from pyomo.environ import *

def solve_rd_projects():
    model = ConcreteModel()

    projects = list(range(1, 8))  # Projects 1 through 7

    NPV = {
        1: 600000, 2: 580000, 3: 550000, 4: 400000,
        5: 650000, 6: 725000, 7: 340000
    }

    Eng = {
        1: 9, 2: 4, 3: 7, 4: 12,
        5: 8, 6: 10, 7: 8
    }

    Cost = {
        1: 196000, 2: 400000, 3: 70000, 4: 180000,
        5: 225000, 6: 200000, 7: 130000
    }

    model.y = Var(projects, domain=Binary)

    # Maximize NPV
    model.objective = Objective(
        expr=sum(NPV[i] * model.y[i] for i in projects),
        sense=maximize
    )

    # Budget constraint
    model.budget = Constraint(expr=sum(Cost[i] * model.y[i] for i in projects) <= 1300000)

    # Engineer constraint
    model.engineers = Constraint(expr=sum(Eng[i] * model.y[i] for i in projects) <= 35)

    # Project dependencies
    model.max_one_of_12 = Constraint(expr=model.y[1] + model.y[2] <= 1)
    model.project4_depends_on_2 = Constraint(expr=model.y[4] <= model.y[2])

    solver = SolverFactory("cbc")
    result = solver.solve(model)

    if result.solver.termination_condition != TerminationCondition.optimal:
        return "❌ Solver failed to find an optimal solution."

    selected_projects = [i for i in projects if model.y[i]() >= 0.5]
    total_npv = sum(NPV[i] for i in selected_projects)
    total_cost = sum(Cost[i] for i in selected_projects)
    total_eng = sum(Eng[i] for i in selected_projects)

    summary = (
        "✅ **Optimal Project Selection Found**\n\n"
        f"📌 **Selected Projects:** {selected_projects}\n"
        f"💰 **Total Cost:** ${total_cost:,.0f} / $1,300,000\n"
        f"👷 **Engineers Used:** {total_eng} / 35\n"
        f"💹 **Total Net Present Value (NPV):** ${total_npv:,.0f}"
    )

    return summary