# Code Generation - MCTS for Programming Tasks

This example shows how MCTS can guide LLM code generation by exploring different implementation strategies.

```julia
using LLMMCTS

# State represents the current state of code generation
# It includes the code written so far and the problem being solved

function code_generation_transition(state::Dict, args::NamedTuple)
    current_step = get(state, :step, 0)
    problem = state[:problem]
    code_so_far = get(state, :code, "")
    
    if current_step == 0
        # First step: Plan the approach
        new_code = """
        # Function to solve: $(problem)
        function solve_problem(input)
        """
        newstate = Dict(
            :step => 1,
            :code => new_code,
            :thought => "Plan the approach for: $(problem)",
            :reward => 2.0,
            :isterminal => false
        )
        progressvalue = 5.0
    elseif current_step == 1
        # Second step: Implement main logic
        new_code = code_so_far * """
            # Main logic implementation
            result = input * 2  # Placeholder implementation
            return result
        end
        """
        newstate = Dict(
            :step => 2,
            :code => new_code,
            :thought => "Implement main function logic",
            :reward => 3.0,
            :isterminal => false
        )
        progressvalue = 7.0
    elseif current_step == 2
        # Third step: Add tests
        new_code = code_so_far * """
        
        # Test the function
        @assert solve_problem(5) == 10
        @assert solve_problem(0) == 0
        println("All tests passed!")
        """
        newstate = Dict(
            :step => 3,
            :code => new_code,
            :thought => "Add unit tests to verify implementation",
            :reward => 5.0,
            :isterminal => true  # Code generation complete
        )
        progressvalue = 10.0
    else
        newstate = Dict(
            :step => current_step,
            :code => code_so_far,
            :thought => "Code generation complete",
            :reward => 10.0,
            :isterminal => true
        )
        progressvalue = 10.0
    end
    
    return Dict(
        :newNodeKey => "code_step_$current_step",
        :newstate => newstate,
        :progressvalue => progressvalue
    )
end

# Initial state
initialstate = Dict(
    :step => 0,
    :problem => "Create a function that doubles its input",
    :code => "",
    :reward => 0,
    :isterminal => false
)

# Transition arguments
transitionargs = (max_steps = 3,)

# Run MCTS
result = runMCTS(
    initialstate,
    code_generation_transition,
    transitionargs;
    maxiterations = 20,
    explorationweight = 1.0,
    maxSimulationDepth = 3,
    horizontalSampleExpansionPhase = 3
)

# Display results
println("Code Generation Example")
println("=======================")
println()
println("Problem: ", initialstate[:problem])
println()
println("Generated code:")
println(result.bestTerminalState[:code])
println()
println("Code generation complete! ✓")
println("Root node visits: ", result.root.visits)
```