106 lines
4.0 KiB
Python
106 lines
4.0 KiB
Python
"""
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Website Analysis Monitoring Models
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Database models for tracking website analysis tasks and execution logs.
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"""
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from sqlalchemy import Column, Integer, String, Text, DateTime, Boolean, JSON, Index, ForeignKey
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from sqlalchemy.orm import relationship
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from datetime import datetime
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# Import the same Base from enhanced_strategy_models
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from models.enhanced_strategy_models import Base
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class WebsiteAnalysisTask(Base):
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"""
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Model for storing website analysis monitoring tasks.
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Tracks per-user, per-URL website analysis with recurring checks.
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"""
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__tablename__ = "website_analysis_tasks"
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id = Column(Integer, primary_key=True, index=True)
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# User and URL Identification
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user_id = Column(String(255), nullable=False, index=True) # Clerk user ID (string)
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website_url = Column(String(500), nullable=False) # URL to analyze
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task_type = Column(String(50), nullable=False) # 'user_website' or 'competitor'
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competitor_id = Column(String(255), nullable=True) # For competitor tasks (domain or identifier)
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# Task Status
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status = Column(String(50), default='active') # 'active', 'failed', 'paused'
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# Execution Tracking
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last_check = Column(DateTime, nullable=True)
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last_success = Column(DateTime, nullable=True)
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last_failure = Column(DateTime, nullable=True)
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failure_reason = Column(Text, nullable=True)
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# Scheduling
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next_check = Column(DateTime, nullable=True, index=True) # Next scheduled check time
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frequency_days = Column(Integer, default=10) # Recurring frequency in days
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# Metadata
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created_at = Column(DateTime, default=datetime.utcnow)
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updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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# Execution Logs Relationship
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execution_logs = relationship(
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"WebsiteAnalysisExecutionLog",
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back_populates="task",
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cascade="all, delete-orphan"
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)
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# Indexes for efficient queries
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# Note: Index names match migration script to avoid conflicts
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__table_args__ = (
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Index('idx_website_analysis_tasks_user_url', 'user_id', 'website_url'),
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Index('idx_website_analysis_tasks_user_task_type', 'user_id', 'task_type'),
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Index('idx_website_analysis_tasks_next_check', 'next_check'),
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Index('idx_website_analysis_tasks_status', 'status'),
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Index('idx_website_analysis_tasks_task_type', 'task_type'),
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)
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def __repr__(self):
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return f"<WebsiteAnalysisTask(id={self.id}, user_id={self.user_id}, url={self.website_url}, type={self.task_type}, status={self.status})>"
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class WebsiteAnalysisExecutionLog(Base):
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"""
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Model for storing website analysis execution logs.
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Tracks individual execution attempts with results and error details.
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"""
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__tablename__ = "website_analysis_execution_logs"
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id = Column(Integer, primary_key=True, index=True)
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# Task Reference
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task_id = Column(Integer, ForeignKey("website_analysis_tasks.id"), nullable=False, index=True)
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# Execution Details
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execution_date = Column(DateTime, default=datetime.utcnow, nullable=False)
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status = Column(String(50), nullable=False) # 'success', 'failed', 'skipped', 'running'
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# Results
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result_data = Column(JSON, nullable=True) # Analysis results (style_analysis, crawl_result, etc.)
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error_message = Column(Text, nullable=True)
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execution_time_ms = Column(Integer, nullable=True)
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# Metadata
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created_at = Column(DateTime, default=datetime.utcnow)
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# Relationship to task
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task = relationship("WebsiteAnalysisTask", back_populates="execution_logs")
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# Indexes for efficient queries
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# Note: Index names match migration script to avoid conflicts
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__table_args__ = (
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Index('idx_website_analysis_execution_logs_task_execution_date', 'task_id', 'execution_date'),
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Index('idx_website_analysis_execution_logs_status', 'status'),
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)
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def __repr__(self):
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return f"<WebsiteAnalysisExecutionLog(id={self.id}, task_id={self.task_id}, status={self.status}, execution_date={self.execution_date})>"
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