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import gradio as gr
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Função de simulação, ajustada para Gradio
def run_monte_carlo_simulation(tasks_list_of_tuples, n_simulations):
task_names_original = [
'1. Requisitos', '2. Arquitetura', '3. Back-end',
'4. Front-end', '5. Testes', '6. Implantação'
]
tasks = [{'tarefa': name, 'a': a, 'm': m, 'b': b} for name, (a, m, b) in zip(task_names_original, tasks_list_of_tuples)]
task_names = [task['tarefa'] for task in tasks]
n_simulations = int(n_simulations)
simulation_results = {name: np.zeros(n_simulations) for name in task_names}
for i in range(n_simulations):
for task in tasks:
a, m, b = task['a'], task['m'], task['b']
if not (a > 0 and a <= m and m <= b):
raise gr.Error(f"Parâmetros inválidos na tarefa: {task['tarefa']}. Garanta que Otimista <= Provável <= Pessimista.")
mu = (a + 4 * m + b) / 6
if b - a == 0:
task_duration = a
else:
gamma = 4.0
alpha = 1 + gamma * (mu - a) / (b - a)
beta = 1 + gamma * (b - mu) / (b - a)
sample = np.random.beta(alpha, beta)
task_duration = a + sample * (b - a)
simulation_results[task['tarefa']][i] = task_duration
results_df = pd.DataFrame(simulation_results)
results_df['Duração Total'] = results_df.sum(axis=1)
return results_df
# Função principal da interface
def generate_analysis(*args):
n_simulations = args[-1]
task_values = args[:-1]
tasks_list_of_tuples = [tuple(task_values[i:i+3]) for i in range(0, len(task_values), 3)]
results_df = run_monte_carlo_simulation(tasks_list_of_tuples, n_simulations)
durations = results_df['Duração Total']
summary_df = pd.DataFrame({
'Métrica': ['Prazo Realista (50%)', 'Prazo de Segurança (95%)', 'Nível de Incerteza'],
'Valor (dias)': [f"{np.median(durations):.1f}", f"{np.percentile(durations, 95):.1f}", f"{np.std(durations):.1f}"]
}).set_index('Métrica')
fig_hist, ax_hist = plt.subplots(figsize=(10, 6))
sns.histplot(durations, kde=True, bins=50, ax=ax_hist, color="skyblue")
ax_hist.axvline(np.median(durations), color='green', linestyle='-', label=f'Prazo Realista: {np.median(durations):.1f} dias')
ax_hist.axvline(np.percentile(durations, 95), color='purple', linestyle=':', lw=2, label=f'Prazo de Segurança: {np.percentile(durations, 95):.1f} dias')
ax_hist.set_title('Distribuição de Resultados', fontsize=16)
ax_hist.set_xlabel('Duração Total (dias)')
ax_hist.set_ylabel('Frequência')
ax_hist.legend()
task_names_original = ['1. Requisitos', '2. Arquitetura', '3. Back-end', '4. Front-end', '5. Testes', '6. Implantação']
correlations = results_df.corr(numeric_only=True)['Duração Total'].drop('Duração Total')
correlations.index = task_names_original
sorted_correlations = correlations.abs().sort_values(ascending=False)
fig_tornado, ax_tornado = plt.subplots(figsize=(10, 6))
sns.barplot(x=sorted_correlations.values, y=sorted_correlations.index, orient='h', ax=ax_tornado, palette='viridis')
ax_tornado.set_title('Análise de Sensibilidade', fontsize=16)
ax_tornado.set_xlabel('Força de Impacto no Cronograma')
return summary_df, fig_hist, fig_tornado
# Construção da Interface com Gradio
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("# 🗺️ Simulador Interativo de Risco de Projetos")
gr.Markdown("Altere as estimativas de cada tarefa para analisar diferentes cenários e seus impactos.")
task_inputs = []
task_names = ['1. Requisitos', '2. Arquitetura', '3. Back-end', '4. Front-end', '5. Testes', '6. Implantação']
initial_values = [(5, 7, 15), (8, 10, 18), (20, 25, 45), (15, 20, 30), (10, 12, 20), (1, 2, 4)]
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### ⚙️ Parâmetros de Entrada")
for i, name in enumerate(task_names):
with gr.Accordion(name, open=(i < 2)):
a = gr.Slider(1, 100, value=initial_values[i][0], step=1, label="Otimista (a)")
m = gr.Slider(1, 100, value=initial_values[i][1], step=1, label="Provável (m)")
b = gr.Slider(1, 100, value=initial_values[i][2], step=1, label="Pessimista (b)")
task_inputs.extend([a, m, b])
n_sim = gr.Slider(1000, 100000, value=50000, step=1000, label="Nº de Simulações")
task_inputs.append(n_sim)
run_button = gr.Button("Analisar Cenário", variant="primary")
with gr.Column(scale=2):
gr.Markdown("### 🔍 Resultados da Análise")
summary_output = gr.DataFrame(headers=["Métrica", "Valor (dias)"], row_count=3, col_count=2)
with gr.Tabs():
with gr.TabItem("Distribuição de Resultados"):
hist_output = gr.Plot()
with gr.TabItem("Análise de Sensibilidade"):
tornado_output = gr.Plot()
run_button.click(
fn=generate_analysis,
inputs=task_inputs,
outputs=[summary_output, hist_output, tornado_output]
)
demo.launch()
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