Delete appStore/policyaction.py

appStore/policyaction.py

@@ -1,238 +0,0 @@
-# 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.policyaction_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 = 'policyaction'
-params  = get_classifier_params(classifier_identifier)
-
-@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')
-
-        else:
-            st.info("🤔 No Policy/Plans found")