#!/usr/bin/env elixir

# Unlimited Progressive Challenge Generator with Real DSPy Module Integration
# Automatically generates increasingly difficult problems and solves them using actual DSPy reasoning modules

defmodule UnlimitedChallengesSolver do
  @moduledoc """
  Auto-generates unlimited progressive challenges that increase in complexity
  and solves them using real DSPy reasoning modules. Runs indefinitely with
  continuous learning and adaptation.
  """

  # Import all DSPy modules
  alias Dspy.{
    Predict, ChainOfThought, SelfConsistency, MultiStep, 
    AdaptiveBacktracking, ProgramOfThoughts, TreeOfThoughts, 
    MassCollaboration, Signature
  }

  # Define signatures for each level
  defmodule QuestionImproverSignature do
    use Dspy.Signature
    
    input_field :template_question, :string, "A question with template variables like {variable_123}"
    output_field :improved_question, :string, "Same question but with realistic specific values replacing template variables"
  end

  defmodule Level1Signature do
    use Dspy.Signature
    
    input_field :question, :string, "A factual question requiring direct knowledge retrieval"
    output_field :answer, :string, "Direct factual answer"
    output_field :confidence, :integer, "Confidence level (0-100)"
  end

  defmodule Level2Signature do
    use Dspy.Signature
    
    input_field :problem, :string, "A multi-step problem requiring step-by-step reasoning"
    output_field :reasoning_steps, :string, "Step-by-step reasoning process"
    output_field :final_answer, :string, "Final computed answer"
    output_field :confidence, :integer, "Confidence level (0-100)"
  end

  defmodule Level3Signature do
    use Dspy.Signature
    
    input_field :complex_problem, :string, "A complex problem requiring multiple solution approaches"
    output_field :approach_1, :string, "First solution approach and result"
    output_field :approach_2, :string, "Second solution approach and result"
    output_field :approach_3, :string, "Third solution approach and result"
    output_field :consistency_check, :string, "Analysis of consistency across approaches"
    output_field :final_answer, :string, "Most reliable answer based on consistency"
    output_field :confidence, :integer, "Confidence level (0-100)"
  end

  defmodule Level4Signature do
    use Dspy.Signature
    
    input_field :scenario, :string, "Complex multi-step business or analytical scenario"
    output_field :situation_analysis, :string, "Analysis of the current situation and key factors"
    output_field :option_evaluation, :string, "Evaluation of available strategic options"
    output_field :recommendation, :string, "Final recommendation with justification"
    output_field :implementation_plan, :string, "Step-by-step implementation approach"
    output_field :risk_assessment, :string, "Identified risks and mitigation strategies"
    output_field :confidence, :integer, "Confidence level (0-100)"
  end

  defmodule Level5Signature do
    use Dspy.Signature
    
    input_field :ethical_dilemma, :string, "Complex ethical dilemma with multiple perspectives"
    input_field :constraints, :list, "Ethical and practical constraints to maintain"
    output_field :perspective_analysis, :string, "Analysis of different stakeholder perspectives"
    output_field :ethical_framework, :string, "Ethical framework applied to the problem"
    output_field :solution_attempts, :string, "Multiple solution attempts and their evaluation"
    output_field :final_recommendation, :string, "Final ethically sound recommendation"
    output_field :constraint_compliance, :string, "Verification of constraint satisfaction"
    output_field :confidence, :integer, "Confidence level (0-100)"
  end

  defmodule Level6Signature do
    use Dspy.Signature
    
    input_field :research_question, :string, "Scientific research question requiring computational analysis"
    input_field :data_context, :string, "Available data and analytical context"
    output_field :hypothesis, :string, "Research hypothesis to test"
    output_field :methodology, :string, "Computational methodology and approach"
    output_field :analysis_code, :string, "Executable code for analysis (pseudo-code)"
    output_field :results_interpretation, :string, "Interpretation of computational results"
    output_field :conclusions, :string, "Evidence-based conclusions"
    output_field :confidence, :integer, "Confidence level (0-100)"
  end

  defmodule Level7Signature do
    use Dspy.Signature
    
    input_field :innovation_challenge, :string, "Creative innovation challenge"
    input_field :constraints, :list, "Design and practical constraints"
    output_field :creative_exploration, :string, "Exploration of creative solution space"
    output_field :innovative_concepts, :string, "Multiple innovative concept directions"
    output_field :concept_evaluation, :string, "Evaluation and refinement of concepts"
    output_field :final_innovation, :string, "Final innovative solution design"
    output_field :implementation_approach, :string, "Approach for bringing innovation to reality"
    output_field :confidence, :integer, "Confidence level (0-100)"
  end

  defmodule Level8Signature do
    use Dspy.Signature
    
    input_field :complex_system_challenge, :string, "Complex system design challenge requiring multiple expertise areas"
    input_field :expert_domains, :list, "Required areas of expertise"
    input_field :system_requirements, :list, "System requirements and constraints"
    output_field :expert_perspectives, :string, "Perspectives from different expert domains"
    output_field :integration_analysis, :string, "Analysis of how expert inputs integrate"
    output_field :conflict_resolution, :string, "Resolution of conflicting expert recommendations"
    output_field :unified_design, :string, "Unified system design incorporating all expertise"
    output_field :implementation_strategy, :string, "Collaborative implementation strategy"
    output_field :confidence, :integer, "Confidence level (0-100)"
  end

  # Problem generation templates (same as before but more targeted)
  @problem_templates %{
    1 => [
      "What is the capital of {country}?",
      "How many {time_unit} are in a {period}?", 
      "What is {number1} {operation} {number2}?",
      "What element has the chemical symbol {element_symbol}?",
      "In what year was {historical_event} invented/discovered?",
      "What is the largest {category} in the world?",
      "How many sides does a {geometric_shape} have?",
      "What is the freezing point of {substance} in {temperature_scale}?",
      "Which planet is {ordinal_number} from the sun?",
      "What language is primarily spoken in {country}?"
    ],
    
    2 => [
      "A store sells {quantity1} {item} for ${price}. If you buy {quantity2} {item} and pay with ${payment_amount}, how much change do you receive?",
      "{person1} is {age_relation} {person2}. If {person2} is currently {age} years old, and in {years_future} years {person2} will be {target_age}, how old is {person1} now?",
      "A rectangular garden measures {length} by {width} meters. If you want to increase the area by {percentage}% by adding equal amounts to both length and width, what should the new dimensions be?",
      "Two trains start {distance} km apart. Train A travels at {speed1} km/h toward Train B, which travels at {speed2} km/h toward Train A. When and where do they meet?",
      "If {workers1} workers can complete a job in {time1} hours, how long will it take {workers2} workers to complete the same job, assuming they work at the same rate?",
      "A recipe for {servings1} people calls for {ingredient_amount} {ingredient_unit} of {ingredient}. How much {ingredient} is needed to serve {servings2} people?",
      "An investment of ${principal} grows at {interest_rate}% annual compound interest. What will be the value after {years} years?",
      "A car travels {distance1} km in {time1} hours, then {distance2} km in {time2} hours. What is the average speed for the entire journey?",
      "If {percentage1}% of {group} are {category1}, and {percentage2}% of {category1} are {subcategory}, how many {subcategory} are in a group of {total_size}?",
      "A tank can be filled by pipe A in {time1} hours and by pipe B in {time2} hours. How long will it take to fill the tank if both pipes work together?"
    ],
    
    3 => [
      "Find all real solutions to the equation x^4 - {coefficient1}x^2 + {coefficient2} = 0. Verify your solutions using multiple methods.",
      "Determine whether the infinite series ∑(n=1 to ∞) {numerator}/(n^{exponent} + {constant}) converges or diverges using multiple convergence tests.",
      "Calculate the area under the curve y = {function_type} from x = {start_point} to x = {end_point} using at least three different methods.",
      "Solve the optimization problem: maximize {objective_function} subject to {constraint1} ≥ 0 and {constraint2} ≤ {limit}. Verify using different approaches.",
      "Find the maximum and minimum values of f(x,y) = {multivariable_function} subject to the constraint {constraint_equation} = 0.",
      "Solve the differential equation {differential_equation} with initial condition {initial_condition} using multiple solution methods.",
      "Determine the limit of {limit_expression} as x approaches {limit_point} using different limit evaluation techniques.",
      "Find the eigenvalues and eigenvectors of the {matrix_size}x{matrix_size} matrix with specific properties {matrix_properties}.",
      "Calculate the probability of {probability_event} occurring in {probability_context} using different probability approaches.",
      "Determine the convergence radius of the power series ∑(n=0 to ∞) {coefficient_pattern}x^n using multiple methods."
    ],
    
    4 => [
      "{company_type} faces declining {performance_metric} due to {root_cause}. With a budget of ${budget}, they must choose between {strategy1}, {strategy2}, and {strategy3}. Each has different {success_rates}, {timelines}, and {risk_profiles}. Provide comprehensive analysis and recommendation.",
      "A {technology_startup} is considering {business_decision} in the {market_context} market. Analyze market conditions, competitive landscape, financial implications, and technical feasibility. Develop a strategic recommendation.",
      "{organization_type} needs to implement {major_change} while maintaining {critical_operations}. Consider stakeholder impact, resource allocation, timeline management, and risk mitigation. Create a comprehensive implementation plan.",
      "In the {industry} industry, evaluate the impact of {disruptive_technology} on {market_segment}. Analyze short-term disruption, long-term transformation, and strategic responses for different market players.",
      "A {city_size} city is planning {urban_development_project} with budget constraints of ${budget}. Balance {competing_interest1}, {competing_interest2}, and {competing_interest3}. Develop a multi-phase implementation strategy.",
      "A {healthcare_scenario} requires choosing between {treatment_option1} and {treatment_option2}. Consider {patient_factors}, {cost_factors}, {outcome_probabilities}, and {ethical_considerations}. Provide evidence-based recommendation.",
      "{educational_institution} must address {academic_challenge} while dealing with {resource_constraints} and {stakeholder_pressures}. Design a solution addressing pedagogical, administrative, and financial aspects.",
      "Given {environmental_crisis} affecting {geographic_region}, develop an intervention strategy considering {ecological_factors}, {economic_impacts}, {political_constraints}, and {technological_solutions}.",
      "{global_corporation} faces {regulatory_challenge} across {number_countries} countries with different {legal_frameworks}. Design a compliance strategy addressing legal, operational, and financial requirements.",
      "A {research_institution} must allocate {resource_type} across {number_projects} competing projects with different success probabilities, impact potential, and time horizons. Optimize the allocation strategy."
    ],
    
    5 => [
      "An AI company developed facial recognition technology that could help solve missing children cases but raises significant privacy concerns. Address {ethical_dimension1}, {ethical_dimension2}, and {ethical_dimension3} while considering {stakeholder1}, {stakeholder2}, and {stakeholder3} perspectives. Ensure reasoning avoids {bias_type} and maintains {ethical_standard}.",
      "A pharmaceutical company must decide on pricing for a life-saving medication in {market_context}. Balance profit motives, patient access, research costs, and regulatory pressure while ensuring {accessibility_requirement} and {sustainability_requirement}.",
      "Design an AI system for {high_stakes_domain} that must be {requirement1}, {requirement2}, and {requirement3}. Address potential failure modes while ensuring {safety_standard} and {ethical_compliance}.",
      "A social media platform must handle {content_moderation_challenge} across cultures with different value systems. Develop an approach that respects cultural sensitivity while maintaining platform integrity.",
      "In a resource scarcity scenario, allocate {limited_resource} between {competing_need1}, {competing_need2}, and {competing_need3}. Consider equity, efficiency, and sustainability principles while avoiding allocation bias.",
      "An autonomous vehicle must make decisions in {moral_dilemma_scenario} involving {moral_consideration1} and {moral_consideration2}. Design a decision framework that handles edge cases while maintaining moral consistency.",
      "Address {social_justice_issue} in {institutional_context} by balancing progressive and conservative approaches while considering historical context, current constraints, and future implications.",
      "Design privacy-preserving technology for {data_sensitive_domain} that enables {beneficial_use1} and {beneficial_use2} while preventing {misuse_risk1} and {misuse_risk2}.",
      "In {crisis_scenario}, coordinate response between stakeholders with conflicting priorities. Develop an approach that ensures fairness and effectiveness.",
      "A decision-making body must choose between {policy_option1} and {policy_option2} in {political_context} while addressing various interest groups and public interest."
    ],
    
    6 => [
      "Analyze the relationship between {scientific_variable1} and {scientific_variable2} in {research_domain}. Design experiments, collect data, perform statistical analysis, and determine if the relationship is {relationship_type1} or {relationship_type2}.",
      "Develop a {computational_model} to predict {outcome_variable} based on {input_variable1}, {input_variable2}, and {input_variable3}. Implement multiple algorithms, compare performance, and validate using {validation_method}.",
      "Investigate {scientific_phenomenon} using {methodology1} and {methodology2}. Generate testable hypotheses, design computational experiments, analyze results, and draw evidence-based conclusions.",
      "Create a {simulation_type} simulation to study {complex_system} behavior under different conditions. Implement sensitivity analysis, parameter optimization, and scenario modeling.",
      "Analyze {large_dataset_type} data to identify {pattern_type} patterns related to {research_question}. Use multiple analysis techniques and machine learning methods to extract insights and make predictions.",
      "Design an optimization algorithm to solve {optimization_problem} with multiple constraints. Implement different approaches, compare efficiency, and validate solutions computationally.",
      "Study {complex_network} using network analysis methods to understand {network_property1} and {network_property2}. Create computational models to predict network behavior under different scenarios.",
      "Develop a predictive model for {time_series_data} incorporating {external_factor1}, {external_factor2}, and seasonal components. Implement forecasting algorithms and validate using robust methods.",
      "Analyze {experimental_data_type} from {scientific_domain} to test {scientific_hypothesis}. Use advanced computational techniques to process data and draw statistical conclusions.",
      "Create an agent-based model to simulate {social_phenomenon} involving different agent types and environmental factors. Study emergent behaviors and policy implications."
    ],
    
    7 => [
      "Design an innovative solution to reduce {waste_type} in {industry_context} while creating value for multiple stakeholders. Explore {creative_approach1}, {creative_approach2}, and unconventional methods.",
      "Develop {breakthrough_technology} that revolutionizes {problem_domain} by combining {technology1}, {technology2}, and novel approaches. Address technical challenges and adoption barriers.",
      "Create {artistic_expression} that communicates {complex_concept} to {target_audience} while incorporating cultural elements and innovation aspects. Consider multiple creative pathways.",
      "Design {educational_innovation} that transforms how {subject_matter} is taught by leveraging different pedagogical approaches and technology integration. Address learning challenges.",
      "Invent {product_concept} that solves {user_problem} in {market_context} using innovative features and disruptive elements. Explore multiple creative directions.",
      "Develop a creative framework for {problem_solving_domain} that combines different methodologies with novel perspectives. Handle various complexity factors.",
      "Create {storytelling_format} that explores {philosophical_question} through multiple narrative approaches and experimental techniques. Address thematic elements.",
      "Design {collaborative_platform} that enables {creative_activity} between different participant types. Incorporate innovation mechanisms.",
      "Develop {game_concept} that teaches {complex_skill} through engaging gameplay mechanics and learning integration. Balance engagement with educational objectives.",
      "Create {artistic_installation} that demonstrates {scientific_principle} using interactive elements and sensory components. Make abstract concepts tangible and engaging."
    ],
    
    8 => [
      "Design a real-time global messaging platform supporting {user_scale}+ concurrent users with end-to-end encryption, 99.99% uptime, and sub-second message delivery. Coordinate system architects, performance engineers, security specialists, and reliability engineers.",
      "Develop a {complex_system} for {critical_domain} requiring expertise in {domain1}, {domain2}, {domain3}, and {domain4}. Address system requirements and integration challenges through multi-expert collaboration.",
      "Create an innovative solution for {global_challenge} by integrating perspectives from {expert_background1}, {expert_background2}, {expert_background3}, and {expert_background4}. Handle multiple complexity dimensions.",
      "Design {advanced_technology} that combines {technical_domain1}, {technical_domain2}, and {technical_domain3} to solve {multifaceted_problem}. Coordinate specialist and generalist expertise.",
      "Develop comprehensive strategy for {organizational_transformation} requiring change management, technology integration, process optimization, and cultural adaptation. Leverage multiple types of expertise.",
      "Create {research_initiative} addressing {scientific_question} that spans multiple disciplines. Design collaborative framework for different types of researchers and practitioners.",
      "Design {policy_framework} for {governance_challenge} requiring input from policy experts, technical advisors, stakeholder representatives, and implementation specialists.",
      "Develop {educational_ecosystem} that transforms {learning_domain} through collaboration between different types of educators, technologists, and learning scientists.",
      "Create {healthcare_innovation} addressing {medical_challenge} by coordinating medical specialists, biomedical engineers, and health informaticists. Address clinical, safety, and regulatory requirements.",
      "Design {environmental_solution} for {ecological_challenge} requiring collaboration between environmental scientists, policy analysts, technology developers, and community organizers."
    ]
  }

  # Data for variable substitution
  @substitution_data %{
    countries: ["Japan", "Brazil", "Germany", "Australia", "India", "Canada", "South Korea", "Mexico"],
    operations: ["+", "-", "*", "/"],
    time_units: ["seconds", "minutes", "hours", "days", "weeks", "months"],
    periods: ["minute", "hour", "day", "week", "month", "year"],
    geometric_shapes: ["triangle", "square", "pentagon", "hexagon", "octagon", "decagon"],
    company_types: ["tech startup", "manufacturing company", "retail chain", "financial services firm", "healthcare provider"],
    industries: ["healthcare", "finance", "education", "manufacturing", "retail", "technology", "energy"],
    technologies: ["artificial intelligence", "blockchain", "quantum computing", "biotechnology", "renewable energy"],
    expert_domains: ["system architecture", "performance optimization", "cybersecurity", "user experience", "data science", "regulatory compliance"]
  }

  def start_unlimited_solving do
    IO.puts("🚀 Starting Unlimited Challenge Solver with Real DSPy Integration")
    IO.puts("🧠 Using actual DSPy reasoning modules for each challenge level")
    IO.puts("♾️  Running in unbounded mode with increasing difficulty")
    IO.puts("📊 Tracking performance metrics and learning patterns\n")
    
    # Initialize metrics and DSPy modules
    metrics = initialize_metrics()
    modules = initialize_dspy_modules()
    
    run_solving_loop(metrics, modules)
  end

  defp initialize_metrics do
    %{
      total_challenges: 0,
      successful_solutions: 0,
      level_performance: %{1 => [], 2 => [], 3 => [], 4 => [], 5 => [], 6 => [], 7 => [], 8 => []},
      average_confidence: 0.0,
      difficulty_multiplier: 1.0,
      start_time: DateTime.utc_now()
    }
  end

  defp initialize_dspy_modules do
    %{
      question_improver: Dspy.Predict.new(QuestionImproverSignature),
      level_1: Dspy.Predict.new(Level1Signature),
      level_2: Dspy.ChainOfThought.new(Level2Signature),
      level_3: Dspy.SelfConsistency.new(Level3Signature, num_samples: 2),
      level_4: Dspy.MultiStep.new([
        {:analyze, Level4Signature},
        {:evaluate, Level4Signature}, 
        {:recommend, Level4Signature}
      ]),
      level_5: Dspy.AdaptiveBacktracking.new(Level5Signature, [
        constraint: &ethical_constraint_check/2,
        perspective: &multiple_perspective_check/2
      ]),
      level_6: Dspy.ProgramOfThoughts.new(Level6Signature, executor: :elixir),
      level_7: Dspy.TreeOfThoughts.new(Level7Signature, num_thoughts: 4, max_depth: 3),
      level_8: Dspy.MassCollaboration.AgentCoordinator.new(
        agents: [
          %{role: :architect, expertise: "System Architecture"},
          %{role: :engineer, expertise: "Performance Engineering"}, 
          %{role: :security, expertise: "Security Specialist"},
          %{role: :reliability, expertise: "Reliability Engineering"}
        ],
        collaboration_rounds: 3
      )
    }
  end

  defp run_solving_loop(metrics, modules) do
    # Select next challenge level based on performance and progression
    level = select_challenge_level(metrics)
    
    # Generate challenge for the selected level
    challenge = generate_challenge(level, metrics.difficulty_multiplier)
    
    # Improve question with LLM if it contains template variables
    improved_problem = if String.contains?(challenge.problem, "_") do
      improve_question_with_llm(challenge.problem, modules)
    else
      challenge.problem
    end
    
    improved_challenge = %{challenge | problem: improved_problem}
    
    # Display challenge
    display_challenge_header(improved_challenge, metrics)
    
    # Solve using appropriate DSPy module
    {solution, solve_time} = solve_with_dspy_module(improved_challenge, modules, level)
    
    # Display solution
    display_solution_result(solution, solve_time)
    
    # Update metrics based on solution quality
    updated_metrics = update_performance_metrics(metrics, level, solution, solve_time)
    
    # Display updated progress
    display_progress_summary(updated_metrics)
    
    # Brief pause before next challenge
    Process.sleep(1500)
    
    # Continue loop with updated metrics
    run_solving_loop(updated_metrics, modules)
  end

  defp select_challenge_level(metrics) do
    total = metrics.total_challenges
    
    # Progressive level unlock based on performance
    cond do
      total < 5 -> Enum.random([1, 2])
      total < 15 -> Enum.random([1, 2, 3])
      total < 30 -> Enum.random([1, 2, 3, 4])
      total < 50 -> Enum.random([1, 2, 3, 4, 5])
      total < 75 -> Enum.random([1, 2, 3, 4, 5, 6])
      total < 100 -> Enum.random([1, 2, 3, 4, 5, 6, 7])
      true -> Enum.random([1, 2, 3, 4, 5, 6, 7, 8])
    end
  end

  defp generate_challenge(level, difficulty_multiplier) do
    template = @problem_templates[level] |> Enum.random()
    variables = extract_and_generate_variables(template, difficulty_multiplier)
    problem_text = substitute_variables(template, variables)
    
    %{
      level: level,
      problem: problem_text,
      variables: variables,
      difficulty: calculate_difficulty_score(level, difficulty_multiplier),
      timestamp: DateTime.utc_now()
    }
  end

  defp improve_question_with_llm(question, modules) do
    case Dspy.Module.forward(modules.question_improver, %{template_question: question}) do
      {:ok, prediction} ->
        case prediction do
          %{attrs: %{improved_question: improved}} when is_binary(improved) -> improved
          %{outputs: %{improved_question: improved}} when is_binary(improved) -> improved
          _ -> question  # Fallback to original if parsing fails
        end
      _ -> question  # Fallback to original on error
    end
  end

  defp extract_and_generate_variables(template, difficulty_multiplier) do
    variable_names = Regex.scan(~r/\{([^}]+)\}/, template, capture: :all_but_first)
                     |> List.flatten()
                     |> Enum.uniq()
    
    Enum.map(variable_names, fn var_name ->
      {var_name, generate_variable_value(var_name, difficulty_multiplier)}
    end)
    |> Enum.into(%{})
  end

  defp generate_variable_value(var_name, difficulty_multiplier) do
    base_multiplier = max(1, round(difficulty_multiplier))
    
    case var_name do
      "country" -> Enum.random(@substitution_data.countries)
      "operation" -> Enum.random(@substitution_data.operations)
      "time_unit" -> Enum.random(@substitution_data.time_units)
      "period" -> Enum.random(@substitution_data.periods)
      "geometric_shape" -> Enum.random(@substitution_data.geometric_shapes)
      "company_type" -> Enum.random(@substitution_data.company_types)
      
      var when var in ["number1", "number2", "quantity1", "quantity2"] ->
        Enum.random(1..50) * base_multiplier
        
      var when var in ["price", "payment_amount"] ->
        (Enum.random(5..100) * base_multiplier) |> to_string()
        
      var when var in ["age", "years_future", "target_age"] ->
        Enum.random(5..80)
        
      var when var in ["length", "width", "distance", "distance1", "distance2"] ->
        Enum.random(10..500) * base_multiplier
        
      var when var in ["speed1", "speed2"] ->
        Enum.random(30..120)
        
      var when var in ["percentage"] ->
        Enum.random(10..50)
        
      var when var in ["budget"] ->
        amounts = [1_000_000, 5_000_000, 10_000_000, 50_000_000]
        (Enum.random(amounts) * base_multiplier) |> to_string()
        
      var when var in ["user_scale"] ->
        scales = ["100M", "500M", "1B", "10B"]
        scale = Enum.random(scales)
        if base_multiplier > 1 do
          "#{base_multiplier}#{scale}"
        else
          scale
        end
        
      # Generate complex variable types
      var ->
        cond do
          String.contains?(var, "requirement") ->
            requirements = ["high availability", "scalability", "security", "performance", "reliability"]
            Enum.random(requirements)
          
          String.contains?(var, "challenge") ->
            challenges = ["technical complexity", "resource constraints", "stakeholder alignment", "regulatory compliance"]
            Enum.random(challenges)
          
          String.contains?(var, "domain") ->
            Enum.random(@substitution_data.expert_domains)
          
          String.contains?(var, "technology") ->
            Enum.random(@substitution_data.technologies)
          
          true ->
            "#{var}_#{:rand.uniform(1000)}"
        end
    end
  end

  defp substitute_variables(template, variables) do
    Enum.reduce(variables, template, fn {var_name, value}, text ->
      String.replace(text, "{#{var_name}}", to_string(value))
    end)
  end

  defp calculate_difficulty_score(level, difficulty_multiplier) do
    base_score = level * 12.5  # 12.5 points per level (100 max for level 8)
    final_score = base_score * difficulty_multiplier
    min(round(final_score), 100)
  end

  defp solve_with_dspy_module(challenge, modules, level) do
    start_time = System.monotonic_time(:millisecond)
    
    # Prepare inputs based on level
    inputs = prepare_module_inputs(challenge, level)
    
    # Get appropriate module and solve
    module_key = String.to_atom("level_#{level}")
    module = modules[module_key]
    
    try do
      # Execute DSPy module
      result = case level do
        level when level in [1, 2] ->
          Dspy.Module.forward(module, inputs)
          
        3 ->
          # Self-consistency generates multiple samples
          Dspy.Module.forward(module, inputs)
          
        4 ->
          # Multi-step requires sequential execution
          Dspy.Module.forward(module, inputs)
          
        5 ->
          # Adaptive backtracking with constraints
          Dspy.Module.forward(module, inputs)
          
        6 ->
          # Program of thoughts combines reasoning with computation
          Dspy.Module.forward(module, inputs)
          
        7 ->
          # Tree of thoughts explores multiple creative paths
          Dspy.Module.forward(module, inputs)
          
        8 ->
          # Mass collaboration coordinates multiple agents
          Dspy.Module.forward(module, inputs)
      end
      
      solve_time = System.monotonic_time(:millisecond) - start_time
      
      # Extract solution information
      solution = process_module_result(result, level)
      
      {solution, solve_time}
    rescue
      error ->
        solve_time = System.monotonic_time(:millisecond) - start_time
        error_solution = %{
          success: false,
          error: inspect(error),
          confidence: 0,
          reasoning: "Module execution failed: #{inspect(error)}"
        }
        {error_solution, solve_time}
    end
  end

  defp prepare_module_inputs(challenge, level) do
    case level do
      1 -> %{question: challenge.problem}
      2 -> %{problem: challenge.problem}
      3 -> %{complex_problem: challenge.problem}
      4 -> %{scenario: challenge.problem}
      5 -> %{
        ethical_dilemma: challenge.problem,
        constraints: ["maintain ethical standards", "avoid bias", "consider all stakeholders"]
      }
      6 -> %{
        research_question: challenge.problem,
        data_context: "Available computational tools and analytical methods"
      }
      7 -> %{
        innovation_challenge: challenge.problem,
        constraints: ["practical feasibility", "user adoption", "resource efficiency"]
      }
      8 -> %{
        complex_system_challenge: challenge.problem,
        expert_domains: ["architecture", "performance", "security", "reliability"],
        system_requirements: ["scalability", "reliability", "security", "performance"]
      }
    end
  end

  defp process_module_result(result, level) do
    case result do
      {:ok, outputs} ->
        confidence = extract_confidence(outputs)
        reasoning = extract_reasoning(outputs, level)
        
        %{
          success: true,
          outputs: outputs,
          confidence: confidence,
          reasoning: reasoning,
          level: level
        }
        
      {:error, error_info} ->
        %{
          success: false,
          error: error_info,
          confidence: 0,
          reasoning: "Solution failed: #{inspect(error_info)}",
          level: level
        }
        
      other ->
        %{
          success: false,
          error: "Unexpected result format",
          confidence: 0,
          reasoning: "Received unexpected result: #{inspect(other)}",
          level: level
        }
    end
  end

  defp extract_confidence(outputs) do
    case outputs do
      %{confidence: conf} when is_integer(conf) -> conf
      %{confidence: conf} when is_binary(conf) -> 
        case Integer.parse(conf) do
          {num, _} -> num
          _ -> 75  # Default confidence
        end
      _ -> 75  # Default confidence if not specified
    end
  end

  defp extract_reasoning(outputs, level) do
    # Helper function to safely convert binary data to string
    to_safe_string = fn value ->
      cond do
        is_binary(value) -> value
        is_bitstring(value) -> :binary.list_to_bin(:binary.bin_to_list(value))
        is_nil(value) -> ""
        true -> to_string(value)
      end
    end

    case level do
      1 -> to_safe_string.(outputs[:answer]) || "Direct factual response"
      2 -> to_safe_string.(outputs[:reasoning]) || to_safe_string.(outputs[:reasoning_steps]) || "Step-by-step analysis completed"
      3 -> "#{to_safe_string.(outputs[:approach_1])} | #{to_safe_string.(outputs[:approach_2])} | #{to_safe_string.(outputs[:consistency_check])}"
      4 -> "#{to_safe_string.(outputs[:situation_analysis])} → #{to_safe_string.(outputs[:recommendation])}"
      5 -> "#{to_safe_string.(outputs[:ethical_framework])} → #{to_safe_string.(outputs[:final_recommendation])}"
      6 -> "#{to_safe_string.(outputs[:methodology])} → #{to_safe_string.(outputs[:conclusions])}"
      7 -> "#{to_safe_string.(outputs[:creative_exploration])} → #{to_safe_string.(outputs[:final_innovation])}"
      8 -> "#{to_safe_string.(outputs[:expert_perspectives])} → #{to_safe_string.(outputs[:unified_design])}"
    end
  end

  defp display_challenge_header(challenge, metrics) do
    level_names = %{
      1 => "Basic Factual Reasoning",
      2 => "Chain of Thought", 
      3 => "Self-Consistency",
      4 => "Multi-Step Analysis",
      5 => "Adaptive Backtracking",
      6 => "Program of Thoughts",
      7 => "Tree of Thoughts", 
      8 => "Mass Collaboration"
    }
    
    level_emojis = %{
      1 => "🟢", 2 => "🟡", 3 => "🔵", 4 => "🟠",
      5 => "🔴", 6 => "⚫", 7 => "🌟", 8 => "🚀"
    }
    
    IO.puts("#{String.duplicate("=", 80)}")
    IO.puts("#{level_emojis[challenge.level]} LEVEL #{challenge.level}: #{level_names[challenge.level]}")
    IO.puts("🎯 Challenge ##{metrics.total_challenges + 1} | Difficulty: #{challenge.difficulty}/100")
    IO.puts("🧠 DSPy Module: #{get_module_description(challenge.level)}")
    IO.puts("#{String.duplicate("-", 80)}")
    IO.puts("📋 PROBLEM:")
    IO.puts(challenge.problem)
    IO.puts("#{String.duplicate("-", 80)}")
    IO.puts("🔄 Solving with DSPy...")
  end

  defp get_module_description(level) do
    descriptions = %{
      1 => "Dspy.Predict (direct reasoning)",
      2 => "Dspy.ChainOfThought (step-by-step)",
      3 => "Dspy.SelfConsistency (multiple approaches)",
      4 => "Dspy.MultiStep (sequential analysis)", 
      5 => "Dspy.AdaptiveBacktracking (constrained reasoning)",
      6 => "Dspy.ProgramOfThoughts (computational reasoning)",
      7 => "Dspy.TreeOfThoughts (creative exploration)",
      8 => "Dspy.MassCollaboration (multi-agent)"
    }
    
    descriptions[level]
  end

  defp display_solution_result(solution, solve_time) do
    if solution.success do
      IO.puts("✅ SOLUTION FOUND")
      IO.puts("⏱️  Solve Time: #{solve_time}ms")
      IO.puts("🎯 Confidence: #{solution.confidence}%")
      IO.puts("🧠 Reasoning: #{String.slice(solution.reasoning, 0, 200)}#{if String.length(solution.reasoning) > 200, do: "...", else: ""}")
      
      if solution.outputs do
        IO.puts("📋 Key Outputs:")
        outputs_list = if is_map(solution.outputs), do: Map.to_list(solution.outputs), else: solution.outputs
        outputs_list
        |> Enum.take(3)  # Show first 3 outputs
        |> Enum.each(fn {key, value} ->
          # Handle different value types safely
          string_value = cond do
            is_map(value) -> inspect(value)
            is_list(value) -> inspect(value)
            is_binary(value) -> value
            is_nil(value) -> "nil"
            true -> to_string(value)
          end
          
          display_value = String.slice(string_value, 0, 150)
          display_value = if String.length(string_value) > 150, do: display_value <> "...", else: display_value
          IO.puts("  • #{key}: #{display_value}")
        end)
      end
    else
      IO.puts("❌ SOLUTION FAILED")
      IO.puts("⏱️  Attempted Time: #{solve_time}ms")
      IO.puts("🚫 Error: #{inspect(solution.error)}")
      IO.puts("📝 Details: #{solution.reasoning}")
    end
    
    IO.puts("")
  end

  defp update_performance_metrics(metrics, level, solution, solve_time) do
    # Update level-specific performance
    level_performance = metrics.level_performance[level] || []
    new_performance_entry = %{
      confidence: solution.confidence,
      success: solution.success,
      solve_time: solve_time,
      timestamp: DateTime.utc_now()
    }
    updated_level_performance = [new_performance_entry | Enum.take(level_performance, 9)]  # Keep last 10
    
    # Calculate success rate and average confidence
    total_successes = if solution.success, do: metrics.successful_solutions + 1, else: metrics.successful_solutions
    total_challenges = metrics.total_challenges + 1
    
    all_confidences = Enum.flat_map(Map.values(metrics.level_performance), fn entries ->
      Enum.map(entries, & &1.confidence)
    end) ++ [solution.confidence]
    
    avg_confidence = if length(all_confidences) > 0 do
      Enum.sum(all_confidences) / length(all_confidences)
    else
      solution.confidence
    end
    
    # Adjust difficulty based on recent performance
    recent_success_rate = calculate_recent_success_rate(updated_level_performance)
    new_difficulty_multiplier = adjust_difficulty_multiplier(metrics.difficulty_multiplier, recent_success_rate)
    
    %{
      total_challenges: total_challenges,
      successful_solutions: total_successes,
      level_performance: Map.put(metrics.level_performance, level, updated_level_performance),
      average_confidence: avg_confidence,
      difficulty_multiplier: new_difficulty_multiplier,
      start_time: metrics.start_time
    }
  end

  defp calculate_recent_success_rate(performance_entries) do
    recent_entries = Enum.take(performance_entries, 5)  # Last 5 attempts
    if length(recent_entries) > 0 do
      successes = Enum.count(recent_entries, & &1.success)
      successes / length(recent_entries)
    else
      0.5  # Default success rate
    end
  end

  defp adjust_difficulty_multiplier(current_multiplier, success_rate) do
    adjustment = case success_rate do
      rate when rate > 0.8 -> 0.1   # Increase difficulty if too easy
      rate when rate < 0.3 -> -0.05 # Decrease difficulty if too hard
      _ -> 0                        # Maintain current difficulty
    end
    
    new_multiplier = current_multiplier + adjustment
    max(1.0, min(new_multiplier, 3.0))  # Clamp between 1.0 and 3.0
  end

  defp display_progress_summary(metrics) do
    elapsed_seconds = DateTime.diff(DateTime.utc_now(), metrics.start_time, :second)
    success_rate = if metrics.total_challenges > 0 do
      Float.round(metrics.successful_solutions / metrics.total_challenges * 100, 1)
    else
      0.0
    end
    
    challenges_per_minute = if elapsed_seconds > 0 do
      Float.round(metrics.total_challenges / elapsed_seconds * 60, 1)
    else
      0.0
    end
    
    IO.puts("📊 PERFORMANCE SUMMARY:")
    IO.puts("• Total Challenges: #{metrics.total_challenges}")
    IO.puts("• Success Rate: #{success_rate}%")
    IO.puts("• Average Confidence: #{Float.round(metrics.average_confidence, 1)}%")
    IO.puts("• Difficulty Multiplier: #{Float.round(metrics.difficulty_multiplier, 2)}x")
    IO.puts("• Rate: #{challenges_per_minute} challenges/minute")
    IO.puts("• Runtime: #{elapsed_seconds} seconds")
    
    IO.puts("\n📈 LEVEL PERFORMANCE:")
    Enum.each(1..8, fn level ->
      performances = metrics.level_performance[level] || []
      if length(performances) > 0 do
        level_success_rate = Enum.count(performances, & &1.success) / length(performances) * 100
        avg_confidence = Enum.sum(Enum.map(performances, & &1.confidence)) / length(performances)
        avg_time = Enum.sum(Enum.map(performances, & &1.solve_time)) / length(performances)
        
        IO.puts("  Level #{level}: #{length(performances)} attempts, #{Float.round(level_success_rate, 1)}% success, #{Float.round(avg_confidence, 1)}% conf, #{round(avg_time)}ms avg")
      end
    end)
    
    IO.puts("\n⏳ Next challenge in 1.5 seconds...\n")
  end

  # Constraint checking functions for Level 5 (Adaptive Backtracking)
  defp ethical_constraint_check(solution_attempt, _context) do
    # Simple constraint check - in real implementation this would be more sophisticated
    reasoning = solution_attempt[:ethical_framework] || ""
    
    has_ethical_framework = String.length(reasoning) > 10
    avoids_bias_language = !String.contains?(String.downcase(reasoning), ["always", "never", "all", "none"])
    
    has_ethical_framework and avoids_bias_language
  end

  defp multiple_perspective_check(solution_attempt, _context) do
    # Check if multiple perspectives are considered
    perspective_analysis = solution_attempt[:perspective_analysis] || ""
    
    # Simple check for multiple viewpoints
    perspective_indicators = ["perspective", "viewpoint", "stakeholder", "different", "various", "multiple"]
    perspective_count = Enum.count(perspective_indicators, fn indicator ->
      String.contains?(String.downcase(perspective_analysis), indicator)
    end)
    
    perspective_count >= 2
  end
end

# Start the unlimited challenge solver
UnlimitedChallengesSolver.start_unlimited_solving()