# Complete PYOMO implementation

import pyomo.environ as pyo
from pyomo.opt import SolverFactory

def optimize_browser_web():
    """Optimization for maximizing browser market share coverage"""

    # 1. MODEL CREATION
    model = pyo.ConcreteModel()

    # 2. DATA SETUP
    # Define the data based on the provided database schema and current stored values
    accelerators = [1, 2, 3]
    browsers = [101, 102, 103, 104]
    market_shares = {101: 0.4, 102: 0.3, 103: 0.15, 104: 0.1}
    compatibility = {
        1: [101, 102],
        2: [103],
        3: [101, 104]
    }
    max_accelerators = 2  # Predefined limit for the number of accelerators

    # 3. SETS
    model.A = pyo.Set(initialize=accelerators)
    model.B = pyo.Set(initialize=browsers)

    # 4. PARAMETERS
    model.market_share = pyo.Param(model.B, initialize=market_shares)

    # 5. VARIABLES
    model.y = pyo.Var(model.A, within=pyo.Binary)
    model.x = pyo.Var(model.B, within=pyo.Binary)

    # 6. OBJECTIVE FUNCTION
    def obj_rule(model):
        return sum(model.market_share[b] * model.x[b] for b in model.B)
    model.objective = pyo.Objective(rule=obj_rule, sense=pyo.maximize)

    # 7. CONSTRAINTS

    # Accelerator Selection Limit
    def accelerator_limit_rule(model):
        return sum(model.y[a] for a in model.A) <= max_accelerators
    model.accelerator_limit = pyo.Constraint(rule=accelerator_limit_rule)

    # Coverage Constraint
    def coverage_rule(model, b):
        compatible_accelerators = [a for a in model.A if b in compatibility[a]]
        return model.x[b] <= sum(model.y[a] for a in compatible_accelerators)
    model.coverage_constraint = pyo.Constraint(model.B, rule=coverage_rule)

    # 8. SOLVING WITH GUROBI
    solver = SolverFactory('gurobi')

    # Solve the model
    results = solver.solve(model, tee=True)

    # 9. RESULT PROCESSING
    if results.solver.termination_condition == pyo.TerminationCondition.optimal:
        print("Optimal solution found!")
        print(f"Optimal value: {pyo.value(model.objective)}")

        # Extract variable values
        print("\nVariable values:")
        for a in model.A:
            y_val = pyo.value(model.y[a])
            print(f"y[{a}] = {int(y_val)}")
        for b in model.B:
            x_val = pyo.value(model.x[b])
            print(f"x[{b}] = {int(x_val)}")

    elif results.solver.termination_condition == pyo.TerminationCondition.infeasible:
        print("Problem is infeasible")
    elif results.solver.termination_condition == pyo.TerminationCondition.unbounded:
        print("Problem is unbounded")
    else:
        print(f"Solver terminated with condition: {results.solver.termination_condition}")

    return model

# Run the optimization
optimize_browser_web()