ultima versao

app.py

@@ -0,0 +1,89 @@
+import gradio as gr
+import pandas as pd
+import plotly.express as px
+import matplotlib.pyplot as plt
+import statistics
+
+LIBRARIES = ["ALOY", "APSTUD", "CLI", "TIMOB", "XD"]
+
+def grafico(list_output_mean, list_output_neosp, nome_projeto, pip_choices):
+    list_output_mean = [3.152778, 3.375000, 1.423820, 1.052039, 1.297747, 1.224785, 2.250000, 2.375000, 1.540773, 1.847639, 1.491953, 1.052039, 0.983369, 1.669528, 1.665236, 1.412554, 1.375000, 1.078326, 1.556330, 1.625000, 1.860515, 1.491953, 1.160944, 1.675966, 1.987661, 2.369099, 1.431867, 1.944742, 2.329399, 1.925429]
+    list_output_NEOSP = [3.191631, 3.417342, 1.202562, 0.867979, 1.225224, 1.028501, 2.165318, 2.291910, 1.141041, 1.785504, 1.086850, 0.875381, 0.714992, 1.604599, 1.833541, 0.860600, 1.393656, 1.152935, 1.364006, 1.647414, 1.527748, 1.236909, 1.403306, 1.655692, 1.770828, 1.937058, 0.861534, 1.341726, 1.904503, 1.449757]
+    list_results = [ ["Median Regressor", statistics.mean(list_output_mean)], ["NEOSP-SVR Regressor", statistics.mean(list_output_NEOSP)] ]
+    fig, (ax1, ax2) = plt.subplots(1, 2)
+    # ax1
+    if "Median Regressor" in pip_choices:
+        df_list_output_mean = pd.DataFrame(list_output_mean, columns=["MAE"])   
+        ax1.plot(df_list_output_mean.index, df_list_output_mean["MAE"],  label="Median Regressor")
+    if "NEOSP-SVR Regressor" in pip_choices:
+        df_list_output_NEOSP = pd.DataFrame(list_output_NEOSP, columns=["MAE"])
+        ax1.plot(df_list_output_NEOSP.index, df_list_output_NEOSP["MAE"], label="NEOSP-SVR Regressor")
+    ax1.set_xlabel("Index Execução")
+    ax1.set_ylabel("MAE")
+    ax1.legend()
+    # ax2
+    if "Median Regressor" or "NEOSP-SVR Regressor" in pip_choices:
+        df = pd.DataFrame(list_results, columns = ["Model","MAE"])    
+    if "Median Regressor" in pip_choices:
+        ax2.bar(df["Model"].iloc[[0]], df["MAE"].iloc[[0]])
+    if "NEOSP-SVR Regressor" in pip_choices:
+        ax2.bar(df["Model"].iloc[[1]], df["MAE"].iloc[[1]])
+    if "NEOSP-SVR Regressor" and "NEOSP-SVR Regressor" in pip_choices:
+        ax2.bar(df["Model"], df["MAE"])
+    if "Median Regressor" or "NEOSP-SVR Regressor" in pip_choices:
+        ax2.set_ylabel("MAE Médio")
+        ax2.set_xlabel("Modelos")
+    # graficos geral
+    fig.set_figwidth(15) 
+    fig.set_figheight(4) 
+    fig.suptitle("Projeto {}".format(nome_projeto))
+    return gr.update(value=plt, visible=True)
+
+def create_pip_plot(libraries, pip_choices):
+    if "ALOY" in libraries:
+        list_output_ALOY_mean = [3.152778, 3.375000, 1.423820, 1.052039, 1.297747, 1.224785, 2.250000, 2.375000, 1.540773, 1.847639, 1.491953, 1.052039, 0.983369, 1.669528, 1.665236, 1.412554, 1.375000, 1.078326, 1.556330, 1.625000, 1.860515, 1.491953, 1.160944, 1.675966, 1.987661, 2.369099, 1.431867, 1.944742, 2.329399, 1.925429]
+        list_output_ALOY_NEOSP = [3.191631, 3.417342, 1.202562, 0.867979, 1.225224, 1.028501, 2.165318, 2.291910, 1.141041, 1.785504, 1.086850, 0.875381, 0.714992, 1.604599, 1.833541, 0.860600, 1.393656, 1.152935, 1.364006, 1.647414, 1.527748, 1.236909, 1.403306, 1.655692, 1.770828, 1.937058, 0.861534, 1.341726, 1.904503, 1.449757]
+        return grafico(list_output_ALOY_mean, list_output_ALOY_NEOSP, "ALOY", pip_choices)
+    elif "APSTUD" in libraries:
+        list_output_ALOY_mean = [3.152778, 3.375000, 1.423820, 1.052039, 1.297747, 1.224785, 2.250000, 2.375000, 1.540773, 1.847639, 1.491953, 1.052039, 0.983369, 1.669528, 1.665236, 1.412554, 1.375000, 1.078326, 1.556330, 1.625000, 1.860515, 1.491953, 1.160944, 1.675966, 1.987661, 2.369099, 1.431867, 1.944742, 2.329399, 1.925429]
+        list_output_ALOY_NEOSP = [3.191631, 3.417342, 1.202562, 0.867979, 1.225224, 1.028501, 2.165318, 2.291910, 1.141041, 1.785504, 1.086850, 0.875381, 0.714992, 1.604599, 1.833541, 0.860600, 1.393656, 1.152935, 1.364006, 1.647414, 1.527748, 1.236909, 1.403306, 1.655692, 1.770828, 1.937058, 0.861534, 1.341726, 1.904503, 1.449757]
+        return grafico(list_output_ALOY_mean, list_output_ALOY_NEOSP, "APSTUD", pip_choices)
+    elif "CLI" in libraries:
+        list_output_ALOY_mean = [3.152778, 3.375000, 1.423820, 1.052039, 1.297747, 1.224785, 2.250000, 2.375000, 1.540773, 1.847639, 1.491953, 1.052039, 0.983369, 1.669528, 1.665236, 1.412554, 1.375000, 1.078326, 1.556330, 1.625000, 1.860515, 1.491953, 1.160944, 1.675966, 1.987661, 2.369099, 1.431867, 1.944742, 2.329399, 1.925429]
+        list_output_ALOY_NEOSP = [3.191631, 3.417342, 1.202562, 0.867979, 1.225224, 1.028501, 2.165318, 2.291910, 1.141041, 1.785504, 1.086850, 0.875381, 0.714992, 1.604599, 1.833541, 0.860600, 1.393656, 1.152935, 1.364006, 1.647414, 1.527748, 1.236909, 1.403306, 1.655692, 1.770828, 1.937058, 0.861534, 1.341726, 1.904503, 1.449757]
+        return grafico(list_output_ALOY_mean, list_output_ALOY_NEOSP, "CLI", pip_choices)
+    elif "TIMOB" in libraries:        
+        list_output_ALOY_mean = [3.152778, 3.375000, 1.423820, 1.052039, 1.297747, 1.224785, 2.250000, 2.375000, 1.540773, 1.847639, 1.491953, 1.052039, 0.983369, 1.669528, 1.665236, 1.412554, 1.375000, 1.078326, 1.556330, 1.625000, 1.860515, 1.491953, 1.160944, 1.675966, 1.987661, 2.369099, 1.431867, 1.944742, 2.329399, 1.925429]
+        list_output_ALOY_NEOSP = [3.191631, 3.417342, 1.202562, 0.867979, 1.225224, 1.028501, 2.165318, 2.291910, 1.141041, 1.785504, 1.086850, 0.875381, 0.714992, 1.604599, 1.833541, 0.860600, 1.393656, 1.152935, 1.364006, 1.647414, 1.527748, 1.236909, 1.403306, 1.655692, 1.770828, 1.937058, 0.861534, 1.341726, 1.904503, 1.449757]
+        return grafico(list_output_ALOY_mean, list_output_ALOY_NEOSP, "TIMOB", pip_choices)
+    elif "XD" in libraries:
+        list_output_ALOY_mean = [3.152778, 3.375000, 1.423820, 1.052039, 1.297747, 1.224785, 2.250000, 2.375000, 1.540773, 1.847639, 1.491953, 1.052039, 0.983369, 1.669528, 1.665236, 1.412554, 1.375000, 1.078326, 1.556330, 1.625000, 1.860515, 1.491953, 1.160944, 1.675966, 1.987661, 2.369099, 1.431867, 1.944742, 2.329399, 1.925429]
+        list_output_ALOY_NEOSP = [3.191631, 3.417342, 1.202562, 0.867979, 1.225224, 1.028501, 2.165318, 2.291910, 1.141041, 1.785504, 1.086850, 0.875381, 0.714992, 1.604599, 1.833541, 0.860600, 1.393656, 1.152935, 1.364006, 1.647414, 1.527748, 1.236909, 1.403306, 1.655692, 1.770828, 1.937058, 0.861534, 1.341726, 1.904503, 1.449757]
+        return grafico(list_output_ALOY_mean, list_output_ALOY_NEOSP, "XD", pip_choices)
+    else:
+        return gr.update(visible=True)
+    
+with gr.Blocks() as 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=["Median 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.Plot(visible=False)
+            #issue_plot = gr.Plot(visible=False)
+
+    fetch.click(create_pip_plot, inputs=[libraries, pip], outputs=pip_plot)
+    #fetch.click(create_star_plot, inputs=[libraries, pip], outputs=star_plot)
+    #fetch.click(create_issue_plot, inputs=[libraries, issues], outputs=issue_plot)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,2 @@
+plotly
+helpers