AI platform insights monitoring and website analysis monitoring services added

backend/models/website_analysis_monitoring_models.py

@@ -0,0 +1,105 @@
+"""
+Website Analysis Monitoring Models
+Database models for tracking website analysis tasks and execution logs.
+"""
+
+from sqlalchemy import Column, Integer, String, Text, DateTime, Boolean, JSON, Index, ForeignKey
+from sqlalchemy.orm import relationship
+from datetime import datetime
+
+# Import the same Base from enhanced_strategy_models
+from models.enhanced_strategy_models import Base
+
+
+class WebsiteAnalysisTask(Base):
+    """
+    Model for storing website analysis monitoring tasks.
+    
+    Tracks per-user, per-URL website analysis with recurring checks.
+    """
+    __tablename__ = "website_analysis_tasks"
+    
+    id = Column(Integer, primary_key=True, index=True)
+    
+    # User and URL Identification
+    user_id = Column(String(255), nullable=False, index=True)  # Clerk user ID (string)
+    website_url = Column(String(500), nullable=False)  # URL to analyze
+    task_type = Column(String(50), nullable=False)  # 'user_website' or 'competitor'
+    competitor_id = Column(String(255), nullable=True)  # For competitor tasks (domain or identifier)
+    
+    # Task Status
+    status = Column(String(50), default='active')  # 'active', 'failed', 'paused'
+    
+    # Execution Tracking
+    last_check = Column(DateTime, nullable=True)
+    last_success = Column(DateTime, nullable=True)
+    last_failure = Column(DateTime, nullable=True)
+    failure_reason = Column(Text, nullable=True)
+    
+    # Scheduling
+    next_check = Column(DateTime, nullable=True, index=True)  # Next scheduled check time
+    frequency_days = Column(Integer, default=10)  # Recurring frequency in days
+    
+    # Metadata
+    created_at = Column(DateTime, default=datetime.utcnow)
+    updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
+    
+    # Execution Logs Relationship
+    execution_logs = relationship(
+        "WebsiteAnalysisExecutionLog",
+        back_populates="task",
+        cascade="all, delete-orphan"
+    )
+    
+    # Indexes for efficient queries
+    # Note: Index names match migration script to avoid conflicts
+    __table_args__ = (
+        Index('idx_website_analysis_tasks_user_url', 'user_id', 'website_url'),
+        Index('idx_website_analysis_tasks_user_task_type', 'user_id', 'task_type'),
+        Index('idx_website_analysis_tasks_next_check', 'next_check'),
+        Index('idx_website_analysis_tasks_status', 'status'),
+        Index('idx_website_analysis_tasks_task_type', 'task_type'),
+    )
+    
+    def __repr__(self):
+        return f"<WebsiteAnalysisTask(id={self.id}, user_id={self.user_id}, url={self.website_url}, type={self.task_type}, status={self.status})>"
+
+
+class WebsiteAnalysisExecutionLog(Base):
+    """
+    Model for storing website analysis execution logs.
+    
+    Tracks individual execution attempts with results and error details.
+    """
+    __tablename__ = "website_analysis_execution_logs"
+    
+    id = Column(Integer, primary_key=True, index=True)
+    
+    # Task Reference
+    task_id = Column(Integer, ForeignKey("website_analysis_tasks.id"), nullable=False, index=True)
+    
+    # Execution Details
+    execution_date = Column(DateTime, default=datetime.utcnow, nullable=False)
+    status = Column(String(50), nullable=False)  # 'success', 'failed', 'skipped', 'running'
+    
+    # Results
+    result_data = Column(JSON, nullable=True)  # Analysis results (style_analysis, crawl_result, etc.)
+    error_message = Column(Text, nullable=True)
+    execution_time_ms = Column(Integer, nullable=True)
+    
+    # Metadata
+    created_at = Column(DateTime, default=datetime.utcnow)
+    
+    # Relationship to task
+    task = relationship("WebsiteAnalysisTask", back_populates="execution_logs")
+    
+    # Indexes for efficient queries
+    # Note: Index names match migration script to avoid conflicts
+    __table_args__ = (
+        Index('idx_website_analysis_execution_logs_task_execution_date', 'task_id', 'execution_date'),
+        Index('idx_website_analysis_execution_logs_status', 'status'),
+    )
+    
+    def __repr__(self):
+        return f"<WebsiteAnalysisExecutionLog(id={self.id}, task_id={self.task_id}, status={self.status}, execution_date={self.execution_date})>"
+