Update appStore/target.py

appStore/target.py

@@ -8,19 +8,231 @@ import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import streamlit as st
-from utils.target_classifier import load_targetClassifier, target_classification 
+from utils.group_classifier import load_policyactionClassifier, policyaction_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
+
 
 # Declare all the necessary variables
-classifier_identifier = 'target'
+classifier_identifier = 'policyaction'
 params  = get_classifier_params(classifier_identifier)
 
-## Labels dictionary ###
-_lab_dict = {
-            'NEGATIVE':'NO TARGET INFO',
-            'TARGET':'TARGET',
-            }
+@st.cache_data
+def to_excel(df):
+    df['Target Validation'] = 'No'
+    df['Netzero Validation'] = 'No'
+    df['GHG Validation'] = 'No'
+    df['Adapt-Mitig Validation'] = 'No'
+    df['Sector'] = 'No'
+    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('L2:L{}'.format(len_df), 
+                              {'validate': 'list', 
+                               'source': ['No', 'Yes', 'Discard']})
+    worksheet.data_validation('M2:L{}'.format(len_df), 
+                              {'validate': 'list', 
+                               'source': ['No', 'Yes', 'Discard']})
+    worksheet.data_validation('N2:L{}'.format(len_df), 
+                              {'validate': 'list', 
+                               'source': ['No', 'Yes', 'Discard']})
+    worksheet.data_validation('O2:L{}'.format(len_df), 
+                              {'validate': 'list', 
+                               'source': ['No', 'Yes', 'Discard']})
+    worksheet.data_validation('P2:L{}'.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_policyactionClassifier(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 = policyaction_classification(haystack_doc=df,
+                                            threshold= params['threshold'])
+
+                st.session_state.key1 = df
+
+
+
+def action_display():
+    if  'key1' in st.session_state:
+        df = st.session_state.key1
+                
+        
+        df['Action_check']  = df['Policy-Action Label'].apply(lambda x: True if 'Action' in x else False)
+        hits  = df[df['Action_check'] == True]
+        # hits['GHG Label'] = hits['GHG Label'].apply(lambda i: _lab_dict[i])
+        range_val = min(5,len(hits))
+        if range_val !=0:
+            count_action = len(hits)
+            #count_netzero = sum(hits['Netzero Label'] == 'NETZERO')
+            #count_ghg = sum(hits['GHG Label'] == 'GHG')
+            #count_economy = sum([True if 'Economy-wide' in x else False 
+            #                  for x in hits['Sector Label']])
+            
+            # count_df = df['Target Label'].value_counts()
+            # count_df = count_df.rename('count')
+            # count_df = count_df.rename_axis('Target Label').reset_index()
+            # count_df['Label_def'] = count_df['Target Label'].apply(lambda x: _lab_dict[x])
+
+            # fig = px.bar(count_df, y="Label_def", x="count", orientation='h', height=200)
+        #    c1, c2 = st.columns([1,1])
+        #    with c1:
+        #        st.write('**Target Paragraphs**: `{}`'.format(count_target))
+        #        st.write('**NetZero Related Paragraphs**: `{}`'.format(count_netzero))
+#
+ #               # st.plotly_chart(fig,use_container_width= True)
+  #          
+            # count_netzero = sum(hits['Netzero Label'] == 'NETZERO')
+            # count_ghg = sum(hits['GHG Label'] == 'LABEL_2')
+            # count_economy = sum([True if 'Economy-wide' in x else False 
+            #                   for x in hits['Sector Label']])
+     #       with c2:
+      #          st.write('**GHG Related Paragraphs**: `{}`'.format(count_ghg))
+       #         st.write('**Economy-wide Related Paragraphs**: `{}`'.format(count_economy))
+        #    st.write('-------------------')    
+    #        hits = hits.sort_values(by=['Relevancy'], ascending=False)
+     #       netzerohit = hits[hits['Netzero Label'] == 'NETZERO']
+      #      if not netzerohit.empty:
+       #         netzerohit = netzerohit.sort_values(by = ['Netzero Score'], ascending = False)
+        #        # st.write('-------------------')
+                # st.markdown("###### Netzero paragraph ######")
+      #          st.write('**Netzero paragraph** `page {}`: {}'.format(netzerohit.iloc[0]['page'],
+      #                          netzerohit.iloc[0]['text'].replace("\n", " ")))                        
+       #         st.write("")
+        #    else:
+         #       st.info("🤔 No Netzero paragraph found")
+
+            # st.write("**Result {}** `page {}` (Relevancy Score: {:.2f})'".format(i+1,hits.iloc[i]['page'],hits.iloc[i]['Relevancy'])")
+           # st.write('-------------------')
+            st.write("")
+            st.markdown("###### Top few Action Classified paragraph/text results from list of {} classified paragraphs ######".format(count_action))
+            st.markdown("""<hr style="height:10px;border:none;color:#097969;background-color:#097969;" /> """, unsafe_allow_html=True)
+            range_val = min(5,len(hits))
+            for i in range(range_val):
+                # the page number reflects the page that contains the main paragraph 
+                # according to split limit, the overlapping part can be on a separate page
+                st.write('**Result {}** : `page {}`, `Sector: {}`,\
+                            `Indicators: {}`, `Adapt-Mitig :{}`'\
+                    .format(i+1,
+                            hits.iloc[i]['page'], hits.iloc[i]['Sector Label'],
+                            hits.iloc[i]['Indicator Label'],hits.iloc[i]['Adapt-Mitig Label']))                        
+                st.write("\t Text: \t{}".format(hits.iloc[i]['text'].replace("\n", " ")))
+            hits = hits.reset_index(drop =True)
+            st.write('----------------')
+            st.write('Explore the data')
+            st.write(hits)
+            df.drop(columns = ['Action_check'],inplace=True)
+            df_xlsx = to_excel(df)
+            
+            with st.sidebar:
+                st.write('-------------')
+                st.download_button(label='📥 Download Result',
+                            data=df_xlsx ,
+                            file_name= 'cpu_analysis.xlsx')
+
+        else:
+            st.info("🤔 No Actions found")
+
+
+def policy_display():
+    if  'key1' in st.session_state:
+        df = st.session_state.key1
+                
+        
+        df['Policy_check']  = df['Policy-Action Label'].apply(lambda x: True if 'Policies & Plans' in x else False)
+        hits  = df[df['Policy_check'] == True]
+        # hits['GHG Label'] = hits['GHG Label'].apply(lambda i: _lab_dict[i])
+        range_val = min(5,len(hits))
+        if range_val !=0:
+            count_policy = len(hits)
+            #count_netzero = sum(hits['Netzero Label'] == 'NETZERO')
+            #count_ghg = sum(hits['GHG Label'] == 'GHG')
+            #count_economy = sum([True if 'Economy-wide' in x else False 
+            #                  for x in hits['Sector Label']])
+            
+            # count_df = df['Target Label'].value_counts()
+            # count_df = count_df.rename('count')
+            # count_df = count_df.rename_axis('Target Label').reset_index()
+            # count_df['Label_def'] = count_df['Target Label'].apply(lambda x: _lab_dict[x])
+
+            # fig = px.bar(count_df, y="Label_def", x="count", orientation='h', height=200)
+        #    c1, c2 = st.columns([1,1])
+        #    with c1:
+        #        st.write('**Target Paragraphs**: `{}`'.format(count_target))
+        #        st.write('**NetZero Related Paragraphs**: `{}`'.format(count_netzero))
+#
+ #               # st.plotly_chart(fig,use_container_width= True)
+  #          
+            # count_netzero = sum(hits['Netzero Label'] == 'NETZERO')
+            # count_ghg = sum(hits['GHG Label'] == 'LABEL_2')
+            # count_economy = sum([True if 'Economy-wide' in x else False 
+            #                   for x in hits['Sector Label']])
+     #       with c2:
+      #          st.write('**GHG Related Paragraphs**: `{}`'.format(count_ghg))
+       #         st.write('**Economy-wide Related Paragraphs**: `{}`'.format(count_economy))
+        #    st.write('-------------------')    
+    #        hits = hits.sort_values(by=['Relevancy'], ascending=False)
+     #       netzerohit = hits[hits['Netzero Label'] == 'NETZERO']
+      #      if not netzerohit.empty:
+       #         netzerohit = netzerohit.sort_values(by = ['Netzero Score'], ascending = False)
+        #        # st.write('-------------------')
+                # st.markdown("###### Netzero paragraph ######")
+      #          st.write('**Netzero paragraph** `page {}`: {}'.format(netzerohit.iloc[0]['page'],
+      #                          netzerohit.iloc[0]['text'].replace("\n", " ")))                        
+       #         st.write("")
+        #    else:
+         #       st.info("🤔 No Netzero paragraph found")
 
+            # st.write("**Result {}** `page {}` (Relevancy Score: {:.2f})'".format(i+1,hits.iloc[i]['page'],hits.iloc[i]['Relevancy'])")
+           # st.write('-------------------')
+            st.write("")
+            st.markdown("###### Top few Policy/Plans Classified paragraph/text results from list of {} classified paragraphs ######".format(count_policy))
+            st.markdown("""<hr style="height:10px;border:none;color:#097969;background-color:#097969;" /> """, unsafe_allow_html=True)
+            range_val = min(5,len(hits))
+            for i in range(range_val):
+                # the page number reflects the page that contains the main paragraph 
+                # according to split limit, the overlapping part can be on a separate page
+                st.write('**Result {}** : `page {}`, `Sector: {}`,\
+                            `Indicators: {}`, `Adapt-Mitig :{}`'\
+                    .format(i+1,
+                            hits.iloc[i]['page'], hits.iloc[i]['Sector Label'],
+                            hits.iloc[i]['Indicator Label'],hits.iloc[i]['Adapt-Mitig Label']))                        
+                st.write("\t Text: \t{}".format(hits.iloc[i]['text'].replace("\n", " ")))
+            hits = hits.reset_index(drop =True)
+            st.write('----------------')
+            st.write('Explore the data')
+            st.write(hits)
+            df.drop(columns = ['Policy_check'],inplace=True)
+            df_xlsx = to_excel(df)
+            
+            with st.sidebar:
+                st.write('-------------')
+                st.download_button(label='📥 Download Result',
+                            data=df_xlsx ,
+                            file_name= 'cpu_analysis.xlsx')
 
-# Download model from HF Hub
-vg_model = SetFitModel.from_pretrained("leavoigt/vulnerable_groups")
+        else:
+            st.info("🤔 No Policy/Plans found")