rely/lib/core/llm/llm_service.dart

import 'dart:convert';

import 'package:dio/dio.dart';
import 'package:flutter_riverpod/flutter_riverpod.dart';
import 'package:relationship_saver/core/llm/captured_fact_draft_parser.dart';
import 'package:relationship_saver/core/llm/llm_config.dart';
import 'package:relationship_saver/features/people/domain/person_models.dart';
import 'package:relationship_saver/features/share_intake/domain/share_models.dart';

class LlmService {
  LlmService(this._ref);

  final Ref _ref;

  Future<LlmConfigState> get _config async {
    final state = _ref.read(llmConfigProvider);
    if (!state.isConfigured) {
      throw Exception('LLM not configured');
    }
    return state;
  }

  Future<List<GeneratedSignal>> generateSignals(
    List<PersonProfile> people,
  ) async {
    final config = await _config;
    final apiKey = await _ref.read(llmConfigProvider.notifier).getApiKey();

    if (apiKey == null || apiKey.isEmpty) {
      throw Exception('No API key available');
    }

    final systemPrompt =
        '''You are a relationship assistant that helps users maintain meaningful connections with their friends and family.
Based on the person's profile data, generate helpful signals such as:
- Check-in reminders
- Gift ideas
- Follow-up suggestions
- Relationship maintenance tips

Return your response as a JSON array of signals, each with:
- title: short actionable title
- description: brief explanation
- type: "recommendation", "gift", "followup", or "reminder"
- personId: the person's ID (use a valid ID from the input if applicable)

Only return the JSON array, no other text.''';

    final peopleContext = people
        .map(
          (p) =>
              '- ${p.name} (${p.relationship}): tags=${p.tags.join(", ")}, notes=${p.notes.isEmpty ? "none" : p.notes}, affinity=${p.affinityScore}',
        )
        .join('\n');

    final userPrompt =
        '''Here are the people in my life:
$peopleContext

Generate 3-5 helpful relationship signals for these people.''';

    try {
      final response = await _callLlm(
        provider: config.provider,
        apiKey: apiKey,
        systemPrompt: systemPrompt,
        userPrompt: userPrompt,
      );

      return _parseSignalsResponse(response, people);
    } catch (e) {
      throw Exception('Failed to generate signals: $e');
    }
  }

  Future<String> completeText({
    required String systemPrompt,
    required String userPrompt,
  }) async {
    final config = await _config;
    final String? apiKey = await _ref
        .read(llmConfigProvider.notifier)
        .getApiKey();

    if (apiKey == null || apiKey.isEmpty) {
      throw Exception('No API key available');
    }

    return _callLlm(
      provider: config.provider,
      apiKey: apiKey,
      systemPrompt: systemPrompt,
      userPrompt: userPrompt,
    );
  }

  Future<CapturedFactDraft?> suggestCaptureDraft(SharedPayload payload) async {
    final config = await _config;
    final String? apiKey = await _ref
        .read(llmConfigProvider.notifier)
        .getApiKey();

    if (apiKey == null || apiKey.isEmpty) {
      throw Exception('No API key available');
    }

    final String systemPrompt =
        '''You analyze shared messages and links for a relationship-tracking app.
Return exactly one JSON object with:
- type: one of "note", "like", "dislike", "importantDate", "giftIdea", "placeIdea", "activityIdea", "misc"
- text: cleaned user-facing summary
- label: short optional label
- dateValue: ISO-8601 date if the content clearly describes an important date, otherwise null
- confidence: number from 0 to 1
- isSensitive: true only if the content looks private or sensitive

Be conservative. If the content is ambiguous, prefer "note". Return JSON only.''';

    final String userPrompt =
        '''Source app: ${payload.sourceApp}
Sender: ${payload.sourceDisplayName ?? "unknown"}
Platform: ${payload.platform}
URL: ${payload.url ?? "none"}
Raw text:
${payload.rawText}''';

    try {
      final String response = await _callLlm(
        provider: config.provider,
        apiKey: apiKey,
        systemPrompt: systemPrompt,
        userPrompt: userPrompt,
      );
      return parseCapturedFactDraftSuggestion(
        response,
        fallbackPayload: payload,
      );
    } catch (e) {
      throw Exception('Failed to suggest share capture draft: $e');
    }
  }

  Future<String> _callLlm({
    required LlmProvider provider,
    required String apiKey,
    required String systemPrompt,
    required String userPrompt,
  }) async {
    final dio = Dio(
      BaseOptions(
        connectTimeout: const Duration(seconds: 20),
        receiveTimeout: const Duration(seconds: 20),
        sendTimeout: const Duration(seconds: 20),
      ),
    );

    switch (provider) {
      case LlmProvider.openai:
        return _callOpenAI(dio, apiKey, systemPrompt, userPrompt);
      case LlmProvider.anthropic:
        return _callAnthropic(dio, apiKey, systemPrompt, userPrompt);
      case LlmProvider.google:
        return _callGoogleAI(dio, apiKey, systemPrompt, userPrompt);
    }
  }

  Future<String> _callOpenAI(
    Dio dio,
    String apiKey,
    String systemPrompt,
    String userPrompt,
  ) async {
    final Response<Map<String, dynamic>> response = await dio
        .post<Map<String, dynamic>>(
          'https://api.openai.com/v1/chat/completions',
          options: Options(
            headers: {
              'Authorization': 'Bearer $apiKey',
              'Content-Type': 'application/json',
            },
          ),
          data: jsonEncode({
            'model': 'gpt-4o-mini',
            'messages': [
              {'role': 'system', 'content': systemPrompt},
              {'role': 'user', 'content': userPrompt},
            ],
            'max_tokens': 1000,
          }),
        );

    final Map<String, dynamic> responseData = response.data!;
    final List<dynamic> choices = responseData['choices'] as List<dynamic>;
    final Map<String, dynamic> firstChoice = choices[0] as Map<String, dynamic>;
    final Map<String, dynamic> message =
        firstChoice['message'] as Map<String, dynamic>;
    final String content = message['content'] as String;
    return content;
  }

  Future<String> _callAnthropic(
    Dio dio,
    String apiKey,
    String systemPrompt,
    String userPrompt,
  ) async {
    final Response<Map<String, dynamic>> response = await dio
        .post<Map<String, dynamic>>(
          'https://api.anthropic.com/v1/messages',
          options: Options(
            headers: {
              'x-api-key': apiKey,
              'anthropic-version': '2023-06-01',
              'Content-Type': 'application/json',
            },
          ),
          data: jsonEncode({
            'model': 'claude-3-haiku-20240307',
            'system': systemPrompt,
            'messages': [
              {'role': 'user', 'content': userPrompt},
            ],
            'max_tokens': 1000,
          }),
        );

    final Map<String, dynamic> responseData = response.data!;
    final List<dynamic> contentList = responseData['content'] as List<dynamic>;
    final Map<String, dynamic> firstContent =
        contentList[0] as Map<String, dynamic>;
    final String content = firstContent['text'] as String;
    return content;
  }

  Future<String> _callGoogleAI(
    Dio dio,
    String apiKey,
    String systemPrompt,
    String userPrompt,
  ) async {
    final Response<Map<String, dynamic>>
    response = await dio.post<Map<String, dynamic>>(
      'https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent',
      options: Options(headers: {'Content-Type': 'application/json'}),
      queryParameters: {'key': apiKey},
      data: jsonEncode({
        'systemInstruction': {
          'parts': [
            {'text': systemPrompt},
          ],
        },
        'contents': [
          {
            'parts': [
              {'text': userPrompt},
            ],
          },
        ],
        'generationConfig': {'temperature': 0.7, 'maxOutputTokens': 1000},
      }),
    );

    final Map<String, dynamic> responseData = response.data!;
    final List<dynamic> candidates =
        responseData['candidates'] as List<dynamic>;
    final Map<String, dynamic> firstCandidate =
        candidates[0] as Map<String, dynamic>;
    final Map<String, dynamic> content =
        firstCandidate['content'] as Map<String, dynamic>;
    final List<dynamic> parts = content['parts'] as List<dynamic>;
    final Map<String, dynamic> firstPart = parts[0] as Map<String, dynamic>;
    final String contentText = firstPart['text'] as String;
    return contentText;
  }

  List<GeneratedSignal> _parseSignalsResponse(
    String response,
    List<PersonProfile> people,
  ) {
    try {
      final jsonStart = response.indexOf('[');
      final jsonEnd = response.lastIndexOf(']');

      if (jsonStart == -1 || jsonEnd == -1) {
        return [];
      }

      final jsonStr = response.substring(jsonStart, jsonEnd + 1);
      final List<dynamic> items = jsonDecode(jsonStr) as List<dynamic>;

      return items.map((item) {
        final Map<String, dynamic> itemMap = item as Map<String, dynamic>;
        final String? personId = itemMap['personId'] as String?;
        final validPersonId =
            personId != null && people.any((p) => p.id == personId)
            ? personId
            : (people.isNotEmpty ? people.first.id : null);

        return GeneratedSignal(
          title: itemMap['title'] as String? ?? 'Check in',
          description: itemMap['description'] as String? ?? '',
          type: itemMap['type'] as String? ?? 'recommendation',
          personId: validPersonId,
        );
      }).toList();
    } catch (e) {
      return [];
    }
  }
}

class GeneratedSignal {
  const GeneratedSignal({
    required this.title,
    required this.description,
    required this.type,
    this.personId,
  });

  final String title;
  final String description;
  final String type;
  final String? personId;
}

final llmServiceProvider = Provider<LlmService>((Ref ref) {
  return LlmService(ref);
});