ultima versao

app.py

@@ -6,35 +6,32 @@ import numpy as np
 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", np.mean(list_output_mean)], [
-        "NEOSP-SVR Regressor", np.mean(list_output_NEOSP)]]
+    
+    list_results = [["MbR Regressor", np.mean(list_output_mean)], [
+        "NEOSP-SVR Regressor", np.mean(list_output_neosp)]]
     fig, (ax1, ax2) = plt.subplots(1, 2)
     # ax1
-    if "Median Regressor" in pip_choices:
+    if "MbR 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")
+                 df_list_output_mean["MAE"],  label="MbR Regressor")
     if "NEOSP-SVR Regressor" in pip_choices:
-        df_list_output_NEOSP = pd.DataFrame(list_output_NEOSP, columns=["MAE"])
+        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:
+    if "MbR Regressor" or "NEOSP-SVR Regressor" in pip_choices:
         df = pd.DataFrame(list_results, columns=["Model", "MAE"])
-    if "Median Regressor" in pip_choices:
+    if "MbR 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:
+    if "MbR Regressor" or "NEOSP-SVR Regressor" in pip_choices:
         ax2.set_ylabel("MAE Médio")
         ax2.set_xlabel("Modelos")
     # graficos geral
@@ -82,7 +79,7 @@ with gr.Blocks() as demo:
         with gr.Column():
             gr.Markdown("## Gráficos")
             pip = gr.CheckboxGroup(
-                choices=["Median Regressor", "NEOSP-SVR Regressor"], label="Modelos Preditivos")
+                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():

requirements.txt

@@ -1,4 +1 @@
-matplotlib
-gradio
-pandas
-numpy
+matplotlib