app.py

import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import csv
from scipy.stats import wilcoxon

LIBRARIES = ["ALOY", "APSTUD", "CLI", "CLOV", "COMPASS", "CONFCLOUD", "CONFSERVER", "DAEMON", "DM", "DNN", "DURACLOUD", "EVG", "FAB", 
             "MDL", "MESOS" ,"MULE", "NEXUS", "SERVER", "STL", "TIDOC", "TIMOB", "TISTUD", "XD"]

def grafico(list_output_MbR, list_output_NEOSP, nome_projeto, pip_choices):
    list_results = [["MbR Regressor", np.mean(list_output_MbR)], ["NEOSP-SVR Regressor", np.mean(list_output_NEOSP)]]
    df = pd.DataFrame(list_results, columns=["Model", "MAE"])
    df_list_output_MbR = pd.DataFrame(list_output_MbR, columns=["MAE"])
    df_list_output_NEOSP = pd.DataFrame(list_output_NEOSP, columns=["MAE"])
    fig, (ax1, ax2, ax3)= plt.subplots(1, 3)
    
    # ax1
    ax1.set_xlabel("Index Execução")
    ax1.set_ylabel("MAE")
    ax1.legend()
    if "MbR Regressor" in pip_choices:
        ax1.plot(df_list_output_MbR.index, df_list_output_MbR["MAE"],  label="MbR Regressor", color="red",  alpha=0.5)
    if "NEOSP-SVR Regressor" in pip_choices:
        ax1.plot(df_list_output_NEOSP.index, df_list_output_NEOSP["MAE"], label="NEOSP-SVR Regressor", color="blue",  alpha=0.5)

    # ax2
    ax2.set_ylabel("MAE Médio")
    ax2.set_xlabel("Modelos")
    if "MbR Regressor" in pip_choices:
        graf1 = ax2.bar(df["Model"].iloc[[0]], df["MAE"].iloc[[0]], color="red", alpha=0.5)
        ax2.bar_label(graf1, fmt="%.01f", size=10, label_type="edge")    
    if "NEOSP-SVR Regressor" in pip_choices:
        graf2 = ax2.bar(df["Model"].iloc[[1]], df["MAE"].iloc[[1]], color = "blue",  alpha=0.5)
        ax2.bar_label(graf2, fmt="%.01f", size=10, label_type="edge")
    
    ax3.set_xlabel("MAE")
    ax3.set_ylabel("Frequência")
    if "MbR Regressor" in pip_choices:
        ax3.hist(df_list_output_MbR["MAE"], color="red",  alpha=0.5)
    if "NEOSP-SVR Regressor" in pip_choices:
        ax3.hist(df_list_output_NEOSP["MAE"], color="blue", alpha=0.5)
            
    # graficos geral
    fig.set_figwidth(15)
    fig.set_figheight(4)
    fig.suptitle("Projeto {}".format(nome_projeto))
    # text
        
    resultado = ""
    if ("MbR Regressor" and "NEOSP-SVR Regressor") in pip_choices:
        res = wilcoxon(list_output_MbR, list_output_NEOSP)
        resultado = "MbR vs. NEOSP-SVR -> Wilcoxon -> Statistics: {} | valor-p: {}".format(res.statistic, res.pvalue)
    
    return gr.update(value=plt, visible=True), gr.update(value=resultado, visible=True)


def create_pip_plot(libraries, pip_choices):
    nome_projeto = libraries
    list_output_MbR = []
    with open("metricas_{}_MbR.csv".format(nome_projeto), "r") as arquivo:
        arquivo_csv = csv.reader(arquivo)
        for i, linha in enumerate(arquivo_csv):
            list_output_MbR.append(float(linha[0]))
    list_output_NEOSP_SVR = []
    with open("metricas_{}_NEOSP_SVR.csv".format(nome_projeto), "r") as arquivo:
        arquivo_csv = csv.reader(arquivo)
        for i, linha in enumerate(arquivo_csv):
            list_output_NEOSP_SVR.append(float(linha[0]))
    return grafico(list_output_MbR, list_output_NEOSP_SVR, nome_projeto, pip_choices)
    
    
demo = gr.Blocks()

with demo:
    with gr.Row():
        with gr.Column():
            gr.Markdown("## Conjunto de Dados")
            libraries = gr.Dropdown(choices=LIBRARIES, label="Projeto", value="ALOY")
        with gr.Column():
            gr.Markdown("## Gráficos")
            pip = gr.CheckboxGroup(choices=["MbR Regressor", "NEOSP-SVR Regressor"], label="Modelos Preditivos")
            # stars = gr.CheckboxGroup(choices=["Stars", "Week over Week"], label="")
            # issues = gr.CheckboxGroup(choices=["Issue", "Exclude org members", "week over week"], label="")
    with gr.Row():
        fetch = gr.Button(value="Fetch")
    with gr.Row():
        with gr.Column():
            pip_plot = gr.Plot(visible=False)
            star_plot = gr.Text(visible=False)
            # issue_plot = gr.Plot(visible=False)

    fetch.click(create_pip_plot, inputs=[libraries, pip], outputs=[pip_plot, star_plot])
    #fetch.click(create_star_plot, inputs=[libraries, pip], outputs=star_plot)
    # fetch.click(create_issue_plot, inputs=[libraries, issues], outputs=issue_plot)

demo.launch()