Update app.py

app.py

@@ -18,11 +18,17 @@ import os
 
 # Load terminology datasets:
 basedir = os.path.dirname(__file__)
-dataLOINC = pd.read_csv(basedir + f'LoincTableCore.csv')
-dataPanels = pd.read_csv(basedir + f'PanelsAndForms-ACW1208Labeled.csv')     
-dataSNOMED = pd.read_csv(basedir + f'sct2_TextDefinition_Full-en_US1000124_20220901.txt',sep='\t')   
-dataOMS = pd.read_csv(basedir + f'SnomedOMS.csv')   
-dataICD10 = pd.read_csv(basedir + f'ICD10Diagnosis.csv')   
+#dataLOINC = pd.read_csv(basedir + "\\" + f'LoincTableCore.csv')
+#dataPanels = pd.read_csv(basedir + "\\" + f'PanelsAndForms-ACW1208Labeled.csv')     
+#dataSNOMED = pd.read_csv(basedir + "\\" + f'sct2_TextDefinition_Full-en_US1000124_20220901.txt',sep='\t')   
+#dataOMS = pd.read_csv(basedir + "\\" + f'SnomedOMS.csv')   
+#dataICD10 = pd.read_csv(basedir + "\\" + f'ICD10Diagnosis.csv') 
+
+dataLOINC = pd.read_csv(f'LoincTableCore.csv')
+dataPanels = pd.read_csv(f'PanelsAndForms-ACW1208Labeled.csv')     
+dataSNOMED = pd.read_csv(f'sct2_TextDefinition_Full-en_US1000124_20220901.txt',sep='\t')   
+dataOMS = pd.read_csv(f'SnomedOMS.csv')   
+dataICD10 = pd.read_csv(f'ICD10Diagnosis.csv')   
 
 dir_path = os.path.dirname(os.path.realpath(__file__))
 EXAMPLES = {}
@@ -168,8 +174,8 @@ def group_by_entity(raw):
                     if g21 != 'Series([]  )': SaveResult(s2, outputFile)
 
                     #SNOMED
-                    g31 = g3['conceptId'].to_string().replace(","," ").replace("\n"," ")
-                    g32 = g3['term'].to_string().replace(","," ").replace("\n"," ")
+                    g31 = g3['conceptId'].to_string().replace(","," ").replace("\n"," ").replace("\l"," ").replace("\r"," ")
+                    g32 = g3['term'].to_string().replace(","," ").replace("\n"," ").replace("\l"," ").replace("\r"," ")
                     s3 = ("SNOMED Terms of entity ," + myEntityGroup + ", with term ," + eterm + ", SNOMED concepts of ," + g31 + ", and SNOMED terms of ," + g32 + ", Label,Value, Label,Value, Label,Value  ")
                     if g31 != 'Series([]  )': SaveResult(s3, outputFile)
 
@@ -240,7 +246,7 @@ def ner(text):
 
     #return (ner_content, meta, label, figure)
     outputDataframe = pd.read_csv(outputFile)
-    outputFile = outputFile.replace(os.path.dirname(__file__) + "\\","")  # Just filename for File download UI output element 
+    #outputFile = outputFile.replace(os.path.dirname(__file__) + "\\","")  # Just filename for File download UI output element 
 
     #return (ner_content, meta, label, figure, outputDataframe, outputFile)
     return (ner_content, outputDataframe, outputFile)
@@ -250,13 +256,13 @@ demo = gr.Blocks()
 with demo:
     gr.Markdown(
     """
-    # 🩺⚕️NLP UI for AI Biomedical Entity Type Recognition with Clinical Terminology Harmonisation
+    # 🩺⚕️NLP Clinical Ontology Biomedical NER Model 
     """
     )
     input = gr.Textbox(label="Note text", value="")
-    output=[
-        gr.HighlightedText(label="NER", combine_adjacent=True)
-    ]
+    #output=[
+    #    gr.HighlightedText(label="NER", combine_adjacent=True)
+    #]
     with gr.Tab("Biomedical Entity Recognition"):
         output=[
             gr.HighlightedText(label="NER", combine_adjacent=True),
@@ -280,12 +286,12 @@ with demo:
             btnOMS = gr.Button("OMS")
             btnICD10 = gr.Button("ICD10")
 
-        output=[
-            gr.HighlightedText(label="NER", combine_adjacent=True),
-            gr.File(label="File"),  # add download link here
-            gr.Dataframe(label="Dataframe", headers=["LOINC", "Panels", "SNOMED", "OMS", "ICD10"]),   # add harmonised output for input corpus here as a dataframe to UI
-            gr.Textbox(placeholder="CT Match Results", lines=10)    # add matched text scratchpad here
-        ]
+        #output=[
+        #    gr.HighlightedText(label="NER", combine_adjacent=True),
+        #    gr.File(label="File"),  # add download link here
+        #    gr.Dataframe(label="Dataframe", headers=["LOINC", "Panels", "SNOMED", "OMS", "ICD10"]),   # add harmonised output for input corpus here as a dataframe to UI
+        #    gr.Textbox(placeholder="CT Match Results", lines=10)    # add matched text scratchpad here
+        #]
         
 
         #textCT = gr.Textbox(placeholder="CT Match Results", lines=10)