module LIMPS

"""
LIMPS (Language-Integrated Matrix Processing System) - Julia Module
A comprehensive system for polynomial operations, matrix processing, and entropy analysis
"""

using DynamicPolynomials
using MultivariatePolynomials
using LinearAlgebra
using JSON
using Random
using HTTP
using Sockets
using Statistics
using Logging

# Export main functions
export create_polynomials, analyze_polynomials, optimize_matrix, matrix_to_polynomials
export analyze_text_structure, optimize_polynomial, to_json
export start_http_server, start_limps_server
export LIMPSConfig, configure_limps

# Include submodules
include("polynomials.jl")
include("matrices.jl")
include("entropy.jl")
include("api.jl")
include("config.jl")

# Re-export submodule functions
using .Polynomials: create_polynomials, analyze_polynomials, optimize_polynomial
using .Matrices: optimize_matrix, matrix_to_polynomials
using .Entropy: analyze_text_structure, process_entropy_matrix
using .API: start_http_server, start_limps_server
using .Config: LIMPSConfig, configure_limps

"""
Main LIMPS processing function
"""
function process_limps_data(data::Union{Matrix{Float64}, Vector{Float64}}, 
                           data_type::String="matrix")
    """
    Main entry point for LIMPS data processing
    
    Args:
        data: Input data (matrix or vector)
        data_type: Type of data ("matrix", "vector", "text")
        
    Returns:
        Processed results
    """
    try
        if data_type == "matrix"
            return process_matrix_data(data)
        elseif data_type == "vector"
            return process_vector_data(data)
        elseif data_type == "text"
            return process_text_data(String(data))
        else
            return Dict("error" => "Unknown data type: $data_type")
        end
    catch e
        return Dict("error" => "Processing failed: $(e)")
    end
end

function process_matrix_data(matrix::Matrix{Float64})
    """Process matrix data through LIMPS pipeline"""
    results = Dict{String, Any}()
    
    # Convert to polynomial representation
    results["polynomial_representation"] = matrix_to_polynomials(matrix)
    
    # Analyze structure
    results["structure_analysis"] = analyze_matrix_structure(matrix)
    
    # Optimize based on structure
    complexity = results["structure_analysis"]["complexity_score"]
    if complexity > 0.7
        method = "rank"
    elseif complexity > 0.4
        method = "structure"
    else
        method = "sparsity"
    end
    
    results["optimization"] = optimize_matrix(matrix, method)
    results["optimization_method"] = method
    
    return results
end

function process_vector_data(vector::Vector{Float64})
    """Process vector data through LIMPS pipeline"""
    # Convert to matrix for processing
    matrix = reshape(vector, :, 1)
    return process_matrix_data(matrix)
end

function process_text_data(text::String)
    """Process text data through LIMPS pipeline"""
    results = Dict{String, Any}()
    
    # Analyze text structure
    results["text_analysis"] = analyze_text_structure(text)
    
    # Create feature vector
    features = extract_text_features(text)
    results["feature_vector"] = features
    
    # Convert features to polynomial representation
    variables = ["length", "words", "unique", "avg_len", "entropy"]
    results["polynomial_features"] = create_polynomials(features, variables)
    
    return results
end

function extract_text_features(text::String)
    """Extract numerical features from text"""
    words = split(text)
    
    features = [
        length(text),                    # text_length
        length(words),                   # word_count
        length(unique(words)),           # unique_words
        mean([length(word) for word in words]),  # average_word_length
        calculate_text_entropy(text)     # text_entropy
    ]
    
    return reshape(features, 1, :)
end

function calculate_text_entropy(text::String)
    """Calculate Shannon entropy of text"""
    words = split(text)
    word_freq = Dict{String, Int}()
    
    for word in words
        word_freq[word] = get(word_freq, word, 0) + 1
    end
    
    total_words = length(words)
    entropy = 0.0
    
    for (word, freq) in word_freq
        p = freq / total_words
        entropy -= p * log(p)
    end
    
    return entropy
end

function analyze_matrix_structure(matrix::Matrix{Float64})
    """Analyze matrix structure for optimization decisions"""
    m, n = size(matrix)
    
    # Calculate various metrics
    sparsity = 1.0 - count(!iszero, matrix) / (m * n)
    condition_num = try cond(matrix) catch; Inf end
    matrix_rank = rank(matrix)
    
    # Complexity score based on multiple factors
    complexity = (sparsity * 0.3 + 
                 (condition_num > 1000 ? 0.4 : 0.0) + 
                 (matrix_rank < min(m, n) * 0.5 ? 0.3 : 0.0))
    
    return Dict(
        "sparsity" => sparsity,
        "condition_number" => condition_num,
        "rank" => matrix_rank,
        "complexity_score" => complexity,
        "shape" => [m, n]
    )
end

"""
Batch processing for multiple datasets
"""
function batch_process_limps(data_list::Vector{Any}, 
                            data_types::Vector{String})
    """Process multiple datasets in batch"""
    results = []
    
    for (data, data_type) in zip(data_list, data_types)
        try
            result = process_limps_data(data, data_type)
            push!(results, result)
        catch e
            push!(results, Dict("error" => "Batch processing failed: $(e)"))
        end
    end
    
    return results
end

"""
Health check function for microservice
"""
function health_check()
    """Return system health status"""
    return Dict(
        "status" => "healthy",
        "timestamp" => string(now()),
        "version" => "1.0.0",
        "modules" => ["Polynomials", "Matrices", "Entropy", "API"]
    )
end

"""
Main function for testing
"""
function main()
    println("=== LIMPS.jl Module Test ===")
    
    # Test matrix processing
    println("1. Testing matrix processing...")
    matrix = rand(5, 5)
    result1 = process_limps_data(matrix, "matrix")
    println("Matrix processing: $(haskey(result1, "error") ? "FAILED" : "SUCCESS")")
    
    # Test text processing
    println("2. Testing text processing...")
    text = "Show monthly sales totals for electronics category"
    result2 = process_limps_data(text, "text")
    println("Text processing: $(haskey(result2, "error") ? "FAILED" : "SUCCESS")")
    
    # Test batch processing
    println("3. Testing batch processing...")
    data_list = [matrix, text]
    data_types = ["matrix", "text"]
    result3 = batch_process_limps(data_list, data_types)
    println("Batch processing: $(length(result3)) items processed")
    
    # Test health check
    println("4. Testing health check...")
    health = health_check()
    println("Health status: $(health["status"])")
    
    println("All tests completed!")
end

# Run main function if script is executed directly
if abspath(PROGRAM_FILE) == @__FILE__
    main()
end

end # module LIMPS