Update appStore/target.py

appStore/target.py

@@ -1,27 +1,41 @@
-# # set path
-# import glob, os, sys; 
-# sys.path.append('../utils')
-
-# #import needed libraries
-# import seaborn as sns
-# import matplotlib.pyplot as plt
-# import numpy as np
-# import pandas as pd
-# import streamlit as st
-# from st_aggrid import AgGrid
-# from utils.target_classifier import load_targetClassifier, target_classification 
-# import logging
-# logger = logging.getLogger(__name__)
-# from utils.config import get_classifier_params
-# from io import BytesIO
-# import xlsxwriter
-# import plotly.express as px
-# from pandas.api.types import (
-#     is_categorical_dtype,
-#     is_datetime64_any_dtype,
-#     is_numeric_dtype,
-#     is_object_dtype,
-#     is_list_like)
+# set path
+import glob, os, sys; 
+sys.path.append('../utils')
+
+#import needed libraries
+import seaborn as sns
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import streamlit as st
+from utils.vulnerability_classifier import load_targetClassifier, target_classification
+import logging
+logger = logging.getLogger(__name__)
+from utils.config import get_classifier_params
+from utils.preprocessing import paraLengthCheck
+from io import BytesIO
+import xlsxwriter
+import plotly.express as px
+from utils.vulnerability_classifier import label_dict
+
+
+def app():
+    ### Main app code ###
+    with st.container():
+        
+        if 'key1' in st.session_state:
+
+            # Load the existing dataset
+            df = st.session_state.key1
+
+            # Load the classifier model
+            classifier = load_targetClassifier(classifier_name=params['model_name'])
+            st.session_state['{}_classifier'.format(classifier_identifier)] = classifier
+
+     
+            df = target_classification(haystack_doc=df,
+                                        threshold= params['threshold'])
+            st.session_state.key1 = df
 
 # # Declare all the necessary variables
 # classifier_identifier = 'target'