Building a daily productivity app with Pieces — Part 2: Adding AI Intelligence with Gemini

// lib/models/daily_recap_models.dart

// Don't forget the imports!
import 'dart:convert';
import 'package:google_generative_ai/google_generative_ai.dart';
import '../models/daily_recap_models.dart';

SummaryWithContent {
  id: "4f302bfd-f3c2-4f85-aa79-e7cb314e111d",
  title: "Implemented WebSocket sync",
  content: "# Project XYZ\nFixed critical authentication bug in OAuth token refresh.
            Implemented real-time WebSocket synchronization with automatic 
            reconnection. Pair programmed with Bob on the WebSocket integration...",
  timestamp: 2025-11-04 14:32:15
}

SUMMARY:
   Successfully fixed critical authentication bug and implemented 
   real-time WebSocket synchronization for better data flow.

PROJECTS:
   ✅ Authentication Service [completed]
      Fixed OAuth token refresh logic

   🔄 WebSocket Integration [in_progress]
      Implemented real-time sync with automatic reconnection

PEOPLE WORKED WITH:
   • Alice (code review)
   • Bob (pair programming)

REMINDERS:
   ⚠️  Test WebSocket with production load
   ⚠️  Update documentation for new auth flow
NOTES:
  - Send a message in Google Chat about the progress

dependencies:
  google_generative_ai: ^0.4.6

class DailyRecapService {
  final GenerativeModel _model;

  DailyRecapService({required String apiKey})
      : _model = GenerativeModel(
          model: 'gemini-2.5-flash-lite',  // Fast, efficient, and cost-effective!
          apiKey: apiKey,
          generationConfig: GenerationConfig(
            temperature: 0.7,  // Balanced creativity
            topK: 40,
            topP: 0.95,
            maxOutputTokens: 2048,
            responseMimeType: 'application/json',
          ),
        );

  Future<DailyRecapData> generateDailyRecap({
    required DateTime date,
    required List<SummaryWithContent> summaries,
  }) async {
    // We'll build this step by step!
  }
}

Analyze these summaries and tell me what I did today

Return JSON with: summary, projects, people

Extract and organize into this EXACT JSON format:
{
  "summary": "Brief 1-2 sentence overview",
  "people": ["Person1", "Person2"],
  "projects": [
    {
      "name": "Project Name",
      "description": "What was done",
      "status": "in_progress"  // or "completed" or "not_started"
    }
  ],
  "reminders": ["Reminder 1"],
  "notes": ["Important insight"]
}

// lib/services/daily_recap_service.dart

  // Add below DailyRecapService() and above Future<>


  /// Build the prompt for Gemini
  String _buildPrompt(DateTime date, String context) {
    final dateStr =
        '${date.year}-${date.month.toString().padLeft(2, '0')}-${date.day.toString().padLeft(2, '0')}';

    return '''You are an AI assistant analyzing a developer's workstream summaries for the day: $dateStr.

Based on the following workstream summaries, extract and organize the information into a structured daily recap.

WORKSTREAM SUMMARIES:
$context

Your task is to analyze these summaries and create a comprehensive daily recap with the following information:

1. **summary** (string, 1-2 sentences): A brief overview of what was accomplished today. Focus on the main achievements and work done.

2. **people** (array of strings): List of people mentioned or collaborated with. Look for names, @mentions, or collaboration indicators. Can be empty if no one is mentioned.

3. **projects** (array of objects): Projects worked on today. Each project should have:
   - **name** (string): Project or feature name
   - **description** (string): Brief description of what was done
   - **status** (string): One of: "completed", "in_progress", or "not_started"

4. **reminders** (array of strings): Action items, TODOs, or things to remember for later. Look for phrases like "need to", "should", "TODO", "remember to", etc. Can be empty.

5. **notes** (array of strings): Important observations, learnings, or technical notes from the day. Look for insights, discoveries, or important information. Can be empty.

IMPORTANT GUIDELINES:
- Be concise but informative
- Extract actual information from the summaries, don't make things up
- If a category has no relevant information, use an empty array [] or empty string ""
- For project status: use "completed" if the work is done, "in_progress" if actively working on it, "not_started" if mentioned but not begun
- People names should be just the name (e.g., "Alice", "Bob")
- Keep descriptions clear and specific

Return ONLY valid JSON in this exact format:
{
  "summary": "Brief 1-2 sentence overview",
  "people": ["Person1", "Person2"],
  "projects": [
    {
      "name": "Project Name",
      "description": "What was done",
      "status": "in_progress"
    }
  ],
  "reminders": ["Reminder 1", "Reminder 2"],
  "notes": ["Note 1", "Note 2"]
}
''';
  }

 // lib/services/daily_recap_service.dart

 // Add below _buildPrompt() and above generateDailyRecap()

 String _buildSummariesContext(List<SummaryWithContent> summaries) {
    final buffer = StringBuffer();

    for (int i = 0; i < summaries.length; i++) {
      final summary = summaries[i];
      buffer.writeln('Summary ${i + 1}:');
      buffer.writeln('  ID: ${summary.id}');
      buffer.writeln('  Title: ${summary.title}');
      buffer.writeln(
        '  Time: ${summary.timestamp.hour.toString().padLeft(2, '0')}:${summary.timestamp.minute.toString().padLeft(2, '0')}',
      );
      buffer.writeln('  Content: ${summary.content}');
      buffer.writeln();
    }

    return buffer.toString();
  }

// lib/services/daily_recap_service.dart

// Add below _buildPrompt() and above generateDailyRecap() 
 factory DailyRecapData.empty(DateTime date) {
    return DailyRecapData(
      date: date,
      summary: '',
      people: [],
      projects: [],
      reminders: [],
      notes: [],
    );
  }

// lib/services/daily_recap_service.dart

// Replace the comment in generateDailyRecap() with this:

try {
  final response = await _model.generateContent([Content.text(prompt)]);
  print("Gemini response received. ${response.text}");  // Debug output
  final rawText = response.text ?? '{}';
      
  // Extract JSON from markdown code blocks
  final jsonText = _extractJsonFromMarkdown(rawText);
  final data = jsonDecode(jsonText) as Map<String, dynamic>;

  return DailyRecapData.fromJson(date, data); // ... generate recap
} catch (e) {
  print('Error generating recap: $e');
  return DailyRecapData.empty(date);  // Safe fallback
}

Future<DailyRecapData> generateDailyRecap({
  required DateTime date,
  required List<SummaryWithContent> summaries,
}) async {
  // Step 1: Handle edge case - no summaries
  if (summaries.isEmpty) {
    return DailyRecapData.empty(date);
  }

  // Step 2: Build context from summaries using our helper method
  final context = _buildSummariesContext(summaries);

  // Step 3: Craft the prompt using our prompt builder
  final prompt = _buildPrompt(date, context);

  try {
    // Step 4: Send to Gemini and get response
    final response = await _model.generateContent([Content.text(prompt)]);
    print("Gemini response received. ${response.text}");
    
    // Step 5: Parse the JSON response
    final jsonText = response.text ?? '{}';
    final data = jsonDecode(jsonText) as Map<String, dynamic>;

    // Step 6: Convert to our data model and return
    return DailyRecapData.fromJson(date, data);
  } catch (e) {
    print('Error generating daily recap: $e');
    rethrow; // Let the UI handle the error
  }
}

// lib/models/daily_recap_models.dart

// Add before SummaryWithContent class and after ProjectStatus {}

class ProjectData {
  final String name;
  final String description;
  final ProjectStatus status;  // enum: completed, inProgress, notStarted

  ProjectData({
    required this.name,
    required this.description,
    required this.status,
  });

  factory ProjectData.fromJson(Map<String, dynamic> json) {
    return ProjectData(
      name: json['name'] as String,
      description: json['description'] as String,
      status: _statusFromString(json['status'] as String),
    );
  }

  static ProjectStatus _statusFromString(String status) {
    switch (status) {
      case 'completed':
        return ProjectStatus.completed;
      case 'in_progress':
        return ProjectStatus.inProgress;
      case 'not_started':
        return ProjectStatus.notStarted;
      default:
        return ProjectStatus.notStarted;
    }
  }
}

class DailyRecapData {
  final DateTime date;
  final String summary;
  final List<String> people;
  final List<ProjectData> projects;
  final List<String> reminders;
  final List<String> notes;

  DailyRecapData({
    required this.date,
    required this.summary,
    required this.people,
    required this.projects,
    required this.reminders,
    required this.notes,
  });

  factory DailyRecapData.fromJson(DateTime date, Map<String, dynamic> json) {
    return DailyRecapData(
      date: date,
      summary: json['summary'] as String? ?? '',
      people: (json['people'] as List<dynamic>?)
              ?.map((e) => e as String)
              .toList() ??
          [],
      projects: (json['projects'] as List<dynamic>?)
              ?.map((e) => ProjectData.fromJson(e as Map<String, dynamic>))
              .toList() ??
          [],
      reminders: (json['reminders'] as List<dynamic>?)
              ?.map((e) => e as String)
              .toList() ??
          [],
      notes: (json['notes'] as List<dynamic>?)
              ?.map((e) => e as String)
              .toList() ??
          [],
    );
  }
}

{
  "summary": "Today's work focused on enhancing user experience for video content, developing a tag generator, testing Flutter capabilities, and reviewing code and infrastructure. Significant progress was made on persona generation for AI training data.",
  "people": [],
  "projects": [
    {
      "name": "Video Analytics & User Experience",
      "description": "Analyzed YouTube video analytics for 'Pieces' content and discussed strategies for improving new user experience by leveraging context and memory,
 considering phased UI exposure and temporary access keys.",
      "status": "in_progress"
    },
    {
      "name": "Tag Generator",
      "description": "Generated a Python script for thematic tagging.",
      "status": "completed"
    },
    {
      "name": "Flutter macOS Dynamic Library Loading",
      "description": "Demonstrated Flutter's macOS dynamic library loading and confirmed clipboard monitoring functionality and its integration with long-term memory.",
      "status": "in_progress"
    },
    {
      "name": "AI Persona Generation",
      "description": "Discussed UI for AI persona generation and initiated content compilation for a partner. Presented the 'persona-query-tag-dataset-gen' project, det
ailing the creation of realistic user personas for the Pieces AI assistant, showcasing comprehensive attributes and providing an example of 'Anja Vestergaard'.",
      "status": "in_progress"
    },
    {
      "name": "ML Training & Django API",
      "description": "Addressed an `UnboundLocalError` in ML training and validated nested task management for a Django API.",
      "status": "completed"
    },
    {
      "name": "Infrastructure Upgrades & Containerization",
      "description": "Reviewed infrastructure upgrades, containerization strategies, cost optimizations, and bug fixes across multiple services, including timezone and 
user invitation flows.",
      "status": "in_progress"
    }
  ],
  "reminders": [],
  "notes": [
    "Leveraging context and memory for new user experience improvements.",
    "Clipboard monitoring functionality confirmed and integrated with long-term memory.",
    "Objective for persona generation project is to generate authentic training data for the AI assistant."
  ]
}

class DailyRecapService {
  final GenerativeModel _model;
  late final Box<DailyRecapData> _cacheBox;

  DailyRecapService({required String apiKey})
    : _model = GenerativeModel(
        model: 'gemini-2.5-flash-lite',
        apiKey: apiKey,
        generationConfig: GenerationConfig(
          temperature: 0.7,
          topK: 40,
          topP: 0.95,
          maxOutputTokens: 2048,
          responseMimeType: 'application/json',
        ),
      );


  /// Generate a daily recap from workstream summaries with their content
  /// Set forceRegenerate to true to bypass cache
  Future<DailyRecapData> generateDailyRecap({
    required DateTime date,
    required List<SummaryWithContent> summaries,
  }) async {
    // Check cache first (unless forcing regeneration)
    if (summaries.isEmpty) {
      return DailyRecapData.empty(date);
    }

    // Build context from summaries
    final context = _buildSummariesContext(summaries);

    // Craft the prompt
    final prompt = _buildPrompt(date, context);

    try {
      final response = await _model.generateContent([Content.text(prompt)]);
      // ignore: avoid_print
      print("Gemini response received. ${response.text}");
      final jsonText = response.text ?? '{}';

      // Parse the JSON response
      final data = jsonDecode(jsonText) as Map<String, dynamic>;

      final recap = DailyRecapData.fromJson(date, data);

      return recap;
    } catch (e) {
      // ignore: avoid_print
      print('Error generating daily recap: $e');
      rethrow; // Throw error so UI can handle it
    }
  }

  /// Build context string from summaries with their content
  String _buildSummariesContext(List<SummaryWithContent> summaries) {
    final buffer = StringBuffer();

    for (int i = 0; i < summaries.length; i++) {
      final summary = summaries[i];
      buffer.writeln('Summary ${i + 1}:');
      buffer.writeln('  ID: ${summary.id}');
      buffer.writeln('  Title: ${summary.title}');
      buffer.writeln(
        '  Time: ${summary.timestamp.hour.toString().padLeft(2, '0')}:${summary.timestamp.minute.toString().padLeft(2, '0')}',
      );
      buffer.writeln('  Content: ${summary.content}');
      buffer.writeln();
    }

    return buffer.toString();
  }

/// Extract JSON from markdown code blocks
  String _extractJsonFromMarkdown(String text) {
    // Remove markdown code block formatting
    String cleaned = text.trim();
    
    // Remove leading ```json or ```
    if (cleaned.startsWith('```')) {
      cleaned = cleaned.replaceFirst(RegExp(r'^```(?:json)?\s*'), '');
    }
    
    // Remove trailing ```
    if (cleaned.endsWith('```')) {
      cleaned = cleaned.replaceFirst(RegExp(r'\s*```$'), '');
    }
    
    return cleaned.trim();
  }

  /// Build the prompt for Gemini
  String _buildPrompt(DateTime date, String context) {
    final dateStr =
        '${date.year}-${date.month.toString().padLeft(2, '0')}-${date.day.toString().padLeft(2, '0')}';

    return '''You are an AI assistant analyzing a developer's workstream summaries for the day: $dateStr.

Based on the following workstream summaries, extract and organize the information into a structured daily recap.

WORKSTREAM SUMMARIES:
$context

Your task is to analyze these summaries and create a comprehensive daily recap with the following information:

1. **summary** (string, 1-2 sentences): A brief overview of what was accomplished today. Focus on the main achievements and work done.

2. **people** (array of strings): List of people mentioned or collaborated with. Look for names, @mentions, or collaboration indicators. Can be empty if no one is mentioned.

3. **projects** (array of objects): Projects worked on today. Each project should have:
   - **name** (string): Project or feature name
   - **description** (string): Brief description of what was done
   - **status** (string): One of: "completed", "in_progress", or "not_started"

4. **reminders** (array of strings): Action items, TODOs, or things to remember for later. Look for phrases like "need to", "should", "TODO", "remember to", etc. Can be empty.

5. **notes** (array of strings): Important observations, learnings, or technical notes from the day. Look for insights, discoveries, or important information. Can be empty.

IMPORTANT GUIDELINES:
- Be concise but informative
- Extract actual information from the summaries, don't make things up
- If a category has no relevant information, use an empty array [] or empty string ""
- For project status: use "completed" if the work is done, "in_progress" if actively working on it, "not_started" if mentioned but not begun
- People names should be just the name (e.g., "Alice", "Bob")
- Keep descriptions clear and specific

Return ONLY valid JSON in this exact format:
{
  "summary": "Brief 1-2 sentence overview",
  "people": ["Person1", "Person2"],
  "projects": [
    {
      "name": "Project Name",
      "description": "What was done",
      "status": "in_progress"
    }
  ],
  "reminders": ["Reminder 1", "Reminder 2"],
  "notes": ["Note 1", "Note 2"]
}
''';
  }
}