Update app.py

app.py

@@ -843,6 +843,7 @@ def real_time_check(image_file):
     highlighter2 = Highlighter()
     highlighter3=Highlighter()
     image = Image.open(image_file)
+    
     result_model1 = predict_model1(image)
     parts=result_model1.split("\n")
     del parts[-1]
@@ -853,51 +854,41 @@ def real_time_check(image_file):
     result_model2 = predict_model2(image)
     aihub_deplot_generated_title=result_model2.split("\n")[1].split(":")[1]
     aihub_deplot_table=aihub_deplot_convert_to_dataframe(result_model2)
-    image_base_name = os.path.basename(image_file.name).replace("Source","Label")
-    file_name, _ = os.path.splitext(image_base_name)
 
     result_model3=predict_model3(image)
     unichart_table=unichart_convert_to_dataframe(result_model3)
     unichart_generated_title=result_model3.split(" & ")[0].split(" | ")[1]
     
     #aihub_labeling_data_json="./labeling_data/"+file_name+".json"
-      
-    json_path="./ko_deplot_labeling_data.json"    
-    with open(json_path, 'r', encoding='utf-8') as file:   
-        json_data = json.load(file)       
-    for key, value in json_data.items():  
-        if key == file_name:     
-            ko_deplot_labeling_str=value.get("txt").replace("<0x0A>","\n")            
-            ko_deplot_label_title=ko_deplot_labeling_str.split(" \n ")[0].split(" | ")[1]    
-            break
-
-    ko_deplot_label_table=ko_deplot_convert_to_dataframe2(ko_deplot_labeling_str)
+    if os.path.basename(image_file.name).startswith("C_Source"):
+        image_base_name = os.path.basename(image_file.name).replace("Source","Label")
+        file_name, _ = os.path.splitext(image_base_name)
+        json_path="./ko_deplot_labeling_data.json"    
+        with open(json_path, 'r', encoding='utf-8') as file:   
+            json_data = json.load(file)       
+        for key, value in json_data.items():  
+            if key == file_name:     
+                ko_deplot_labeling_str=value.get("txt").replace("<0x0A>","\n")            
+                ko_deplot_label_title=ko_deplot_labeling_str.split(" \n ")[0].split(" | ")[1]    
+                break
+
+        ko_deplot_label_table=ko_deplot_convert_to_dataframe2(ko_deplot_labeling_str)
 
     #aihub_deplot_labeling_str=process_json_file2(aihub_labeling_data_json)
     #aihub_deplot_label_title=aihub_deplot_labeling_str.split("\n")[1].split(":")[1]
     
-    image_row = aihub_deplot_result_df[aihub_deplot_result_df['data_id'] == file_name.replace("Label","Source")]
-    label_table=""
-    label_title=""
-    if not image_row.empty:
-        label_table = image_row['label_table'].values[0]
-        label_title=label_table.split("\n")[1]
-    
-    aihub_deplot_label_table=aihub_deplot_convert_to_dataframe(label_table)
-
-    json_path="./unichart_labeling_data.json"
-    with open(json_path, 'r', encoding='utf-8') as file:
-        json_data = json.load(file)
-    for entry in json_data:
-        if entry["imgname"]==os.path.basename(image_file.name):
-            unichart_labeling_str=entry["label"]
-            unichart_label_title=entry["label"].split(" & ")[0].split(" | ")[1]
-    unichart_label_table=unichart_convert_to_dataframe(unichart_labeling_str)
-
-    ko_deplot_RMS=evaluate_rms(result_model1,ko_deplot_labeling_str)
-    aihub_deplot_RMS=evaluate_rms(result_model2,label_table)
-    unichart_RMS=evaluate_rms(result_model3.replace("Characteristic","Title").replace("&","\n"),unichart_labeling_str.replace("Characteristic","Title").replace("&","\n"))
-    ko_deplot_score_table=pd.DataFrame({
+        json_path="./unichart_labeling_data.json"
+        with open(json_path, 'r', encoding='utf-8') as file:
+            json_data = json.load(file)
+        for entry in json_data:
+            if entry["imgname"]==os.path.basename(image_file.name):
+                unichart_labeling_str=entry["label"]
+                unichart_label_title=entry["label"].split(" & ")[0].split(" | ")[1]
+        unichart_label_table=unichart_convert_to_dataframe(unichart_labeling_str)
+
+        ko_deplot_RMS=evaluate_rms(result_model1,ko_deplot_labeling_str)
+        unichart_RMS=evaluate_rms(result_model3.replace("Characteristic","Title").replace("&","\n"),unichart_labeling_str.replace("Characteristic","Title").replace("&","\n"))
+        ko_deplot_score_table=pd.DataFrame({
     'category': ['precision', 'recall', 'f1'],
     'value': [
         round(ko_deplot_RMS['table_datapoints_precision'],1),
@@ -905,16 +896,7 @@ def real_time_check(image_file):
         round(ko_deplot_RMS['table_datapoints_f1'],1)
     ]
 })
-    aihub_deplot_score_table=pd.DataFrame({
-        'category': ['precision', 'recall', 'f1'],
-        'value': [
-            round(aihub_deplot_RMS['table_datapoints_precision'],1),
-            round(aihub_deplot_RMS['table_datapoints_recall'],1),
-            round(aihub_deplot_RMS['table_datapoints_f1'],1)
-        ]
-        })
-    
-    unichart_score_table=pd.DataFrame({
+        unichart_score_table=pd.DataFrame({
         'category': ['precision', 'recall', 'f1'],
         'value': [
             round(unichart_RMS['table_datapoints_precision'],1),
@@ -923,14 +905,13 @@ def real_time_check(image_file):
         ]
         })
     
-    ko_deplot_generated_df_row=ko_deplot_table.shape[0]
-    aihub_deplot_generated_df_row=aihub_deplot_table.shape[0]
-    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_aihub_deplot_table=aihub_deplot_table.style.applymap(highlighter2.compare_and_highlight,target_table=aihub_deplot_label_table,pred_table_row=aihub_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)") , gr.DataFrame(styled_aihub_deplot_table,label=aihub_deplot_generated_title+"(aihub deplot 추론 결과)"),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)"),gr.DataFrame(aihub_deplot_label_table,label=label_title+"(aihub deplot 정답 테이블)"),gr.DataFrame(unichart_label_table,label=unichart_label_title+"(VAIV_UniChart 정답 테이블)"),ko_deplot_score_table, aihub_deplot_score_table,unichart_score_table
-    #return ko_deplot_table,aihub_deplot_table,aihub_deplot_label_table,ko_deplot_score_table,aihub_deplot_score_table
+        ko_deplot_generated_df_row=ko_deplot_table.shape[0]
+        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,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
 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= real_time_check(image_uploader)