AI Improvement Review & Collaboration

03_ai_improvement_review.livemd

Sean Chatman

@seanchatmangpt

ai-self-sustaining-system

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AI Improvement Review & Collaboration

Mix.install([
  {:kino, "~> 0.13.0"},
  {:kino_vega_lite, "~> 0.1.11"},
  {:kino_db, "~> 0.2.7"},
  {:vega_lite, "~> 0.1.9"},
  {:explorer, "~> 0.8.0"}
])

Introduction

This notebook enables collaborative review and validation of AI-suggested improvements for the self-sustaining system. Teams can analyze improvement proposals, validate their effectiveness, and make data-driven decisions about implementation.

Connect to Phoenix Application

# Connect to the Phoenix application node
node = :"self_sustaining@localhost"
Node.connect(node)

# Get the integration module
alias SelfSustaining.LivebookIntegration
alias Explorer.DataFrame, as: DF
alias VegaLite, as: Vl

Load AI Improvement Data

# Load AI improvement data
ai_data = LivebookIntegration.get_ai_improvements_data()

Kino.Markdown.new("""
## 🤖 AI Improvement System Overview

### Current Status
- **Total Improvements**: #{length(ai_data.improvements)}
- **Active Tasks**: #{length(ai_data.tasks)}
- **Metrics Collected**: #{length(ai_data.metrics)}
- **Success Rate**: #{Float.round(ai_data.analysis.success_rate, 1)}%

### Data Collection Time
#{DateTime.utc_now() |> DateTime.to_string()}
""")

Recent Improvement Proposals

# Display recent improvements with status
recent_improvements = 
  ai_data.improvements
  |> Enum.take(10)
  |> Enum.map(fn improvement ->
    %{
      id: improvement.id,
      type: improvement.improvement_type || "general",
      status: improvement.status || "pending",
      priority: improvement.priority || "medium",
      description: String.slice(improvement.description || "No description", 0, 100) <> "...",
      confidence: improvement.confidence_score || 0.0,
      impact: improvement.expected_impact || "unknown",
      created_at: improvement.inserted_at |> DateTime.to_string()
    }
  end)

Kino.DataTable.new(recent_improvements, name: "Recent AI Improvement Proposals")

Improvement Type Analysis

# Analyze improvement types and their success rates
improvement_types = 
  ai_data.improvements
  |> Enum.group_by(&amp; &amp;1.improvement_type)
  |> Enum.map(fn {type, improvements} ->
    completed = Enum.count(improvements, &amp;(&amp;1.status == :completed))
    %{
      type: type || "general",
      total: length(improvements),
      completed: completed,
      success_rate: if(length(improvements) > 0, do: completed / length(improvements) * 100, else: 0),
      avg_confidence: Enum.reduce(improvements, 0, &amp;(&amp;1.confidence_score + &amp;2)) / length(improvements)
    }
  end)

# Success rate by improvement type
success_chart = 
  Vl.new(width: 600, height: 400)
  |> Vl.data_from_values(improvement_types)
  |> Vl.mark(:bar)
  |> Vl.encode_field(:x, "type", type: :nominal, title: "Improvement Type")
  |> Vl.encode_field(:y, "success_rate", type: :quantitative, title: "Success Rate (%)")
  |> Vl.encode_field(:color, "avg_confidence", type: :quantitative, title: "Avg Confidence")
  |> Vl.config(title: [text: "Success Rate by Improvement Type", fontSize: 16])

Kino.VegaLite.new(success_chart)

Interactive Improvement Review

# Create improvement selector for detailed review
improvement_selector = Kino.Input.select("Select Improvement for Review", 
  recent_improvements |> Enum.map(&amp;{"#{&amp;1.type}: #{String.slice(&amp;1.description, 0, 50)}...", &amp;1.id})
)

selected_improvement_id = Kino.Input.read(improvement_selector)

if selected_improvement_id do
  selected_improvement = Enum.find(ai_data.improvements, &amp;(&amp;1.id == selected_improvement_id))
  
  if selected_improvement do
    # Get related tasks and metrics
    related_tasks = Enum.filter(ai_data.tasks, &amp;(&amp;1.improvement_id == selected_improvement_id))
    related_metrics = Enum.filter(ai_data.metrics, &amp;(&amp;1.improvement_id == selected_improvement_id))
    
    Kino.Markdown.new("""
    ## 🔍 Improvement Review: #{selected_improvement.improvement_type}
    
    ### Basic Information
    | Property | Value |
    |----------|-------|
    | **ID** | #{selected_improvement.id} |
    | **Type** | #{selected_improvement.improvement_type} |
    | **Status** | #{selected_improvement.status} |
    | **Priority** | #{selected_improvement.priority} |
    | **Confidence Score** | #{selected_improvement.confidence_score}/1.0 |
    | **Expected Impact** | #{selected_improvement.expected_impact} |
    | **Created** | #{selected_improvement.inserted_at |> DateTime.to_string()} |
    
    ### Description
    #{selected_improvement.description}
    
    ### Technical Details
    #{if selected_improvement.technical_details do
        "```\n#{selected_improvement.technical_details}\n```"
      else
        "No technical details available"
      end}
    
    ### Related Tasks
    #{if length(related_tasks) > 0 do
        related_tasks
        |> Enum.map(&amp;"- #{&amp;1.task_type}: #{&amp;1.description}")
        |> Enum.join("\n")
      else
        "No related tasks found"
      end}
    
    ### Performance Metrics
    #{if length(related_metrics) > 0 do
        related_metrics
        |> Enum.take(5)
        |> Enum.map(&amp;"- #{&amp;1.metric_type}: #{&amp;1.value} (#{&amp;1.unit})")
        |> Enum.join("\n")
      else
        "No performance metrics available"
      end}
    """)
  else
    Kino.Markdown.new("Improvement not found.")
  end
else
  Kino.Markdown.new("Please select an improvement to review.")
end

Collaborative Review Process

if selected_improvement_id do
  # Create review form
  review_frame = Kino.Frame.new()
  
  # Review inputs
  review_inputs = [
    technical_feasibility: Kino.Input.select("Technical Feasibility", [
      {"High - Can be implemented easily", "high"},
      {"Medium - Requires moderate effort", "medium"}, 
      {"Low - Significant technical challenges", "low"}
    ]),
    business_impact: Kino.Input.select("Business Impact", [
      {"High - Significant value", "high"},
      {"Medium - Moderate value", "medium"},
      {"Low - Limited value", "low"}
    ]),
    risk_assessment: Kino.Input.select("Risk Level", [
      {"Low - Minimal risk", "low"},
      {"Medium - Manageable risk", "medium"},
      {"High - Significant risk", "high"}
    ]),
    implementation_priority: Kino.Input.select("Implementation Priority", [
      {"Critical - Implement immediately", "critical"},
      {"High - Next sprint", "high"},
      {"Medium - Future planning", "medium"},
      {"Low - Consider later", "low"}
    ]),
    reviewer_notes: Kino.Input.textarea("Reviewer Notes")
  ]
  
  # Submit review button
  submit_review = Kino.Control.button("Submit Review")
  
  # Display review form
  form_content = Kino.Layout.grid([
    Kino.Markdown.new("## 📝 Improvement Review Form"),
    review_inputs.technical_feasibility,
    review_inputs.business_impact,
    review_inputs.risk_assessment,
    review_inputs.implementation_priority,
    review_inputs.reviewer_notes,
    submit_review
  ], columns: 1)
  
  Kino.Frame.render(review_frame, form_content)
  
  # Handle review submission
  Kino.Control.stream(submit_review)
  |> Kino.listen(fn _event ->
    review_data = %{
      improvement_id: selected_improvement_id,
      technical_feasibility: Kino.Input.read(review_inputs.technical_feasibility),
      business_impact: Kino.Input.read(review_inputs.business_impact),
      risk_assessment: Kino.Input.read(review_inputs.risk_assessment),
      implementation_priority: Kino.Input.read(review_inputs.implementation_priority),
      reviewer_notes: Kino.Input.read(review_inputs.reviewer_notes),
      reviewer: "livebook_user",
      review_timestamp: DateTime.utc_now()
    }
    
    # Save review to file
    review_filename = "improvement_review_#{selected_improvement_id}_#{DateTime.utc_now() |> DateTime.to_unix()}.json"
    File.write!(review_filename, Jason.encode!(review_data, pretty: true))
    
    success_message = Kino.Markdown.new("""
    ✅ **Review Submitted Successfully**
    
    Review saved to: `#{review_filename}`
    
    **Review Summary:**
    - Technical Feasibility: #{review_data.technical_feasibility}
    - Business Impact: #{review_data.business_impact}
    - Risk Level: #{review_data.risk_assessment}
    - Priority: #{review_data.implementation_priority}
    """)
    
    Kino.Frame.render(review_frame, success_message)
  end)
  
  review_frame
else
  Kino.Markdown.new("Please select an improvement to review.")
end

Improvement Trends Analysis

# Analyze improvement trends over time
if length(ai_data.improvements) > 0 do
  improvements_timeline = 
    ai_data.improvements
    |> Enum.map(fn improvement ->
      %{
        date: improvement.inserted_at |> DateTime.to_date() |> Date.to_string(),
        type: improvement.improvement_type || "general",
        status: improvement.status || "pending",
        confidence: improvement.confidence_score || 0.0
      }
    end)
  
  # Timeline chart
  timeline_chart = 
    Vl.new(width: 800, height: 400)
    |> Vl.data_from_values(improvements_timeline)
    |> Vl.mark(:circle, size: 100)
    |> Vl.encode_field(:x, "date", type: :temporal, title: "Date")
    |> Vl.encode_field(:y, "type", type: :nominal, title: "Improvement Type")
    |> Vl.encode_field(:color, "status", type: :nominal, title: "Status")
    |> Vl.encode_field(:size, "confidence", type: :quantitative, title: "Confidence")
    |> Vl.config(title: [text: "Improvement Timeline", fontSize: 16])
  
  Kino.VegaLite.new(timeline_chart)
else
  Kino.Markdown.new("No improvement data available for timeline analysis.")
end

AI Performance Metrics

# Display AI system performance metrics
if length(ai_data.metrics) > 0 do
  recent_metrics = 
    ai_data.metrics
    |> Enum.take(20)
    |> Enum.map(fn metric ->
      %{
        metric_type: metric.metric_type || "unknown",
        value: metric.value || 0,
        unit: metric.unit || "",
        timestamp: metric.created_at |> DateTime.to_string(),
        improvement_related: !is_nil(metric.improvement_id)
      }
    end)
  
  # Metrics over time
  metrics_chart = 
    Vl.new(width: 700, height: 300)
    |> Vl.data_from_values(recent_metrics)
    |> Vl.mark(:line, point: true)
    |> Vl.encode_field(:x, "timestamp", type: :temporal, title: "Time")
    |> Vl.encode_field(:y, "value", type: :quantitative, title: "Metric Value")
    |> Vl.encode_field(:color, "metric_type", type: :nominal, title: "Metric Type")
    |> Vl.config(title: [text: "AI System Performance Metrics", fontSize: 16])
  
  Kino.VegaLite.new(metrics_chart)
  
  # Metrics summary table
  Kino.DataTable.new(recent_metrics, name: "Recent Performance Metrics")
else
  Kino.Markdown.new("No performance metrics available.")
end

Improvement Validation Testing

# Testing framework for improvement validation
testing_frame = Kino.Frame.new()

if selected_improvement_id do
  test_inputs = [
    test_type: Kino.Input.select("Test Type", [
      {"Unit Tests", "unit"},
      {"Integration Tests", "integration"},
      {"Performance Tests", "performance"},
      {"User Acceptance Tests", "uat"}
    ]),
    test_environment: Kino.Input.select("Test Environment", [
      {"Development", "dev"},
      {"Staging", "staging"},
      {"Production (Canary)", "prod_canary"}
    ]),
    test_duration: Kino.Input.select("Test Duration", [
      {"Quick (< 1 hour)", "quick"},
      {"Medium (1-4 hours)", "medium"},
      {"Extended (> 4 hours)", "extended"}
    ])
  ]
  
  run_tests_button = Kino.Control.button("Run Validation Tests")
  
  test_form = Kino.Layout.grid([
    Kino.Markdown.new("## 🧪 Improvement Validation Testing"),
    test_inputs.test_type,
    test_inputs.test_environment,
    test_inputs.test_duration,
    run_tests_button
  ], columns: 1)
  
  Kino.Frame.render(testing_frame, test_form)
  
  # Handle test execution
  Kino.Control.stream(run_tests_button)
  |> Kino.listen(fn _event ->
    test_config = %{
      improvement_id: selected_improvement_id,
      test_type: Kino.Input.read(test_inputs.test_type),
      environment: Kino.Input.read(test_inputs.test_environment),
      duration: Kino.Input.read(test_inputs.test_duration),
      started_at: DateTime.utc_now()
    }
    
    # Simulate test execution (in real implementation, this would trigger actual tests)
    test_results = %{
      test_id: "test_#{System.unique_integer()}",
      status: "running",
      progress: 0,
      estimated_completion: DateTime.add(DateTime.utc_now(), 3600, :second)
    }
    
    result_content = Kino.Markdown.new("""
    🚀 **Test Execution Started**
    
    **Test Configuration:**
    - Type: #{test_config.test_type}
    - Environment: #{test_config.environment}
    - Duration: #{test_config.duration}
    - Started: #{test_config.started_at |> DateTime.to_string()}
    
    **Test ID:** #{test_results.test_id}
    **Status:** #{test_results.status}
    **Estimated Completion:** #{test_results.estimated_completion |> DateTime.to_string()}
    
    _Note: In a real implementation, this would integrate with your CI/CD pipeline to execute actual validation tests._
    """)
    
    Kino.Frame.render(testing_frame, result_content)
  end)
  
  testing_frame
else
  Kino.Markdown.new("Please select an improvement to run validation tests.")
end

Team Collaboration Dashboard

# Create collaborative workspace for team discussions
collaboration_frame = Kino.Frame.new()

team_discussion = Kino.Input.textarea("Team Discussion Notes")
add_comment_button = Kino.Control.button("Add Comment")

# Load existing comments (mock data for demo)
existing_comments = [
  %{author: "Alice", timestamp: "2024-06-15 10:30", comment: "This improvement looks promising for performance optimization."},
  %{author: "Bob", timestamp: "2024-06-15 11:15", comment: "Agreed, but we should validate the impact on memory usage."},
  %{author: "Carol", timestamp: "2024-06-15 14:20", comment: "I can help with the testing validation process."}
]

display_comments = fn comments ->
  comment_text = 
    comments
    |> Enum.map(fn comment ->
      "**#{comment.author}** (#{comment.timestamp}): #{comment.comment}"
    end)
    |> Enum.join("\n\n")
  
  Kino.Layout.grid([
    Kino.Markdown.new("## 💬 Team Discussion"),
    Kino.Markdown.new(comment_text),
    team_discussion,
    add_comment_button
  ], columns: 1)
end

Kino.Frame.render(collaboration_frame, display_comments.(existing_comments))

# Handle new comments
Kino.Control.stream(add_comment_button)
|> Kino.listen(fn _event ->
  new_comment = Kino.Input.read(team_discussion)
  
  if String.trim(new_comment) != "" do
    updated_comments = existing_comments ++ [%{
      author: "Current User",
      timestamp: DateTime.utc_now() |> DateTime.to_string(),
      comment: new_comment
    }]
    
    Kino.Frame.render(collaboration_frame, display_comments.(updated_comments))
  end
end)

collaboration_frame

Export Improvement Analysis

# Generate comprehensive improvement analysis report
export_analysis_button = Kino.Control.button("Export Improvement Analysis")
analysis_output = Kino.Frame.new()

Kino.Control.stream(export_analysis_button)
|> Kino.listen(fn _event ->
  timestamp = DateTime.utc_now() |> DateTime.to_iso8601()
  
  analysis_report = %{
    timestamp: timestamp,
    summary: %{
      total_improvements: length(ai_data.improvements),
      success_rate: ai_data.analysis.success_rate,
      recent_improvements: recent_improvements
    },
    improvement_types: improvement_types,
    selected_improvement: if(selected_improvement_id, do: selected_improvement_id, else: nil),
    metrics_analysis: ai_data.metrics |> Enum.take(50),
    recommendations: [
      "Focus on improvements with high confidence scores",
      "Prioritize performance-related enhancements",
      "Implement comprehensive testing for all changes",
      "Maintain collaborative review process"
    ]
  }
  
  analysis_filename = "ai_improvement_analysis_#{String.replace(timestamp, ":", "_")}.json"
  File.write!(analysis_filename, Jason.encode!(analysis_report, pretty: true))
  
  content = Kino.Markdown.new("""
  ✅ **AI Improvement Analysis Exported**
  
  Report saved to: `#{analysis_filename}`
  
  **Analysis Includes:**
  - Improvement success rate analysis
  - Type-based performance metrics
  - Trend analysis over time
  - Collaborative review data
  - Testing validation results
  - Team recommendations
  """)
  
  Kino.Frame.render(analysis_output, content)
end)

Kino.Layout.grid([export_analysis_button, analysis_output], columns: 1)

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