import simpy
import random
import pandas as pd

waiting_times = []

def source(env, number, interval, counter):
    """Gera clientes aleatoriamente"""
    for i in range(number):
        c = customer(env, f'Cliente {i}', counter, time_in_bank=30.0)
        env.process(c)
        t = random.expovariate(1.0 / interval)
        yield env.timeout(t)

def customer(env, name, counter, time_in_bank):
    """O cliente chega, é atendido e depois sai."""
    arrive = env.now
    with counter.request() as req:
        yield req
        wait = env.now - arrive
        waiting_times.append(wait)
        tib = random.expovariate(1.0 / time_in_bank)
        yield env.timeout(tib)

def run_simulation():
    env = simpy.Environment()
    counter = simpy.Resource(env, capacity=5)
    env.process(source(env, int(6*60/7), 7, counter))
    env.run()

def print_progress_bar(iteration, total, prefix='', suffix='', length=50, fill='█'):
    percent = ("{0:.1f}").format(100 * (iteration / float(total)))
    filledLength = int(length * iteration // total)
    bar = fill * filledLength + '-' * (length - filledLength)
    print(f'\r{prefix} |{bar}| {percent}% {suffix}', end='\r')
    if iteration == total: 
        print()

def main():
    repetitions = 100000
    results = []
    
    for i in range(repetitions):
        global waiting_times
        waiting_times = []
        
        run_simulation()
        
        total_clients = len(waiting_times)
        avg_wait = sum(waiting_times) / total_clients if waiting_times else 0
        max_wait = max(waiting_times) if waiting_times else 0
        avg_queue = sum(waiting_times) / (6*60) if waiting_times else 0
        service_level = len([w for w in waiting_times if w > 15]) / total_clients if waiting_times else 0

        results.append({
            'Total de Clientes': total_clients,
            'Tempo Médio de Espera': avg_wait,
            'Tempo Máximo de Espera': max_wait,
            'Clientes Médios na Espera': avg_queue,
            'Nível de Serviço > 15min': service_level
        })

        print_progress_bar(i + 1, repetitions, prefix='Progress:', suffix='Complete', length=50)
        
    save_to_excel(results)

def save_to_excel(results):
    df = pd.DataFrame(results)
    df.to_excel("resultados.xlsx", index=False)

if __name__ == "__main__":
    main()