"""
Docker Log Monitor for N8N

This module provides functionality to monitor N8N Docker container logs
in real-time to catch errors and analyze workflow execution.
"""

import subprocess
import threading
import json
import re
import logging
from datetime import datetime
from typing import Dict, List, Any, Optional, Callable
from pathlib import Path
import time

logger = logging.getLogger(__name__)

class DockerLogMonitor:
    """Monitor N8N Docker container logs for error detection and analysis."""
    
    def __init__(self, container_name: str = "n8n", compose_file: Optional[str] = None):
        """
        Initialize the Docker log monitor.
        
        Args:
            container_name: Name of the N8N Docker container
            compose_file: Path to docker-compose file (optional)
        """
        self.container_name = container_name
        self.compose_file = compose_file
        self.is_monitoring = False
        self.monitor_thread = None
        self.log_callbacks = []
        self.error_patterns = [
            r"ERROR",
            r"FATAL",
            r"Exception",
            r"Error:",
            r"Failed",
            r"Workflow execution.*failed",
            r"Node.*failed",
            r"Timeout",
            r"Connection.*failed",
            r"Authentication.*failed"
        ]
        self.log_buffer = []
        self.max_buffer_size = 1000
    
    def add_log_callback(self, callback: Callable[[Dict[str, Any]], None]):
        """
        Add a callback function to be called when new logs are received.
        
        Args:
            callback: Function to call with log entry data
        """
        self.log_callbacks.append(callback)
    
    def start_monitoring(self, tail_lines: int = 100, follow: bool = True) -> bool:
        """
        Start monitoring Docker logs.
        
        Args:
            tail_lines: Number of existing log lines to retrieve
            follow: Whether to follow new logs in real-time
            
        Returns:
            True if monitoring started successfully
        """
        if self.is_monitoring:
            logger.warning("Log monitoring is already running")
            return False
        
        try:
            self.is_monitoring = True
            self.monitor_thread = threading.Thread(
                target=self._monitor_logs,
                args=(tail_lines, follow),
                daemon=True
            )
            self.monitor_thread.start()
            logger.info(f"Started monitoring logs for container: {self.container_name}")
            return True
            
        except Exception as e:
            logger.error(f"Failed to start log monitoring: {e}")
            self.is_monitoring = False
            return False
    
    def stop_monitoring(self):
        """Stop monitoring Docker logs."""
        self.is_monitoring = False
        if self.monitor_thread and self.monitor_thread.is_alive():
            self.monitor_thread.join(timeout=5)
        logger.info("Stopped log monitoring")
    
    def _monitor_logs(self, tail_lines: int, follow: bool):
        """Internal method to monitor Docker logs."""
        try:
            # Build docker logs command
            if self.compose_file:
                cmd = [
                    "docker-compose", "-f", self.compose_file,
                    "logs", "--tail", str(tail_lines)
                ]
                if follow:
                    cmd.append("-f")
                cmd.append(self.container_name)
            else:
                cmd = [
                    "docker", "logs", "--tail", str(tail_lines)
                ]
                if follow:
                    cmd.append("-f")
                cmd.append(self.container_name)
            
            # Start subprocess
            process = subprocess.Popen(
                cmd,
                stdout=subprocess.PIPE,
                stderr=subprocess.STDOUT,
                universal_newlines=True,
                bufsize=1
            )
            
            # Read logs line by line
            while self.is_monitoring and process.poll() is None:
                line = process.stdout.readline()
                if line:
                    self._process_log_line(line.strip())
            
            # Process any remaining output
            if process.poll() is not None:
                remaining_output = process.stdout.read()
                if remaining_output:
                    for line in remaining_output.split('\n'):
                        if line.strip():
                            self._process_log_line(line.strip())
            
        except Exception as e:
            logger.error(f"Error in log monitoring: {e}")
        finally:
            self.is_monitoring = False
    
    def _process_log_line(self, line: str):
        """Process a single log line."""
        try:
            # Parse timestamp and message
            log_entry = self._parse_log_line(line)
            
            # Add to buffer
            self.log_buffer.append(log_entry)
            if len(self.log_buffer) > self.max_buffer_size:
                self.log_buffer.pop(0)
            
            # Check for errors
            if self._is_error_line(line):
                log_entry['is_error'] = True
                logger.warning(f"Error detected in N8N logs: {line}")
            
            # Call registered callbacks
            for callback in self.log_callbacks:
                try:
                    callback(log_entry)
                except Exception as e:
                    logger.error(f"Error in log callback: {e}")
                    
        except Exception as e:
            logger.error(f"Error processing log line: {e}")
    
    def _parse_log_line(self, line: str) -> Dict[str, Any]:
        """Parse a log line into structured data."""
        log_entry = {
            'timestamp': datetime.now().isoformat(),
            'raw_line': line,
            'message': line,
            'level': 'INFO',
            'is_error': False,
            'workflow_id': None,
            'execution_id': None,
            'node_name': None
        }
        
        # Try to extract timestamp from log line
        timestamp_match = re.search(r'(\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2})', line)
        if timestamp_match:
            log_entry['timestamp'] = timestamp_match.group(1)
        
        # Extract log level
        level_match = re.search(r'\b(DEBUG|INFO|WARN|ERROR|FATAL)\b', line, re.IGNORECASE)
        if level_match:
            log_entry['level'] = level_match.group(1).upper()
        
        # Try to extract workflow ID
        workflow_id_match = re.search(r'workflow[_\s]*id[:\s]*([a-zA-Z0-9-]+)', line, re.IGNORECASE)
        if workflow_id_match:
            log_entry['workflow_id'] = workflow_id_match.group(1)
        
        # Try to extract execution ID
        execution_id_match = re.search(r'execution[_\s]*id[:\s]*([a-zA-Z0-9-]+)', line, re.IGNORECASE)
        if execution_id_match:
            log_entry['execution_id'] = execution_id_match.group(1)
        
        # Try to extract node name
        node_match = re.search(r'node[:\s]*(["\']?)([^"\'\\s]+)\1', line, re.IGNORECASE)
        if node_match:
            log_entry['node_name'] = node_match.group(2)
        
        return log_entry
    
    def _is_error_line(self, line: str) -> bool:
        """Check if a log line contains an error."""
        for pattern in self.error_patterns:
            if re.search(pattern, line, re.IGNORECASE):
                return True
        return False
    
    def get_recent_logs(self, count: int = 50, 
                       error_only: bool = False) -> List[Dict[str, Any]]:
        """
        Get recent logs from buffer.
        
        Args:
            count: Number of log entries to return
            error_only: If True, return only error logs
            
        Returns:
            List of log entries
        """
        logs = self.log_buffer[-count:] if count > 0 else self.log_buffer
        
        if error_only:
            logs = [log for log in logs if log.get('is_error', False)]
        
        return logs
    
    def get_logs_for_workflow(self, workflow_id: str, 
                             since_minutes: int = 60) -> List[Dict[str, Any]]:
        """
        Get logs for a specific workflow.
        
        Args:
            workflow_id: ID of the workflow
            since_minutes: Look for logs in the last N minutes
            
        Returns:
            List of log entries for the workflow
        """
        cutoff_time = datetime.now().timestamp() - (since_minutes * 60)
        
        workflow_logs = []
        for log in self.log_buffer:
            if log.get('workflow_id') == workflow_id:
                try:
                    log_time = datetime.fromisoformat(log['timestamp'].replace('Z', '+00:00')).timestamp()
                    if log_time >= cutoff_time:
                        workflow_logs.append(log)
                except:
                    # If timestamp parsing fails, include the log anyway
                    workflow_logs.append(log)
        
        return workflow_logs
    
    def get_error_summary(self, since_minutes: int = 60) -> Dict[str, Any]:
        """
        Get a summary of errors in the specified time period.
        
        Args:
            since_minutes: Look for errors in the last N minutes
            
        Returns:
            Error summary with counts and patterns
        """
        cutoff_time = datetime.now().timestamp() - (since_minutes * 60)
        
        errors = []
        for log in self.log_buffer:
            if log.get('is_error', False):
                try:
                    log_time = datetime.fromisoformat(log['timestamp'].replace('Z', '+00:00')).timestamp()
                    if log_time >= cutoff_time:
                        errors.append(log)
                except:
                    errors.append(log)
        
        # Analyze error patterns
        error_patterns = {}
        workflow_errors = {}
        node_errors = {}
        
        for error in errors:
            # Count by message pattern
            message = error.get('message', '')
            for pattern in self.error_patterns:
                if re.search(pattern, message, re.IGNORECASE):
                    error_patterns[pattern] = error_patterns.get(pattern, 0) + 1
            
            # Count by workflow
            workflow_id = error.get('workflow_id')
            if workflow_id:
                workflow_errors[workflow_id] = workflow_errors.get(workflow_id, 0) + 1
            
            # Count by node
            node_name = error.get('node_name')
            if node_name:
                node_errors[node_name] = node_errors.get(node_name, 0) + 1
        
        return {
            'total_errors': len(errors),
            'time_period_minutes': since_minutes,
            'error_patterns': error_patterns,
            'workflow_errors': workflow_errors,
            'node_errors': node_errors,
            'recent_errors': errors[-10:] if errors else []  # Last 10 errors
        }
    
    def save_logs_to_file(self, filepath: str, 
                         error_only: bool = False,
                         workflow_id: Optional[str] = None) -> str:
        """
        Save logs to a file.
        
        Args:
            filepath: Path to save the logs
            error_only: If True, save only error logs
            workflow_id: If specified, save only logs for this workflow
            
        Returns:
            Path to the saved file
        """
        logs_to_save = self.log_buffer.copy()
        
        # Filter by workflow if specified
        if workflow_id:
            logs_to_save = [log for log in logs_to_save if log.get('workflow_id') == workflow_id]
        
        # Filter by error status if specified
        if error_only:
            logs_to_save = [log for log in logs_to_save if log.get('is_error', False)]
        
        # Create directory if it doesn't exist
        Path(filepath).parent.mkdir(parents=True, exist_ok=True)
        
        # Save logs
        with open(filepath, 'w') as f:
            for log in logs_to_save:
                f.write(f"{json.dumps(log)}\n")
        
        logger.info(f"Saved {len(logs_to_save)} log entries to: {filepath}")
        return filepath
    
    def is_container_running(self) -> bool:
        """
        Check if the N8N container is running.
        
        Returns:
            True if container is running
        """
        try:
            if self.compose_file:
                result = subprocess.run(
                    ["docker-compose", "-f", self.compose_file, "ps", "-q", self.container_name],
                    capture_output=True, text=True, timeout=10
                )
            else:
                result = subprocess.run(
                    ["docker", "ps", "-q", "-f", f"name={self.container_name}"],
                    capture_output=True, text=True, timeout=10
                )
            
            return bool(result.stdout.strip())
            
        except Exception as e:
            logger.error(f"Error checking container status: {e}")
            return False
    
    def get_container_info(self) -> Dict[str, Any]:
        """
        Get information about the N8N container.
        
        Returns:
            Container information
        """
        try:
            if self.compose_file:
                result = subprocess.run(
                    ["docker-compose", "-f", self.compose_file, "ps", self.container_name],
                    capture_output=True, text=True, timeout=10
                )
            else:
                result = subprocess.run(
                    ["docker", "inspect", self.container_name],
                    capture_output=True, text=True, timeout=10
                )
                
                # Parse JSON output for docker inspect
                if result.returncode == 0:
                    container_data = json.loads(result.stdout)[0]
                    return {
                        'name': self.container_name,
                        'status': container_data['State']['Status'],
                        'running': container_data['State']['Running'],
                        'started_at': container_data['State']['StartedAt'],
                        'image': container_data['Config']['Image'],
                        'ports': container_data['NetworkSettings']['Ports']
                    }
            
            return {
                'name': self.container_name,
                'status': 'unknown',
                'info': result.stdout if result.returncode == 0 else result.stderr
            }
            
        except Exception as e:
            logger.error(f"Error getting container info: {e}")
            return {
                'name': self.container_name,
                'status': 'error',
                'error': str(e)
            }

def create_docker_log_monitor(container_name: str = "n8n", 
                             compose_file: Optional[str] = None):
    """Create a Docker log monitor instance."""
    return DockerLogMonitor(container_name, compose_file)