Update app.py

app.py

@@ -500,18 +500,15 @@ def table_datapoints_precision_recall_per_point(  # 각각 계산...
     number_theta = 0.1,
 ):
   """Computes precisin recall and F1 metrics given two flattened tables.
-
   Parses each string into a dictionary of keys and values using row and column
   headers. Then we match keys between the two dicts as long as their relative
   levenshtein distance is below a threshold. Values are also compared with
   ANLS if strings or relative distance if they are numeric.
-
   Args:
     targets: list of list of strings.
     predictions: list of strings.
     text_theta: relative edit distance above this is set to the maximum of 1.
     number_theta: relative error rate above this is set to the maximum of 1.
-
   Returns:
     Dictionary with per-point precision, recall and F1
   """
@@ -538,16 +535,13 @@ def table_datapoints_precision_recall(  # deplot 성능지표
     number_theta = 0.1,
 ):
   """Aggregated version of table_datapoints_precision_recall_per_point().
-
   Same as table_datapoints_precision_recall_per_point() but returning aggregated
   scores instead of per-point scores.
-
   Args:
     targets: list of list of strings.
     predictions: list of strings.
     text_theta: relative edit distance above this is set to the maximum of 1.
     number_theta: relative error rate above this is set to the maximum of 1.
-
   Returns:
     Dictionary with aggregated precision, recall and F1
   """
@@ -751,36 +745,29 @@ def non_real_time_check(file):
             round(aihub_deplot_RMS['table_datapoints_f1'],1)
         ]
     })
-
-    try:
-        ko_deplot_generated_df=ko_deplot_convert_to_dataframe2(ko_deplot_generated_table)
-        unichart_generated_df=unichart_convert_to_dataframe(unichart_generated_table)
-    except Exception as e:
-        return None,None,None,None,None,None,None,None,None,ko_deplot_generated_table,unichart_generated_table,1
-    
-    #ko_deplot_generated_df=ko_deplot_convert_to_dataframe(ko_deplot_generated_table)
-    #aihub_deplot_generated_df=aihub_deplot_convert_to_dataframe(aihub_deplot_generated_table)
-    #unichart_generated_df=unichart_convert_to_dataframe(unichart_generated_table)
-    
+        
+    ko_deplot_generated_df=ko_deplot_convert_to_dataframe(ko_deplot_generated_table)
+    aihub_deplot_generated_df=aihub_deplot_convert_to_dataframe(aihub_deplot_generated_table)
+    unichart_generated_df=unichart_convert_to_dataframe(unichart_generated_table)
     ko_deplot_labeling_df=ko_deplot_convert_to_dataframe2(ko_deplot_labeling_str)
-    #aihub_deplot_labeling_df=aihub_deplot_convert_to_dataframe(aihub_deplot_label_table)
+    aihub_deplot_labeling_df=aihub_deplot_convert_to_dataframe(aihub_deplot_label_table)
     unichart_labeling_df=unichart_convert_to_dataframe(unichart_labeling_str)
 
     ko_deplot_generated_df_row=ko_deplot_generated_df.shape[0]
-    #aihub_deplot_generated_df_row=aihub_deplot_generated_df.shape[0]
+    aihub_deplot_generated_df_row=aihub_deplot_generated_df.shape[0]
     unichart_generated_df_row=unichart_generated_df.shape[0]
 
 
     styled_ko_deplot_table=ko_deplot_generated_df.style.applymap(highlighter1.compare_and_highlight,target_table=ko_deplot_labeling_df,pred_table_row=ko_deplot_generated_df_row,props='color:red')
     
 
-    #styled_aihub_deplot_table=aihub_deplot_generated_df.style.applymap(highlighter2.compare_and_highlight,target_table=aihub_deplot_labeling_df,pred_table_row=aihub_deplot_generated_df_row,props='color:red')
+    styled_aihub_deplot_table=aihub_deplot_generated_df.style.applymap(highlighter2.compare_and_highlight,target_table=aihub_deplot_labeling_df,pred_table_row=aihub_deplot_generated_df_row,props='color:red')
 
 
     styled_unichart_table=unichart_generated_df.style.applymap(highlighter3.compare_and_highlight,target_table=unichart_labeling_df,pred_table_row=unichart_generated_df_row,props='color:red')
 
     #return ko_deplot_convert_to_dataframe(ko_deplot_generated_table), aihub_deplot_convert_to_dataframe(aihub_deplot_generated_table), aihub_deplot_convert_to_dataframe(label_table), ko_deplot_score_table, aihub_deplot_score_table
-    return gr.DataFrame(styled_ko_deplot_table,label=ko_deplot_generated_title+"(VAIV_DePlot 추론 결과)"),None,gr.DataFrame(styled_unichart_table,label="제목:"+unichart_generated_title+"(VAIV_UniChart 추론 결과)"),gr.DataFrame(ko_deplot_labeling_df,label=ko_deplot_label_title+"(VAIV_DePlot 정답 테이블)"),None,gr.DataFrame(unichart_labeling_df,label="제목:"+unichart_label_title+"(VAIV_UniChart 정답 테이블)"),ko_deplot_score_table,None,unichart_score_table,None,None,0
+    return gr.DataFrame(styled_ko_deplot_table,label=ko_deplot_generated_title+"(VAIV_DePlot 추론 결과)"),gr.DataFrame(styled_aihub_deplot_table,label=aihub_deplot_generated_title+"(aihub deplot 추론 결과)"),gr.DataFrame(styled_unichart_table,label="제목:"+unichart_generated_title+"(VAIV_UniChart 추론 결과)"),gr.DataFrame(ko_deplot_labeling_df,label=ko_deplot_label_title+"(VAIV_DePlot 정답 테이블)"), gr.DataFrame(aihub_deplot_labeling_df,label=aihub_deplot_label_title+"(aihub deplot 정답 테이블)"),gr.DataFrame(unichart_labeling_df,label="제목:"+unichart_label_title+"(VAIV_UniChart 정답 테이블)"),ko_deplot_score_table, aihub_deplot_score_table,unichart_score_table
 
 
 def ko_deplot_display_results(index):
@@ -839,17 +826,11 @@ def real_time_check(image_file):
     del parts[-1]
     result_model1="\n".join(parts)
     ko_deplot_generated_title=result_model1.split("\n")[0].split(" | ")[1]
-    #ko_deplot_table=ko_deplot_convert_to_dataframe2(result_model1)
+    ko_deplot_table=ko_deplot_convert_to_dataframe2(result_model1)
 
     result_model3=predict_model3(image)
-    #unichart_table=unichart_convert_to_dataframe(result_model3)
+    unichart_table=unichart_convert_to_dataframe(result_model3)
     unichart_generated_title=result_model3.split(" & ")[0].split(" | ")[1]
-
-    try:
-        ko_deplot_table=ko_deplot_convert_to_dataframe2(result_model1)
-        unichart_table=unichart_convert_to_dataframe(result_model3)
-    except Exception as e:
-        return None,None,None,None,None,None,None,None,None,result_model1,result_model3,1
     
     #aihub_labeling_data_json="./labeling_data/"+file_name+".json"
     if os.path.basename(image_file.name).startswith("C_Source"):
@@ -901,22 +882,16 @@ def real_time_check(image_file):
         unichart_generated_df_row=unichart_table.shape[0]
         styled_ko_deplot_table=ko_deplot_table.style.applymap(highlighter1.compare_and_highlight,target_table=ko_deplot_label_table,pred_table_row=ko_deplot_generated_df_row,props='color:red')
         styled_unichart_table=unichart_table.style.applymap(highlighter3.compare_and_highlight,target_table=unichart_label_table,pred_table_row=unichart_generated_df_row,props='color:red')
-        return gr.DataFrame(styled_ko_deplot_table,label=ko_deplot_generated_title+"(VAIV_DePlot 추론 결과)") ,None,gr.DataFrame(styled_unichart_table,label=unichart_generated_title+"(VAIV_UniChart 추론 결과)"),gr.DataFrame(ko_deplot_label_table,label=ko_deplot_label_title+"(VAIV_DePlot 정답 테이블)"),None,gr.DataFrame(unichart_label_table,label=unichart_label_title+"(VAIV_UniChart 정답 테이블)"),ko_deplot_score_table,None,unichart_score_table,None,None,0
+        return gr.DataFrame(styled_ko_deplot_table,label=ko_deplot_generated_title+"(VAIV_DePlot 추론 결과)") ,None,gr.DataFrame(styled_unichart_table,label=unichart_generated_title+"(VAIV_UniChart 추론 결과)"),gr.DataFrame(ko_deplot_label_table,label=ko_deplot_label_title+"(VAIV_DePlot 정답 테이블)"),None,gr.DataFrame(unichart_label_table,label=unichart_label_title+"(VAIV_UniChart 정답 테이블)"),ko_deplot_score_table,None,unichart_score_table
     else:
-        return gr.DataFrame(ko_deplot_table,label=ko_deplot_generated_title+"(VAIV_DePlot 추론 결과)"),None,gr.DataFrame(unichart_table,label=unichart_generated_title+"(VAIV_UniChart 추론 결과)"),None,None,None,None,None,None,None,None,0
+        return gr.DataFrame(ko_deplot_table,label=ko_deplot_generated_title+"(VAIV_DePlot 추론 결과)"),None,gr.DataFrame(unichart_table,label=unichart_generated_title+"(VAIV_UniChart 추론 결과)"),None,None,None,None,None,None
 def inference(mode,image_uploader,file_uploader):
     if(mode=="이미지 업로드"):
-        ko_deplot_table, aihub_deplot_table, unichart_table, ko_deplot_label_table,aihub_deplot_label_table,unichart_label_table,ko_deplot_score_table, aihub_deplot_score_table,unichart_score_table,ko_deplot_generated_txt,unichart_generated_txt,flag= real_time_check(image_uploader)
-        if flag==1:
-            return ko_deplot_table, aihub_deplot_table, unichart_table,ko_deplot_label_table, aihub_deplot_label_table,unichart_label_table,ko_deplot_score_table, aihub_deplot_score_table,unichart_score_table,gr.Text(ko_deplot_generated_txt,visible=True),gr.Text(unichart_generated_txt,visible=True)
-        else:
-            return ko_deplot_table, aihub_deplot_table, unichart_table,ko_deplot_label_table, aihub_deplot_label_table,unichart_label_table,ko_deplot_score_table, aihub_deplot_score_table,unichart_score_table,gr.update(visible=False),gr.update(visible=False)
+        ko_deplot_table, aihub_deplot_table, unichart_table, ko_deplot_label_table,aihub_deplot_label_table,unichart_label_table,ko_deplot_score_table, aihub_deplot_score_table,unichart_score_table= real_time_check(image_uploader)
+        return ko_deplot_table, aihub_deplot_table, unichart_table,ko_deplot_label_table, aihub_deplot_label_table,unichart_label_table,ko_deplot_score_table, aihub_deplot_score_table,unichart_score_table
     else:
-        styled_ko_deplot_table,styled_aihub_deplot_table,styled_unichart_table,ko_deplot_label_table,aihub_deplot_label_table,unichart_label_table,ko_deplot_score_table,aihub_deplot_score_table, unichart_score_table,ko_deplot_generated_txt,unichart_generated_txt,flag=non_real_time_check(file_uploader)
-        if flag==1:
-            return styled_ko_deplot_table, styled_aihub_deplot_table, styled_unichart_table,ko_deplot_label_table,aihub_deplot_label_table,unichart_label_table,ko_deplot_score_table, aihub_deplot_score_table, unichart_score_table,gr.Text(ko_deplot_generated_txt,visible=True),gr.Text(unichart_generated_txt,visible=True)
-        else:
-            return styled_ko_deplot_table, styled_aihub_deplot_table, styled_unichart_table,ko_deplot_label_table,aihub_deplot_label_table,unichart_label_table,ko_deplot_score_table, aihub_deplot_score_table, unichart_score_table,gr.update(visible=False),gr.update(visible=False)
+        styled_ko_deplot_table,styled_aihub_deplot_table,styled_unichart_table,ko_deplot_label_table,aihub_deplot_label_table,unichart_label_table,ko_deplot_score_table,aihub_deplot_score_table, unichart_score_table=non_real_time_check(file_uploader)
+        return styled_ko_deplot_table, styled_aihub_deplot_table, styled_unichart_table,ko_deplot_label_table,aihub_deplot_label_table,unichart_label_table,ko_deplot_score_table, aihub_deplot_score_table, unichart_score_table
 def interface_selector(selector):
     if selector == "이미지 업로드":
         return gr.update(visible=True),gr.update(visible=False),gr.State("image_upload"),gr.update(visible=False),gr.update(visible=False),gr.File("./new_top_20_percent_images.txt"),"high score 차트"
@@ -1059,8 +1034,6 @@ with gr.Blocks(css=css) as iface:
             ko_deplot_generated_table=gr.DataFrame(visible=True,label="VAIV_DePlot 추론 결과",elem_classes="dataframe-class")
             aihub_deplot_generated_table=gr.DataFrame(visible=False,label="aihub-deplot 추론 결과",elem_classes="dataframe-class")
             unichart_generated_table=gr.DataFrame(visible=False,label="VAIV_UniChart 추론 결과",elem_classes="dataframe-class")
-            ko_deplot_generated_txt=gr.Text(visible=False,label="VAIV_DePlot 추론 결과")
-            unichart_generated_txt=gr.Text(visible=False,label="VAIV_UniChart 추론 결과")
         with gr.Column():
             ko_deplot_label_table=gr.DataFrame(visible=True,label="VAIV_DePlot 정답테이블",elem_classes="dataframe-class")
             aihub_deplot_label_table=gr.DataFrame(visible=False,label="aihub-deplot 정답테이블",elem_classes="dataframe-class")
@@ -1092,7 +1065,7 @@ with gr.Blocks(css=css) as iface:
     file_uploader.change(display_image_in_file,inputs=[file_uploader],outputs=[image_displayer,image_name])
     pre_button.click(previous_image, outputs=[image_displayer,image_name,pre_button,next_button])
     next_button.click(next_image, outputs=[image_displayer,image_name,pre_button,next_button])
-    inference_button.click(inference,inputs=[upload_option,image_uploader,file_uploader],outputs=[ko_deplot_generated_table, aihub_deplot_generated_table, unichart_generated_table, ko_deplot_label_table, aihub_deplot_label_table, unichart_label_table, ko_deplot_score_table, aihub_deplot_score_table,unichart_score_table,ko_deplot_generated_txt,unichart_generated_txt])
+    inference_button.click(inference,inputs=[upload_option,image_uploader,file_uploader],outputs=[ko_deplot_generated_table, aihub_deplot_generated_table, unichart_generated_table, ko_deplot_label_table, aihub_deplot_label_table, unichart_label_table, ko_deplot_score_table, aihub_deplot_score_table,unichart_score_table])
 
 if __name__ == "__main__":
     print("Launching Gradio interface...")
@@ -1104,4 +1077,4 @@ if __name__ == "__main__":
         # Gradio가 제공하는 URLs을 파일에 기록합니다.
     with open("gradio_url.log", "w") as f:
         print(iface.local_url, file=f)
-        print(iface.share_url, file=f)
+        print(iface.share_url, file=f)