Update appStore/vulnerability_analysis.py

appStore/vulnerability_analysis.py

@@ -62,105 +62,13 @@ def app():
                 classifier = load_vulnerabilityClassifier(classifier_name=params['model_name'])
                 st.session_state['{}_classifier'.format(classifier_identifier)] = classifier
 
-                # if sum(df['Target Label'] == 'TARGET') > 100:
-                #     warning_msg = ": This might take sometime, please sit back and relax."
-                # else:
-                #     warning_msg = ""
-                    
+    
+                # Get the predictions    
                 df = vulnerability_classification(haystack_doc=df,
                                             threshold= params['threshold'])
 
+                # Store df in session state
                 st.session_state.key0 = df
 
 
-                # # st.write(df)
-                # threshold= params['threshold']
-                # truth_df = df.drop(['text'],axis=1)
-                # truth_df = truth_df.astype(float) >= threshold
-                # truth_df = truth_df.astype(str)
-                # categories = list(truth_df.columns)
-
-                # placeholder = {}
-                # for val in categories:
-                #     placeholder[val] = dict(truth_df[val].value_counts())
-                # count_df = pd.DataFrame.from_dict(placeholder)
-                # count_df = count_df.T
-                # count_df = count_df.reset_index()
-                # # st.write(count_df)
-                # placeholder  = []
-                # for i in range(len(count_df)):
-                #     placeholder.append([count_df.iloc[i]['index'],count_df['True'][i],'Yes'])
-                #     placeholder.append([count_df.iloc[i]['index'],count_df['False'][i],'No'])
-                # count_df = pd.DataFrame(placeholder, columns = ['category','count','truth_value'])
-                # # st.write("Total Paragraphs: {}".format(len(df)))
-                # fig = px.bar(count_df, x='category', y='count',
-                #             color='truth_value')
-                # # c1, c2 = st.columns([1,1])
-                # # with c1:
-                # st.plotly_chart(fig,use_container_width= True)
-
-                # truth_df['labels'] = truth_df.apply(lambda x: {i if x[i]=='True' else None for i in categories}, axis=1)
-                # truth_df['labels'] = truth_df.apply(lambda x: list(x['labels'] -{None}),axis=1)
-                # # st.write(truth_df)
-                # df = pd.concat([df,truth_df['labels']],axis=1)
-                # df['Validation'] =  'No'
-                # df['Sector1'] = 'Blank'
-                # df['Sector2'] = 'Blank'
-                # df['Sector3'] = 'Blank'
-                # df['Sector4'] = 'Blank'
-                # df['Sector5'] = 'Blank'
-                # df_xlsx = to_excel(df,categories)
-                # st.download_button(label='📥 Download Current Result',
-                #                 data=df_xlsx ,
-            #     #               file_name= 'file_sector.xlsx')
-            # else:
-            #     st.info("🤔 No document found, please try to upload it at the sidebar!")
-            #     logging.warning("Terminated as no document provided")
-        
-        # # Creating truth value dataframe
-        # if 'key' in st.session_state:
-        #     if st.session_state.key is not None:
-        #         df = st.session_state.key
-        #         st.markdown("###### Select the threshold for classifier ######")
-        #         c4, c5 = st.columns([1,1])
-
-        #         with c4:                    
-        #             threshold = st.slider("Threshold", min_value=0.00, max_value=1.0,
-        #                                   step=0.01, value=0.5,
-        #                 help = "Keep High Value if want refined result, low if dont want to miss anything" )
-        #         sectors =set(df.columns)
-        #         removecols = {'Validation','Sector1','Sector2','Sector3','Sector4',
-        #                       'Sector5','text'}
-        #         sectors  = list(sectors - removecols)
-
-        #         placeholder = {}
-        #         for val in sectors:
-        #             temp = df[val].astype(float) > threshold
-        #             temp = temp.astype(str)
-        #             placeholder[val] = dict(temp.value_counts())
-                    
-        #         count_df = pd.DataFrame.from_dict(placeholder)
-        #         count_df = count_df.T
-        #         count_df = count_df.reset_index()
-        #         placeholder  = []
-        #         for i in range(len(count_df)):
-        #             placeholder.append([count_df.iloc[i]['index'],count_df['False'][i],'False'])
-        #             placeholder.append([count_df.iloc[i]['index'],count_df['True'][i],'True'])
-
-        #         count_df = pd.DataFrame(placeholder, columns = ['sector','count','truth_value'])
-        #         fig = px.bar(count_df, x='sector', y='count',
-        #                     color='truth_value',
-        #                     height=400)
-        #         st.write("")
-        #         st.plotly_chart(fig)
-
-                # df['Validation'] =  'No'
-                # df['Sector1'] = 'Blank'
-                # df['Sector2'] = 'Blank'
-                # df['Sector3'] = 'Blank'
-                # df['Sector4'] = 'Blank'
-                # df['Sector5'] = 'Blank'
-                # df_xlsx = to_excel(df,sectors)
-                # st.download_button(label='📥 Download Current Result',
-                #                 data=df_xlsx ,
-                #               file_name= 'file_sector.xlsx')
+