Marius Mutu
41ae97180e
Implement Tesseract-based OCR to automatically extract vendor name, date, total amount, and VAT from uploaded receipt images/PDFs, reducing manual data entry and improving accuracy. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
85 lines
3.4 KiB
Python
85 lines
3.4 KiB
Python
"""Pydantic schemas for OCR API."""
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from datetime import date
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from decimal import Decimal
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from typing import Optional
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from pydantic import BaseModel, Field
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class ExtractionData(BaseModel):
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"""Extracted receipt data from OCR."""
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receipt_type: str = Field(default='bon_fiscal', description="Receipt type: bon_fiscal or chitanta")
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receipt_number: Optional[str] = Field(default=None, description="Receipt number")
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receipt_series: Optional[str] = Field(default=None, description="Receipt series")
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receipt_date: Optional[date] = Field(default=None, description="Receipt date")
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amount: Optional[Decimal] = Field(default=None, description="Total amount")
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partner_name: Optional[str] = Field(default=None, description="Vendor/partner name")
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cui: Optional[str] = Field(default=None, description="CUI (fiscal identification code)")
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description: Optional[str] = Field(default=None, description="Optional description")
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confidence_amount: float = Field(default=0.0, ge=0, le=1, description="Amount extraction confidence")
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confidence_date: float = Field(default=0.0, ge=0, le=1, description="Date extraction confidence")
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confidence_vendor: float = Field(default=0.0, ge=0, le=1, description="Vendor extraction confidence")
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overall_confidence: float = Field(default=0.0, ge=0, le=1, description="Overall confidence score")
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raw_text: str = Field(default="", description="Raw OCR text")
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class Config:
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"""Pydantic config."""
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json_schema_extra = {
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"example": {
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"receipt_type": "bon_fiscal",
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"receipt_number": "12345",
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"receipt_series": None,
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"receipt_date": "2024-01-15",
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"amount": 125.50,
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"partner_name": "MEGA IMAGE SRL",
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"cui": "12345678",
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"description": None,
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"confidence_amount": 0.95,
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"confidence_date": 0.90,
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"confidence_vendor": 0.75,
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"overall_confidence": 0.87,
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"raw_text": "BON FISCAL\nMEGA IMAGE SRL\n..."
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}
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}
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class OCRResponse(BaseModel):
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"""OCR API response."""
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success: bool = Field(description="Whether OCR processing was successful")
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message: str = Field(description="Status message")
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data: Optional[ExtractionData] = Field(default=None, description="Extracted data")
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class Config:
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"""Pydantic config."""
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json_schema_extra = {
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"example": {
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"success": True,
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"message": "OCR processing successful. Found: amount, date, vendor",
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"data": {
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"receipt_type": "bon_fiscal",
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"receipt_number": "12345",
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"receipt_date": "2024-01-15",
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"amount": 125.50,
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"partner_name": "MEGA IMAGE SRL",
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"cui": "12345678",
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"confidence_amount": 0.95,
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"confidence_date": 0.90,
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"confidence_vendor": 0.75,
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"overall_confidence": 0.87,
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"raw_text": "BON FISCAL\nMEGA IMAGE SRL\n..."
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}
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}
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}
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class OCRStatusResponse(BaseModel):
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"""OCR service status response."""
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available: bool = Field(description="Whether OCR service is available")
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engines: list[str] = Field(description="Available OCR engines")
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message: str = Field(description="Status message")
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