Spaces:
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File size: 2,578 Bytes
8de5584 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | from sqlalchemy import Column, Integer, String, DateTime, Boolean, Float, Text, LargeBinary
from sqlalchemy.ext.declarative import declarative_base
from datetime import datetime
from pgvector.sqlalchemy import Vector
Base = declarative_base()
class Document(Base):
"""Model for uploaded documents"""
__tablename__ = "documents"
id = Column(Integer, primary_key=True)
file_id = Column(String(36), unique=True, index=True)
filename = Column(String(255))
file_size = Column(Integer)
file_type = Column(String(50))
upload_date = Column(DateTime, default=datetime.utcnow)
ocr_status = Column(String(50), default="pending") # pending, completed, failed
extraction_status = Column(String(50), default="pending")
raw_text = Column(Text, nullable=True)
cleaned_text = Column(Text, nullable=True)
file_data = Column(LargeBinary, nullable=True) # Original file bytes (replaces MS SQL vault)
class PathologyReport(Base):
"""Model for extracted pathology report data"""
__tablename__ = "pathology_reports"
id = Column(Integer, primary_key=True)
document_id = Column(String(36), index=True)
patient_id = Column(String(100), nullable=True)
patient_name = Column(String(255), nullable=True)
test_type = Column(String(255))
test_date = Column(DateTime, nullable=True)
findings = Column(Text) # JSON string
diagnosis = Column(Text, nullable=True)
recommendations = Column(Text, nullable=True)
summary = Column(Text)
created_at = Column(DateTime, default=datetime.utcnow)
updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
class DocumentEmbedding(Base):
"""Model for document embeddings (vector storage)"""
__tablename__ = "document_embeddings"
id = Column(Integer, primary_key=True)
document_id = Column(String(36), index=True, unique=True)
embedding = Column(
Vector(768)
) # FremyCompany/BioLORD-2023-M (biomedical domain) embedding is 768-dimensional
text_chunk = Column(Text)
created_at = Column(DateTime, default=datetime.utcnow)
class User(Base):
"""Model for user management"""
__tablename__ = "users"
id = Column(Integer, primary_key=True)
username = Column(String(100), unique=True, index=True)
email = Column(String(255), unique=True, index=True)
password_hash = Column(String(255))
role = Column(String(50), default="doctor") # doctor, lab_tech, admin
is_active = Column(Boolean, default=True)
created_at = Column(DateTime, default=datetime.utcnow)
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