import json
import debugpy
from django.shortcuts import render
from elasticsearch_dsl import Search, Q
from elasticsearch_dsl.connections import connections
from django.conf import settings
import re
from .documents import PDFDocument
from django.http import JsonResponse


# Configuração da conexão com o Elasticsearch
connections.create_connection(hosts=[settings.ELASTICSEARCH_HOSTS])


def spellcheck_view(request):
    query = request.GET.get("q", "")
    suggestions = []

    if query:
        s = Search(index="pdf_documents")
        s = s.suggest(
            "auto_correct",
            query,
            phrase={
                "field": "suggest",
                "size": 3,
                "gram_size": 3,
                "confidence": 2.0,
                "direct_generator": [{"field": "suggest", "suggest_mode": "popular"}],
            },
        )
        response = s.execute()

        if hasattr(response.suggest, "auto_correct"):
            for option in response.suggest.auto_correct[0].options:
                suggestions.append(option.text)

    return JsonResponse({"suggestions": suggestions})

def search_view(request):
    query = request.GET.get('q', '')  # Obtém o termo de pesquisa da URL
    page = int(request.GET.get('page', 1))

    results = []
    suggestions = []
    spelling_correction = None
    total_hits = 0
    per_page = 10

    if query:
        # Processamento especial para termos entre aspas
        exact_phrases = re.findall(r'"([^"]*)"', query)

        # Remove os termos entre aspas da consulta principal
        cleaned_query = query
        for phrase in exact_phrases:
            cleaned_query = cleaned_query.replace(f'"{phrase}"', '')

        # Remove espaços extras e pontuação desnecessária
        cleaned_query = re.sub(r'\s+', ' ', cleaned_query).strip()

        # Cria uma consulta no Elasticsearch
        search = Search(index='diarios_oficiais')

        # Lista para armazenar todas as consultas
        queries = []

        # Adiciona consulta para termos gerais (com fuzziness para tolerância a erros)
        if cleaned_query:
            queries.append(
                Q('multi_match',
                  query=cleaned_query,
                  fields=['title^3', 'pages.content^2'],
                  fuzziness='AUTO',
                  boost=2)
            )

        # Adiciona consultas exatas para frases entre aspas (sem fuzziness)
        for phrase in exact_phrases:
            if phrase.strip():
                queries.append(
                    Q('match_phrase',
                      pages__content={
                          'query': phrase,
                          'boost': 2,
                          'slop': 0  # Sem flexibilidade na ordem das palavras
                      })
                )

        # Combina as consultas com OR (se houver alguma)
        if queries:
            search = search.query(
                Q('bool', should=queries, minimum_should_match=1)
            )

            # Configuração do highlight para mostrar mais contexto
            search = search.highlight(
                'pages.content',
                fragment_size=300,
                number_of_fragments=2,
                pre_tags=['<mark>'],
                post_tags=['</mark>']
            )

            # Paginação
            search = search[(page-1)*per_page:page*per_page]

            # Executa a consulta
            response = search.execute()
            total_hits = response.hits.total.value

            # Processa os resultados
            for hit in response:
                # Extrai o conteúdo destacado ou usa o original
                if hasattr(hit.meta, 'highlight') and hasattr(hit.meta.highlight, 'pages.content'):
                    highlighted_content = ' ... '.join(hit.meta.highlight['pages.content'])
                else:
                    highlighted_content = ""

                # Extrai o título destacado ou usa o original
                if hasattr(hit.meta, 'highlight') and hasattr(hit.meta.highlight, 'title'):
                    highlighted_title = hit.meta.highlight.title[0]
                else:
                    highlighted_title = hit.title

                # Verifica se o resultado corresponde a uma frase exata
                is_exact_match = any(phrase.lower() in hit.pages.content.lower() or
                                    phrase.lower() in hit.title.lower()
                                    for phrase in exact_phrases)

                results.append({
                    'id': hit.meta.id,
                    'title': hit.title,
                    'highlighted_title': highlighted_title,
                    'highlighted_content': highlighted_content,
                    'data': hit.data,
                    'numero': hit.numero,
                    'link': hit.link,
                    'finalizado': hit.finalizado,
                    'is_exact_match': is_exact_match
                })

    # Calcula a paginação
    total_pages = (total_hits + per_page - 1) // per_page if total_hits > 0 else 0

    # Renderiza o template com os resultados
    return render(request, 'diarios/search_results.html', {
        'query': query,
        'results': results,
        'suggestions': suggestions[:5],  # Limita a 5 sugestões
        'spelling_correction': spelling_correction,
        'total_hits': total_hits,
        'page': page,
        'total_pages': total_pages,
        'page_range': range(max(1, page-2), min(total_pages+1, page+3)),
        'has_exact_phrases': bool(exact_phrases)
    })