Update appStore/adapmit.py

appStore/adapmit.py

@@ -1,174 +1,38 @@
-# 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.adapmit_classifier import load_adapmitClassifier,adapmit_classification
-# from utils.keyword_extraction import textrank
-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
-
-# Declare all the necessary variables
-classifier_identifier = 'adapmit'
-params  = get_classifier_params(classifier_identifier)
-
-@st.cache_data
-def to_excel(df):
-    len_df = len(df)
-    output = BytesIO()
-    writer = pd.ExcelWriter(output, engine='xlsxwriter')
-    df.to_excel(writer, index=False, sheet_name='Sheet1')
-    workbook = writer.book
-    worksheet = writer.sheets['Sheet1']
-    worksheet.data_validation('E2:E{}'.format(len_df), 
-                              {'validate': 'list', 
-                               'source': ['No', 'Yes', 'Discard']})
-    worksheet.data_validation('F2:F{}'.format(len_df), 
-                              {'validate': 'list', 
-                               'source': ['No', 'Yes', 'Discard']})
-    worksheet.data_validation('G2:G{}'.format(len_df), 
-                              {'validate': 'list', 
-                               'source': ['No', 'Yes', 'Discard']})                               
-    writer.save()
-    processed_data = output.getvalue()
-    return processed_data
-
-def app():
-
-    ### Main app code ###
-    with st.container():
-                   
-        if 'key1' in st.session_state:
-            df = st.session_state.key1
-
-            classifier = load_adapmitClassifier(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 = ""
-                    
-            df = adapmit_classification(haystack_doc=df,
-                                        threshold= params['threshold'])
-
-            st.session_state.key1 = 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, y='category', x='count',
-            #                 color='truth_value',orientation='h', height =200)
-            #     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)
-            #     st.markdown("###### Top few 'Mitigation' related paragraph/text ######")
-            #     df = df.sort_values(by = ['Mitigation'], ascending=False)
-            #     for i in range(3):
-            #         if df.iloc[i]['Mitigation'] >= 0.50:
-            #             st.write('**Result {}** (Relevancy Score: {:.2f})'.format(i+1,df.iloc[i]['Mitigation']))
-            #             st.write("\t Text: \t{}".format(df.iloc[i]['text'].replace("\n", " ")))
-                
-            #     st.markdown("###### Top few 'Adaptation' related paragraph/text ######")
-            #     df = df.sort_values(by = ['Adaptation'], ascending=False)
-            #     for i in range(3):
-            #         if df.iloc[i]['Adaptation'] > 0.5:
-            #             st.write('**Result {}** (Relevancy Score: {:.2f})'.format(i+1,df.iloc[i]['Adaptation']))
-            #             st.write("\t Text: \t{}".format(df.iloc[i]['text'].replace("\n", " ")))
-            #     # st.write(df[['text','labels']])
-            #     df['Validation'] =  'No'
-            #     df['Val-Mitigation'] = 'No'
-            #     df['Val-Adaptation'] = 'No'
-            #     df_xlsx = to_excel(df)
-            #     st.download_button(label='📥 Download Current Result',
-            #                     data=df_xlsx ,
-            #                   file_name= 'file_adaptation-mitigation.xlsx')
-            #     # st.session_state.key4 = 
-
-            #     # category =set(df.columns)
-            #     # removecols = {'Validation','Val-Adaptation','Val-Mitigation','text'}
-            #     # category  = list(category - removecols)
-
-            # 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 'key4' in st.session_state:
-        #     if st.session_state.key4 is not None:
-        #         df = st.session_state.key4
-        #         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" )
-        #         category =set(df.columns)
-        #         removecols = {'Validation','Val-Adaptation','Val-Mitigation','text'}
-        #         category  = list(category - removecols)
-
-        #         placeholder = {}
-        #         for val in category:
-        #             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 = ['category','count','truth_value'])
-        #         fig = px.bar(count_df, x='category', y='count',
-        #                     color='truth_value',
-        #                     height=400)
-        #         st.write("")
-        #         st.plotly_chart(fig)
-
-        #         df['Validation'] =  'No'
-        #         df['Val-Mitigation'] = 'No'
-        #         df['Val-Adaptation'] = 'No'
-        #         df_xlsx = to_excel(df)
-        #         st.download_button(label='📥 Download Current Result',
-        #                         data=df_xlsx ,
-        #                       file_name= 'file_adaptation-mitigation.xlsx')
-
-
+# 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.adapmit_classifier import load_adapmitClassifier,adapmit_classification
+# from utils.keyword_extraction import textrank
+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
+
+# Declare all the necessary variables
+classifier_identifier = 'adapmit'
+params  = get_classifier_params(classifier_identifier)
+
+
+def app():
+    ### Main app code ###
+    with st.container():          
+        if 'key1' in st.session_state:
+            df = st.session_state.key1
+
+            classifier = load_adapmitClassifier(classifier_name=params['model_name'])
+            st.session_state['{}_classifier'.format(classifier_identifier)] = classifier
+                    
+            df = adapmit_classification(haystack_doc=df,
+                                        threshold= params['threshold'])
+
+            st.session_state.key1 = df