src/ui/log_viewer.py

import gradio as gr
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from datetime import datetime, timedelta
from typing import List, Dict, Any, Tuple
import json
from ..core.database_logger import get_logger, LogLevel, LogCategory

class LogViewer:
    """Interface para visualização de logs"""
    
    def __init__(self):
        self.logger = get_logger()
    
    def get_log_data(self, level_filter: str = "ALL", category_filter: str = "ALL",
                    hours_back: int = 24, limit: int = 1000) -> pd.DataFrame:
        """Recupera dados de log filtrados"""
        
        # Calcular tempo de início
        start_time = (datetime.now() - timedelta(hours=hours_back)).isoformat()
        
        # Aplicar filtros
        level = None if level_filter == "ALL" else level_filter
        category = None if category_filter == "ALL" else category_filter
        
        # Buscar logs
        logs = self.logger.get_logs(
            level=level,
            category=category,
            start_time=start_time,
            limit=limit
        )
        
        if not logs:
            return pd.DataFrame()
        
        # Converter para DataFrame
        df = pd.DataFrame(logs)
        df['timestamp'] = pd.to_datetime(df['timestamp'])
        
        return df
    
    def create_log_table(self, level_filter: str, category_filter: str, 
                        hours_back: int, limit: int) -> str:
        """Cria tabela HTML com os logs"""
        
        df = self.get_log_data(level_filter, category_filter, hours_back, limit)
        
        if df.empty:
            return "<p>Nenhum log encontrado com os filtros aplicados.</p>"
        
        # Preparar dados para exibição
        display_df = df[['timestamp', 'level', 'category', 'message', 'module', 'function']].copy()
        display_df['timestamp'] = display_df['timestamp'].dt.strftime('%Y-%m-%d %H:%M:%S')
        
        # Aplicar cores baseadas no nível
        def style_level(level):
            colors = {
                'DEBUG': '#6c757d',
                'INFO': '#17a2b8',
                'WARNING': '#ffc107',
                'ERROR': '#dc3545',
                'CRITICAL': '#6f42c1'
            }
            return f'<span style="color: {colors.get(level, "#000")}; font-weight: bold;">{level}</span>'
        
        display_df['level'] = display_df['level'].apply(style_level)
        
        # Converter para HTML
        html_table = display_df.to_html(escape=False, index=False, classes='table table-striped')
        
        return f"""
        <div style="max-height: 600px; overflow-y: auto;">
            {html_table}
        </div>
        """
    
    def create_log_statistics(self, hours_back: int) -> str:
        """Cria estatísticas dos logs"""
        
        stats = self.logger.get_statistics()
        
        # Logs das últimas N horas
        start_time = (datetime.now() - timedelta(hours=hours_back)).isoformat()
        recent_logs = self.logger.get_logs(start_time=start_time, limit=10000)
        
        recent_df = pd.DataFrame(recent_logs) if recent_logs else pd.DataFrame()
        
        html = f"""
        <div class="row">
            <div class="col-md-6">
                <h4>📊 Estatísticas Gerais</h4>
                <ul>
                    <li><strong>Total de Logs:</strong> {stats['total_logs']:,}</li>
                    <li><strong>Logs (últimas 24h):</strong> {stats['logs_last_24h']:,}</li>
                    <li><strong>Logs (últimas {hours_back}h):</strong> {len(recent_df):,}</li>
                </ul>
            </div>
            <div class="col-md-6">
                <h4>📈 Distribuição por Nível</h4>
                <ul>
        """
        
        for level, count in stats['logs_by_level'].items():
            html += f"<li><strong>{level}:</strong> {count:,}</li>"
        
        html += """
                </ul>
            </div>
        </div>
        <div class="row mt-3">
            <div class="col-12">
                <h4>🏷️ Distribuição por Categoria</h4>
                <ul>
        """
        
        for category, count in stats['logs_by_category'].items():
            html += f"<li><strong>{category}:</strong> {count:,}</li>"
        
        html += """
                </ul>
            </div>
        </div>
        """
        
        return html
    
    def create_performance_chart(self, hours_back: int) -> go.Figure:
        """Cria gráfico de métricas de performance"""
        
        start_time = (datetime.now() - timedelta(hours=hours_back)).isoformat()
        metrics = self.logger.get_performance_metrics(start_time=start_time, limit=1000)
        
        if not metrics:
            fig = go.Figure()
            fig.add_annotation(
                text="Nenhuma métrica de performance encontrada",
                xref="paper", yref="paper",
                x=0.5, y=0.5, showarrow=False
            )
            return fig
        
        df = pd.DataFrame(metrics)
        df['timestamp'] = pd.to_datetime(df['timestamp'])
        
        # Agrupar por nome da métrica
        fig = go.Figure()
        
        for metric_name in df['metric_name'].unique():
            metric_data = df[df['metric_name'] == metric_name]
            
            fig.add_trace(go.Scatter(
                x=metric_data['timestamp'],
                y=metric_data['metric_value'],
                mode='lines+markers',
                name=metric_name,
                hovertemplate='<b>%{fullData.name}</b><br>' +
                             'Tempo: %{x}<br>' +
                             'Valor: %{y}<br>' +
                             '<extra></extra>'
            ))
        
        fig.update_layout(
            title="Métricas de Performance ao Longo do Tempo",
            xaxis_title="Timestamp",
            yaxis_title="Valor",
            hovermode='closest'
        )
        
        return fig
    
    def create_log_timeline_chart(self, hours_back: int) -> go.Figure:
        """Cria gráfico de timeline dos logs"""
        
        start_time = (datetime.now() - timedelta(hours=hours_back)).isoformat()
        logs = self.logger.get_logs(start_time=start_time, limit=5000)
        
        if not logs:
            fig = go.Figure()
            fig.add_annotation(
                text="Nenhum log encontrado no período",
                xref="paper", yref="paper",
                x=0.5, y=0.5, showarrow=False
            )
            return fig
        
        df = pd.DataFrame(logs)
        df['timestamp'] = pd.to_datetime(df['timestamp'])
        
        # Contar logs por hora e nível
        df['hour'] = df['timestamp'].dt.floor('H')
        log_counts = df.groupby(['hour', 'level']).size().reset_index(name='count')
        
        fig = go.Figure()
        
        colors = {
            'DEBUG': '#6c757d',
            'INFO': '#17a2b8',
            'WARNING': '#ffc107',
            'ERROR': '#dc3545',
            'CRITICAL': '#6f42c1'
        }
        
        for level in log_counts['level'].unique():
            level_data = log_counts[log_counts['level'] == level]
            
            fig.add_trace(go.Bar(
                x=level_data['hour'],
                y=level_data['count'],
                name=level,
                marker_color=colors.get(level, '#000000')
            ))
        
        fig.update_layout(
            title="Distribuição de Logs por Hora e Nível",
            xaxis_title="Hora",
            yaxis_title="Quantidade de Logs",
            barmode='stack'
        )
        
        return fig
    
    def search_logs(self, search_term: str, hours_back: int) -> str:
        """Busca logs por termo específico"""
        
        start_time = (datetime.now() - timedelta(hours=hours_back)).isoformat()
        all_logs = self.logger.get_logs(start_time=start_time, limit=10000)
        
        if not all_logs:
            return "<p>Nenhum log encontrado.</p>"
        
        # Filtrar logs que contêm o termo de busca
        filtered_logs = [
            log for log in all_logs 
            if search_term.lower() in log['message'].lower() or 
               search_term.lower() in log['module'].lower() or
               search_term.lower() in log['function'].lower()
        ]
        
        if not filtered_logs:
            return f"<p>Nenhum log encontrado com o termo '{search_term}'.</p>"
        
        df = pd.DataFrame(filtered_logs)
        df['timestamp'] = pd.to_datetime(df['timestamp'])
        
        # Preparar dados para exibição
        display_df = df[['timestamp', 'level', 'category', 'message', 'module', 'function']].copy()
        display_df['timestamp'] = display_df['timestamp'].dt.strftime('%Y-%m-%d %H:%M:%S')
        
        # Destacar termo de busca
        def highlight_term(text, term):
            if pd.isna(text):
                return text
            return str(text).replace(term, f'<mark>{term}</mark>')
        
        for col in ['message', 'module', 'function']:
            display_df[col] = display_df[col].apply(lambda x: highlight_term(x, search_term))
        
        html_table = display_df.to_html(escape=False, index=False, classes='table table-striped')
        
        return f"""
        <div style="max-height: 600px; overflow-y: auto;">
            <p><strong>Encontrados {len(filtered_logs)} logs com o termo '{search_term}'</strong></p>
            {html_table}
        </div>
        """
    
    def export_logs(self, level_filter: str, category_filter: str, 
                   hours_back: int, format_type: str) -> str:
        """Exporta logs em diferentes formatos"""
        
        df = self.get_log_data(level_filter, category_filter, hours_back, 10000)
        
        if df.empty:
            return "Nenhum log para exportar."
        
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        
        if format_type == "CSV":
            filename = f"logs_export_{timestamp}.csv"
            df.to_csv(filename, index=False)
            return f"Logs exportados para {filename}"
        
        elif format_type == "JSON":
            filename = f"logs_export_{timestamp}.json"
            df.to_json(filename, orient='records', date_format='iso')
            return f"Logs exportados para {filename}"
        
        elif format_type == "HTML":
            filename = f"logs_export_{timestamp}.html"
            html_content = f"""
            <!DOCTYPE html>
            <html>
            <head>
                <title>Relatório de Logs - {timestamp}</title>
                <style>
                    body {{ font-family: Arial, sans-serif; margin: 20px; }}
                    table {{ border-collapse: collapse; width: 100%; }}
                    th, td {{ border: 1px solid #ddd; padding: 8px; text-align: left; }}
                    th {{ background-color: #f2f2f2; }}
                    .debug {{ color: #6c757d; }}
                    .info {{ color: #17a2b8; }}
                    .warning {{ color: #ffc107; }}
                    .error {{ color: #dc3545; }}
                    .critical {{ color: #6f42c1; }}
                </style>
            </head>
            <body>
                <h1>Relatório de Logs</h1>
                <p>Gerado em: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}</p>
                <p>Total de registros: {len(df)}</p>
                {df.to_html(escape=False, index=False, classes='table')}
            </body>
            </html>
            """
            
            with open(filename, 'w', encoding='utf-8') as f:
                f.write(html_content)
            
            return f"Relatório HTML exportado para {filename}"
        
        return "Formato não suportado."

def create_log_viewer_interface() -> gr.Blocks:
    """Cria a interface do visualizador de logs"""
    
    viewer = LogViewer()
    
    with gr.Blocks(title="📊 Visualizador de Logs") as interface:
        gr.Markdown("# 📊 Sistema de Visualização de Logs")
        
        with gr.Tab("📋 Logs Recentes"):
            with gr.Row():
                with gr.Column(scale=1):
                    level_filter = gr.Dropdown(
                        choices=["ALL"] + [level.value for level in LogLevel],
                        value="ALL",
                        label="Filtrar por Nível"
                    )
                    category_filter = gr.Dropdown(
                        choices=["ALL"] + [cat.value for cat in LogCategory],
                        value="ALL",
                        label="Filtrar por Categoria"
                    )
                    hours_back = gr.Slider(
                        minimum=1, maximum=168, value=24, step=1,
                        label="Horas Anteriores"
                    )
                    limit = gr.Slider(
                        minimum=10, maximum=5000, value=100, step=10,
                        label="Limite de Registros"
                    )
                    refresh_btn = gr.Button("🔄 Atualizar", variant="primary")
                
                with gr.Column(scale=3):
                    log_table = gr.HTML(label="Logs")
            
            refresh_btn.click(
                fn=viewer.create_log_table,
                inputs=[level_filter, category_filter, hours_back, limit],
                outputs=log_table
            )
        
        with gr.Tab("📊 Estatísticas"):
            with gr.Row():
                stats_hours = gr.Slider(
                    minimum=1, maximum=168, value=24, step=1,
                    label="Período (horas)"
                )
                stats_refresh_btn = gr.Button("🔄 Atualizar Estatísticas", variant="primary")
            
            stats_html = gr.HTML(label="Estatísticas")
            
            stats_refresh_btn.click(
                fn=viewer.create_log_statistics,
                inputs=[stats_hours],
                outputs=stats_html
            )
        
        with gr.Tab("📈 Gráficos"):
            with gr.Row():
                chart_hours = gr.Slider(
                    minimum=1, maximum=168, value=24, step=1,
                    label="Período (horas)"
                )
                chart_refresh_btn = gr.Button("🔄 Atualizar Gráficos", variant="primary")
            
            with gr.Row():
                timeline_chart = gr.Plot(label="Timeline de Logs")
                performance_chart = gr.Plot(label="Métricas de Performance")
            
            chart_refresh_btn.click(
                fn=lambda hours: (viewer.create_log_timeline_chart(hours), viewer.create_performance_chart(hours)),
                inputs=[chart_hours],
                outputs=[timeline_chart, performance_chart]
            )
        
        with gr.Tab("🔍 Busca"):
            with gr.Row():
                search_term = gr.Textbox(label="Termo de Busca", placeholder="Digite o termo para buscar...")
                search_hours = gr.Slider(
                    minimum=1, maximum=168, value=24, step=1,
                    label="Período (horas)"
                )
                search_btn = gr.Button("🔍 Buscar", variant="primary")
            
            search_results = gr.HTML(label="Resultados da Busca")
            
            search_btn.click(
                fn=viewer.search_logs,
                inputs=[search_term, search_hours],
                outputs=search_results
            )
        
        with gr.Tab("📤 Exportar"):
            with gr.Row():
                export_level = gr.Dropdown(
                    choices=["ALL"] + [level.value for level in LogLevel],
                    value="ALL",
                    label="Filtrar por Nível"
                )
                export_category = gr.Dropdown(
                    choices=["ALL"] + [cat.value for cat in LogCategory],
                    value="ALL",
                    label="Filtrar por Categoria"
                )
                export_hours = gr.Slider(
                    minimum=1, maximum=168, value=24, step=1,
                    label="Período (horas)"
                )
                export_format = gr.Dropdown(
                    choices=["CSV", "JSON", "HTML"],
                    value="CSV",
                    label="Formato"
                )
                export_btn = gr.Button("📤 Exportar", variant="primary")
            
            export_result = gr.Textbox(label="Resultado da Exportação")
            
            export_btn.click(
                fn=viewer.export_logs,
                inputs=[export_level, export_category, export_hours, export_format],
                outputs=export_result
            )
        
        # Carregar dados iniciais
        interface.load(
            fn=lambda: (viewer.create_log_table("ALL", "ALL", 24, 100), viewer.create_log_statistics(24)),
            outputs=[log_table, stats_html]
        )
    
    return interface