ef749e4878
- Set up FastAPI application structure - Implement SQLite database with SQLAlchemy - Create WhatsApp webhook endpoints - Implement message storage and analysis - Integrate Gemini 2.5 Pro for message analysis - Add email delivery of insights - Configure APScheduler for weekend analysis - Add linting with Ruff
34 lines
972 B
Python
34 lines
972 B
Python
"""
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SQLAlchemy models for WhatsApp messages.
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"""
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from sqlalchemy import Boolean, Column, DateTime, Integer, String, Text
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from sqlalchemy.sql import func
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from app.db.base_class import Base
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class Message(Base):
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"""
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Model for storing WhatsApp messages.
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"""
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id = Column(Integer, primary_key=True, index=True)
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# WhatsApp message details
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sender_phone = Column(String(20), index=True, nullable=False)
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sender_name = Column(String(255), index=True, nullable=False)
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message_body = Column(Text, nullable=False)
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# Metadata
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timestamp = Column(DateTime, default=func.now(), nullable=False, index=True)
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# Tracking fields
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analyzed = Column(Boolean, default=False, index=True)
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last_analyzed_at = Column(DateTime, nullable=True)
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def __repr__(self) -> str:
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"""
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String representation of the message.
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"""
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return f"<Message {self.id}: {self.sender_name} ({self.sender_phone}) at {self.timestamp}>"
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