa/banca)")
# Client data (for B2B receipts - buyer information)
client_name: Optional[str] = Field(default=None, description="Client/customer company name")
client_cui: Optional[str] = Field(default=None, description="Client CUI/CIF fiscal code")
client_address: Optional[str] = Field(default=None, description="Client address")
confidence_amount: float = Field(default=0.0, ge=0, le=1, description="Amount extraction confidence")
confidence_date: float = Field(default=0.0, ge=0, le=1, description="Date extraction confidence")
confidence_vendor: float = Field(default=0.0, ge=0, le=1, description="Vendor extraction confidence")
confidence_client: float = Field(default=0.0, ge=0, le=1, description="Client extraction confidence")
overall_confidence: float = Field(default=0.0, ge=0, le=1, description="Overall confidence score")
raw_text: str = Field(default="", description="Raw OCR text")
ocr_engine: str = Field(default="", description="OCR engine used: paddleocr or tesseract")
processing_time_ms: int = Field(default=0, ge=0, description="Processing time in milliseconds")
class Config:
"""Pydantic config."""
json_schema_extra = {
"example": {
"receipt_type": "bon_fiscal",
"receipt_number": "1360760",
"receipt_series": "0146",
"receipt_date": "2025-10-11",
"amount": 186.16,
"partner_name": "FIVE-HOLDING S.A.",
"cui": "10562600",
"description": None,
"tva_entries": [
{"code": "A", "percent": 19, "amount": 25.00},
{"code": "B", "percent": 9, "amount": 7.31}
],
"tva_total": 32.31,
"address": "JUD. CONSTANTA, MUN. CONSTANTA, STR. ION ROATA NR. 3",
"items_count": 17,
"confidence_amount": 0.98,
"confidence_date": 0.98,
"confidence_vendor": 0.95,
"overall_confidence": 0.97,
"raw_text": "FIVE-HOLDING S.A.\nCIF: RO10562600\n..."
}
}
class OCRResponse(BaseModel):
"""OCR API response."""
success: bool = Field(description="Whether OCR processing was successful")
message: str = Field(description="Status message")
data: Optional[ExtractionData] = Field(default=None, description="Extracted data")
class Config:
"""Pydantic config."""
json_schema_extra = {
"example": {
"success": True,
"message": "OCR processing successful. Found: amount, date, vendor",
"data": {
"receipt_type": "bon_fiscal",
"receipt_number": "12345",
"receipt_date": "2024-01-15",
"amount": 125.50,
"partner_name": "MEGA IMAGE SRL",
"cui": "12345678",
"confidence_amount": 0.95,
"confidence_date": 0.90,
"confidence_vendor": 0.75,
"overall_confidence": 0.87,
"raw_text": "BON FISCAL\nMEGA IMAGE SRL\n..."
}
}
}
class OCRStatusResponse(BaseModel):
"""OCR service status response."""
available: bool = Field(description="Whether OCR service is available")
engines: list[str] = Field(description="Available OCR engines")
message: str = Field(description="Status message")