defmodule Raxol.Core.Performance.AIIntegration do
  @moduledoc """
  AI integration module for performance analysis.

  This module provides the interface for integrating with AI services
  to enhance performance analysis and provide intelligent recommendations.
  It bridges the gap between performance metrics and AI-powered insights.
  """

  require Raxol.Core.Runtime.Log

  alias Raxol.Core.Performance.Analyzer
  # AI modules removed - using stub implementations

  @type ai_service :: :openai | :anthropic | :local | :mock
  @type analysis_depth :: :basic | :detailed | :comprehensive
  @type optimization_level :: :minimal | :balanced | :aggressive

  @doc """
  Analyzes performance metrics (AI features disabled - returns basic analysis).

  ## Parameters
    - metrics: Map containing performance metrics from the monitor
    - options: Map of analysis options
      - :service (atom) - AI service to use (currently disabled)
      - :depth (atom) - Analysis depth (currently disabled)
      - :focus (list) - Areas to focus on ([:fps, :memory, :jank, :gc])
      - :historical_data (list) - Optional historical metrics for trend analysis
      - :optimization_level (atom) - Level of optimization suggestions

  ## Returns
    - Map containing:
      - :insights - AI-generated performance insights
      - :recommendations - Prioritized optimization suggestions
      - :risk_assessment - Performance risk analysis
      - :optimization_impact - Expected impact of suggested optimizations
      - :ai_confidence - AI's confidence in the analysis
      - :code_suggestions - Specific code optimization examples
  """
  def analyze_performance(metrics, options \\ %{}) do
    # Prepare data for AI analysis
    analysis_data = Analyzer.analyze(metrics)
    ai_data = Analyzer.prepare_ai_data(analysis_data)

    # Merge with options
    ai_data =
      Map.merge(ai_data, %{
        options:
          Map.merge(
            %{
              service: :mock,
              depth: :detailed,
              focus: [:fps, :memory, :jank, :gc],
              historical_data: [],
              optimization_level: :balanced
            },
            options
          )
      })

    # Send data to AI service for analysis
    case ai_data.options.service do
      :mock -> generate_mock_analysis(ai_data)
      :openai -> call_openai_service(ai_data)
      :anthropic -> call_anthropic_service(ai_data)
      :local -> call_local_ai_service(ai_data)
      _ -> {:error, "Unsupported AI service"}
    end
  end

  @doc """
  Generates a comprehensive performance report with AI insights.

  ## Parameters
    - metrics: Map containing performance metrics
    - options: Map of report options
      - :format (atom) - Report format (:text, :json, :html, :markdown)
      - :include_graphs (boolean) - Whether to include performance graphs
      - :include_code_samples (boolean) - Whether to include code optimization examples
      - :ai_service (atom) - AI service to use for analysis

  ## Returns
    - Map containing the formatted report and additional data
  """
  def generate_report(metrics, options \\ %{}) do
    analysis = analyze_performance(metrics, options)

    case analysis do
      {:error, reason} ->
        {:error, reason}

      analysis_data ->
        %{
          report: generate_report_content(analysis_data, options),
          analysis: analysis_data,
          metadata: %{
            generated_at: DateTime.utc_now(),
            version: "1.0.0",
            ai_service: Map.get(options, :ai_service, :mock)
          }
        }
    end
  end

  @doc """
  Provides real-time performance optimization suggestions.

  ## Parameters
    - component_name: Name of the component being analyzed
    - context: Map containing component context and metrics
    - options: Map of optimization options

  ## Returns
    - List of optimization suggestions with priority and impact
  """
  def get_optimization_suggestions(component_name, _context, _options \\ %{}) do
    # AI module removed - returning basic suggestions
    # Previously: PerformanceOptimization.analyze_performance()
    suggestions = []

    # Filter suggestions for the specific component
    component_suggestions =
      suggestions
      |> Enum.filter(fn suggestion ->
        suggestion.name == component_name or suggestion.type == :pattern
      end)
      |> Enum.map(fn suggestion ->
        %{
          priority: calculate_priority(suggestion),
          impact: estimate_impact(suggestion),
          effort: estimate_effort(suggestion),
          description: suggestion.suggestion,
          type: suggestion.type
        }
      end)
      |> Enum.sort_by(& &1.priority, :desc)

    {:ok, component_suggestions}
  end

  @doc """
  Generates contextual help content for performance issues.

  ## Parameters
    - issue_type: Type of performance issue (:slow_rendering, :memory_leak, etc.)
    - context: Additional context about the issue

  ## Returns
    - Generated help content with explanations and solutions
  """
  def generate_performance_help(issue_type, context \\ %{}) do
    # AI features disabled - ContentGeneration module removed
    _prompt = build_help_prompt(issue_type, context)

    {:ok,
     "AI performance help disabled. Issue type: #{issue_type}, Context: #{inspect(context, limit: 50)}"}
  end

  @doc """
  Predicts performance issues based on current metrics and trends.

  ## Parameters
    - metrics: Current performance metrics
    - historical_data: Historical metrics for trend analysis
    - options: Prediction options

  ## Returns
    - Map containing predicted issues and confidence levels
  """
  def predict_performance_issues(
        metrics,
        historical_data \\ [],
        _options \\ %{}
      ) do
    # Analyze current metrics
    current_analysis = Analyzer.analyze(metrics)

    # Analyze trends if historical data is available
    trend_analysis = analyze_trends(historical_data)

    # Combine analyses for prediction
    predictions = %{
      short_term: predict_short_term_issues(current_analysis, trend_analysis),
      medium_term: predict_medium_term_issues(current_analysis, trend_analysis),
      long_term: predict_long_term_issues(current_analysis, trend_analysis),
      confidence:
        calculate_prediction_confidence(current_analysis, trend_analysis)
    }

    {:ok, predictions}
  end

  @spec generate_mock_analysis(any()) :: any()
  defp generate_mock_analysis(_ai_data) do
    %{
      insights: [
        "Component rendering time has increased by 15% over the last 10 frames",
        "Memory usage shows a steady upward trend, indicating potential memory leaks",
        "Garbage collection frequency is higher than optimal for this workload",
        "Several components are re-rendering unnecessarily due to prop changes"
      ],
      recommendations: [
        %{
          priority: "high",
          area: "Component Optimization",
          description:
            "Implement React.memo for pure components to prevent unnecessary re-renders",
          impact:
            "High performance improvement (20-30% reduction in render time)",
          effort: "Low",
          code_example: "const MemoizedComponent = React.memo(Component);"
        },
        %{
          priority: "high",
          area: "Memory Management",
          description:
            "Review component lifecycle and implement proper cleanup",
          impact: "Significant memory reduction (15-25% less memory usage)",
          effort: "Medium",
          code_example: "useEffect(() => { return () => cleanup(); }, []);"
        },
        %{
          priority: "medium",
          area: "State Management",
          description:
            "Optimize state updates to reduce unnecessary re-renders",
          impact:
            "Medium performance improvement (10-15% better responsiveness)",
          effort: "Medium",
          code_example: "const [state, setState] = useState(initialState);"
        }
      ],
      risk_assessment: %{
        overall_risk: "medium",
        areas: [
          %{
            "area" => "Rendering Performance",
            "level" => "high",
            "trend" => "declining"
          },
          %{
            "area" => "Memory Usage",
            "level" => "medium",
            "trend" => "increasing"
          },
          %{"area" => "State Management", "level" => "low", "trend" => "stable"}
        ],
        immediate_concerns: [
          "Memory leak in user profile component",
          "Slow rendering in data table"
        ],
        long_term_risks: [
          "Performance degradation under load",
          "Increased memory footprint"
        ]
      },
      optimization_impact: %{
        "Performance" => "20-30% improvement in render times",
        "Memory Usage" => "15-25% reduction in memory footprint",
        "User Experience" => "Significantly smoother interactions",
        "Battery Life" => "10-15% improvement on mobile devices"
      },
      ai_confidence: 0.87,
      code_suggestions: [
        %{
          file: "components/DataTable.tsx",
          line: 45,
          suggestion: "Add React.memo wrapper",
          before: "export default function DataTable({ data }) {",
          after: "const DataTable = React.memo(function DataTable({ data }) {"
        },
        %{
          file: "hooks/useOptimizedState.ts",
          line: 12,
          suggestion: "Implement state batching",
          before: "setState(newValue);",
          after: "setState(prev => ({ ...prev, ...newValue }));"
        }
      ]
    }
  end

  @spec call_openai_service(any()) :: any()
  defp call_openai_service(ai_data) do
    api_key = System.get_env("OPENAI_API_KEY")

    case is_nil(api_key) do
      true ->
        Raxol.Core.Runtime.Log.warning(
          "OpenAI API key not configured, falling back to mock analysis",
          %{service: :openai, data: ai_data}
        )

        generate_mock_analysis(ai_data)

      false ->
        case make_openai_request(ai_data, api_key) do
          {:ok, response} ->
            parse_openai_response(response)

          {:error, reason} ->
            Raxol.Core.Runtime.Log.warning(
              "OpenAI API request failed, falling back to mock analysis",
              %{service: :openai, error: reason, data: ai_data}
            )

            generate_mock_analysis(ai_data)
        end
    end
  end

  @spec make_openai_request(any(), any()) :: any()
  defp make_openai_request(ai_data, api_key) do
    url = "https://api.openai.com/v1/chat/completions"

    headers = [
      {"Authorization", "Bearer #{api_key}"},
      {"Content-Type", "application/json"}
    ]

    prompt = build_performance_analysis_prompt(ai_data)

    body =
      Jason.encode!(%{
        model: "gpt-3.5-turbo",
        messages: [%{role: "user", content: prompt}],
        max_tokens: 1000,
        temperature: 0.3
      })

    HTTPoison.post(url, body, headers, timeout: 30_000, recv_timeout: 30_000)
  end

  @spec parse_openai_response(String.t()) :: {:ok, any()} | {:error, any()}
  defp parse_openai_response(response) do
    case response do
      %{status_code: 200, body: body} ->
        case Jason.decode(body) do
          {:ok, %{"choices" => [%{"message" => %{"content" => _content}} | _]}} ->
            # Parse the AI response and convert to our expected format
            # For now, return mock data as the response format needs to be standardized
            generate_mock_analysis(%{})

          _ ->
            {:error, "Invalid OpenAI response format"}
        end

      error ->
        {:error, error}
    end
  end

  @spec build_performance_analysis_prompt(any()) :: any()
  defp build_performance_analysis_prompt(ai_data) do
    """
    Analyze the following performance metrics and provide insights:

    Metrics: #{inspect(ai_data.metrics)}
    Analysis: #{inspect(ai_data.analysis)}
    Context: #{inspect(ai_data.context)}

    Please provide performance insights, recommendations, and risk assessment.
    """
  end

  @spec call_anthropic_service(any()) :: any()
  defp call_anthropic_service(ai_data) do
    Raxol.Core.Runtime.Log.warning(
      "Anthropic service integration not yet implemented, falling back to mock analysis",
      %{service: :anthropic, data: ai_data}
    )

    generate_mock_analysis(ai_data)
  end

  @spec call_local_ai_service(any()) :: any()
  defp call_local_ai_service(ai_data) do
    Raxol.Core.Runtime.Log.warning(
      "Local AI service integration not yet implemented, falling back to mock analysis",
      %{service: :local, data: ai_data}
    )

    generate_mock_analysis(ai_data)
  end

  @spec generate_report_content(any(), any()) :: any()
  defp generate_report_content(analysis, options) do
    format = Map.get(options, :format, :text)
    include_code_samples = Map.get(options, :include_code_samples, false)

    case format do
      :text -> format_text_report(analysis, include_code_samples)
      :json -> Jason.encode!(analysis, pretty: true)
      :html -> format_html_report(analysis, include_code_samples)
      :markdown -> format_markdown_report(analysis, include_code_samples)
      _ -> format_text_report(analysis, include_code_samples)
    end
  end

  @spec format_text_report(any(), any()) :: String.t()
  defp format_text_report(analysis, include_code_samples) do
    """
    Performance Analysis Report
    =========================

    Key Insights:
    #{Enum.join(analysis.insights, "\n")}

    Recommendations:
    #{format_recommendations(analysis.recommendations)}

    Risk Assessment:
    #{format_risk_assessment(analysis.risk_assessment)}

    Expected Impact:
    #{format_impact(analysis.optimization_impact)}

    #{case {include_code_samples, analysis.code_suggestions} do
      {true, suggestions} when not is_nil(suggestions) -> "Code Suggestions:\n#{format_code_suggestions(suggestions)}"
      _ -> ""
    end}

    AI Confidence: #{Float.round(analysis.ai_confidence * 100, 1)}%
    """
  end

  @spec format_html_report(any(), any()) :: String.t()
  defp format_html_report(analysis, include_code_samples) do
    """
    <!DOCTYPE html>
    <html>
      <head>
        <title>Performance Analysis Report</title>
        <style>
          body { font-family: system-ui, sans-serif; line-height: 1.6; margin: 2em; }
          .section { margin: 1.5em 0; }
          .high { color: #e53e3e; }
          .medium { color: #dd6b20; }
          .low { color: #38a169; }
          .code-block { background: #f7fafc; padding: 1em; border-radius: 4px; font-family: monospace; }
          .recommendation { border-left: 4px solid #3182ce; padding-left: 1em; margin: 1em 0; }
        </style>
      </head>
      <body>
        <h1>Performance Analysis Report</h1>

        <div class="section">
          <h2>Key Insights</h2>
          <ul>
            #{Enum.map_join(analysis.insights, "\n", &"<li>#{&1}</li>")}
          </ul>
        </div>

        <div class="section">
          <h2>Recommendations</h2>
          #{format_recommendations_html(analysis.recommendations)}
        </div>

        <div class="section">
          <h2>Risk Assessment</h2>
          #{format_risk_assessment_html(analysis.risk_assessment)}
        </div>

        <div class="section">
          <h2>Expected Impact</h2>
          #{format_impact_html(analysis.optimization_impact)}
        </div>

        #{case {include_code_samples, analysis.code_suggestions} do
      {true, suggestions} when not is_nil(suggestions) -> "<div class=\"section\"><h2>Code Suggestions</h2>#{format_code_suggestions_html(suggestions)}</div>"
      _ -> ""
    end}

        <div class="section">
          <p><strong>AI Confidence:</strong> #{Float.round(analysis.ai_confidence * 100, 1)}%</p>
        </div>
      </body>
    </html>
    """
  end

  @spec format_markdown_report(any(), any()) :: String.t()
  defp format_markdown_report(analysis, include_code_samples) do
    """
    # Performance Analysis Report

    ## Key Insights
    #{Enum.map_join(analysis.insights, "\n", &"- #{&1}")}

    ## Recommendations
    #{format_recommendations_markdown(analysis.recommendations)}

    ## Risk Assessment
    #{format_risk_assessment_markdown(analysis.risk_assessment)}

    ## Expected Impact
    #{format_impact_markdown(analysis.optimization_impact)}

    #{case {include_code_samples, analysis.code_suggestions} do
      {true, suggestions} when not is_nil(suggestions) -> "## Code Suggestions\n#{format_code_suggestions_markdown(suggestions)}"
      _ -> ""
    end}

    **AI Confidence:** #{Float.round(analysis.ai_confidence * 100, 1)}%
    """
  end

  @spec format_recommendations(any()) :: String.t()
  defp format_recommendations(recommendations) do
    Enum.map_join(recommendations, "\n", fn rec ->
      """
      [#{rec.priority}] #{rec.area}
      Description: #{rec.description}
      Impact: #{rec.impact}
      Effort: #{rec.effort}
      #{case rec.code_example do
        nil -> ""
        example -> "Code: #{example}"
      end}
      """
    end)
  end

  @spec format_recommendations_html(any()) :: String.t()
  defp format_recommendations_html(recommendations) do
    Enum.map_join(recommendations, "\n", fn rec ->
      """
      <div class="recommendation #{rec.priority}">
        <h3>[#{rec.priority}] #{rec.area}</h3>
        <p><strong>Description:</strong> #{rec.description}</p>
        <p><strong>Impact:</strong> #{rec.impact}</p>
        <p><strong>Effort:</strong> #{rec.effort}</p>
        #{case rec.code_example do
        nil -> ""
        example -> "<div class=\"code-block\">#{example}</div>"
      end}
      </div>
      """
    end)
  end

  @spec format_recommendations_markdown(any()) :: String.t()
  defp format_recommendations_markdown(recommendations) do
    Enum.map_join(recommendations, "\n", fn rec ->
      """
      ### [#{rec.priority}] #{rec.area}
      **Description:** #{rec.description}
      **Impact:** #{rec.impact}
      **Effort:** #{rec.effort}
      #{case rec.code_example do
        nil -> ""
        example -> "```\n#{example}\n```"
      end}
      """
    end)
  end

  @spec format_risk_assessment(any()) :: String.t()
  defp format_risk_assessment(risk) do
    """
    Overall Risk: #{risk.overall_risk}

    Area Risks:
    #{Enum.map_join(risk.areas, "\n", fn area -> "  #{area["area"]}: #{area["level"]} (#{area["trend"]})" end)}

    Immediate Concerns:
    #{Enum.join(risk.immediate_concerns, "\n")}

    Long-term Risks:
    #{Enum.join(risk.long_term_risks, "\n")}
    """
  end

  @spec format_risk_assessment_html(any()) :: String.t()
  defp format_risk_assessment_html(risk) do
    """
    <p><strong>Overall Risk:</strong> <span class="#{risk.overall_risk}">#{risk.overall_risk}</span></p>

    <h3>Area Risks</h3>
    <ul>
      #{Enum.map_join(risk.areas, "\n", fn area -> "<li>#{area["area"]}: <span class=\"#{area["level"]}\">#{area["level"]}</span> (#{area["trend"]})</li>" end)}
    </ul>

    <h3>Immediate Concerns</h3>
    <ul>
      #{Enum.map_join(risk.immediate_concerns, "\n", fn concern -> "<li>#{concern}</li>" end)}
    </ul>

    <h3>Long-term Risks</h3>
    <ul>
      #{Enum.map_join(risk.long_term_risks, "\n", fn risk -> "<li>#{risk}</li>" end)}
    </ul>
    """
  end

  @spec format_risk_assessment_markdown(any()) :: String.t()
  defp format_risk_assessment_markdown(risk) do
    """
    **Overall Risk:** #{risk.overall_risk}

    ### Area Risks
    #{Enum.map_join(risk.areas, "\n", fn area -> "- #{area["area"]}: #{area["level"]} (#{area["trend"]})" end)}

    ### Immediate Concerns
    #{Enum.map_join(risk.immediate_concerns, "\n", fn concern -> "- #{concern}" end)}

    ### Long-term Risks
    #{Enum.map_join(risk.long_term_risks, "\n", fn risk -> "- #{risk}" end)}
    """
  end

  @spec format_impact(any()) :: String.t()
  defp format_impact(impact) do
    Enum.map_join(impact, "\n", fn {area, value} -> "#{area}: #{value}" end)
  end

  @spec format_impact_html(any()) :: String.t()
  defp format_impact_html(impact) do
    """
    <ul>
      #{Enum.map_join(impact, "\n", fn {area, value} -> "<li><strong>#{area}:</strong> #{value}</li>" end)}
    </ul>
    """
  end

  @spec format_impact_markdown(any()) :: String.t()
  defp format_impact_markdown(impact) do
    Enum.map_join(impact, "\n", fn {area, value} ->
      "- **#{area}:** #{value}"
    end)
  end

  @spec format_code_suggestions(any()) :: String.t()
  defp format_code_suggestions(suggestions) do
    Enum.map_join(suggestions, "\n", fn suggestion ->
      """
      File: #{suggestion.file}:#{suggestion.line}
      Suggestion: #{suggestion.suggestion}
      Before: #{suggestion.before}
      After: #{suggestion.after}
      """
    end)
  end

  @spec format_code_suggestions_html(any()) :: String.t()
  defp format_code_suggestions_html(suggestions) do
    Enum.map_join(suggestions, "\n", fn suggestion ->
      """
      <div class="code-suggestion">
        <h4>#{suggestion.file}:#{suggestion.line}</h4>
        <p><strong>Suggestion:</strong> #{suggestion.suggestion}</p>
        <div class="code-block">
          <strong>Before:</strong><br>
          #{suggestion.before}
        </div>
        <div class="code-block">
          <strong>After:</strong><br>
          #{suggestion.after}
        </div>
      </div>
      """
    end)
  end

  @spec format_code_suggestions_markdown(any()) :: String.t()
  defp format_code_suggestions_markdown(suggestions) do
    Enum.map_join(suggestions, "\n", fn suggestion ->
      """
      ### #{suggestion.file}:#{suggestion.line}
      **Suggestion:** #{suggestion.suggestion}

      **Before:**
      ```
      #{suggestion.before}
      ```

      **After:**
      ```
      #{suggestion.after}
      ```
      """
    end)
  end

  @spec calculate_priority(any()) :: any()
  defp calculate_priority(suggestion) do
    case suggestion.issue do
      :slow_rendering when suggestion.avg_time > 100 -> "high"
      :slow_rendering when suggestion.avg_time > 50 -> "medium"
      :memory_leak -> "high"
      :excessive_updates -> "medium"
      _ -> "low"
    end
  end

  @spec estimate_impact(any()) :: any()
  defp estimate_impact(suggestion) do
    case suggestion.issue do
      :slow_rendering when suggestion.avg_time > 100 ->
        "High performance improvement"

      :slow_rendering when suggestion.avg_time > 50 ->
        "Medium performance improvement"

      :memory_leak ->
        "Significant memory reduction"

      :excessive_updates ->
        "Reduced CPU usage"

      _ ->
        "Minor improvement"
    end
  end

  @spec estimate_effort(any()) :: any()
  defp estimate_effort(suggestion) do
    case suggestion.issue do
      :slow_rendering -> "Medium"
      :memory_leak -> "High"
      :excessive_updates -> "Low"
      _ -> "Low"
    end
  end

  @spec build_help_prompt(any(), any()) :: any()
  defp build_help_prompt(issue_type, context) do
    base_prompts = %{
      slow_rendering: "How to optimize slow component rendering",
      memory_leak: "How to identify and fix memory leaks in React components",
      excessive_updates: "How to reduce unnecessary component re-renders",
      jank: "How to fix UI jank and improve frame rate",
      gc_pressure: "How to reduce garbage collection pressure"
    }

    base_prompt =
      Map.get(base_prompts, issue_type, "How to improve performance")

    case map_size(context) do
      0 ->
        base_prompt

      _ ->
        context_str =
          context
          |> Map.to_list()
          |> Enum.map_join(", ", fn {k, v} -> "#{k}: #{v}" end)

        "#{base_prompt} in context: #{context_str}"
    end
  end

  @spec analyze_trends(any()) :: any()
  defp analyze_trends(historical_data) do
    # Simple trend analysis - in a real implementation, this would use more sophisticated algorithms
    case historical_data do
      [] ->
        %{trend: "stable", confidence: 0.0}

      data when length(data) < 5 ->
        %{trend: "insufficient_data", confidence: 0.0}

      data ->
        recent = Enum.take(data, 5)
        older = Enum.take(data, -5)

        recent_avg = Enum.sum(recent) / length(recent)
        older_avg = Enum.sum(older) / length(older)

        trend =
          case recent_avg > older_avg * 1.1 do
            true -> "declining"
            false -> "stable"
          end

        confidence = min(abs(recent_avg - older_avg) / older_avg, 1.0)

        %{trend: trend, confidence: confidence}
    end
  end

  @spec predict_short_term_issues(any(), any()) :: any()
  defp predict_short_term_issues(_current_analysis, _trend_analysis) do
    # Predict issues in the next few minutes
    []
  end

  @spec predict_medium_term_issues(any(), any()) :: any()
  defp predict_medium_term_issues(_current_analysis, _trend_analysis) do
    # Predict issues in the next few hours
    []
  end

  @spec predict_long_term_issues(any(), any()) :: any()
  defp predict_long_term_issues(_current_analysis, _trend_analysis) do
    # Predict issues in the next few days/weeks
    []
  end

  @spec calculate_prediction_confidence(any(), any()) :: any()
  defp calculate_prediction_confidence(_current_analysis, _trend_analysis) do
    # Calculate confidence based on data quality and trend consistency
    0.75
  end
end
