Update appStore/target.py

appStore/target.py

@@ -8,168 +8,41 @@ 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 st_aggrid import AgGrid
+from utils.tapp_classifier import load_tappClassifier, tapp_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)
 
 # Declare all the necessary variables
-classifier_identifier = 'target'
-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
+tapp_classifier_identifier = 'tapp'
+param1  = get_classifier_params(tapp_classifier_identifier)
 
 def app():
-
-    #### APP INFO #####
-    #     st.write(
-    #         """     
-    #         The **Target Extraction** app is an easy-to-use interface built \
-    #             in Streamlit for analyzing policy documents for \
-    #              Classification of the paragraphs/texts in the document *If it \
-    #             contains any Economy-Wide Targets related information* - \
-    #             developed by GIZ Data Service Center, GFA, IKI Tracs, \
-    #              SV Klima and SPA. \n
-    #         """)
-
-
     ### Main app code ###
     with st.container():
         if 'key0' in st.session_state:
             df = st.session_state.key0
 
-            #load Classifier
-            classifier = load_targetClassifier(classifier_name=params['model_name'])
-            st.session_state['{}_classifier'.format(classifier_identifier)] = classifier
+            #load Classifiers
+            classifier = load_tappClassifier(classifier_name=param1['model_name'])
+            st.session_state['{}_classifier'.format(tapp_classifier_identifier)] = classifier
+            
             if len(df) > 100:
                 warning_msg = ": This might take sometime, please sit back and relax."
             else:
                 warning_msg = ""
                 
-            df  = target_classification(haystack_doc=df,
-                                    threshold= params['threshold'])
-            st.session_state.key1 = df
-
-          # # excel part
-            # temp = df[df['Relevancy']>threshold]
-            
-            # df['Validation'] =  'No'
-            # df_xlsx = to_excel(df)
-            # st.download_button(label='📥 Download Current Result',
-            #                 data=df_xlsx ,
-            #                 file_name= 'file_target.xlsx')
-              
-def target_display():
-    if  'key1' in st.session_state:
-        df = st.session_state.key1
-                
-        
-        hits  = df[df['Target Label'] == 'TARGET']
-        # hits['GHG Label'] = hits['GHG Label'].apply(lambda i: _lab_dict[i])
-        range_val = min(5,len(hits))
-        if range_val !=0:
-            count_target = sum(hits['Target Label'] == 'TARGET')
-            count_netzero = sum(hits['Netzero Label'] == 'NET-ZERO')
-            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'] == 'NET-ZERO']
-            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.markdown("###### Top few Target Classified paragraph/text results ######")
-            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 {}** (Relevancy Score: {:.2f}): `page {}`, `Sector: {}`,\
-                            `GHG: {}`, `Adapt-Mitig :{}`'\
-                    .format(i+1,hits.iloc[i]['Relevancy'],
-                            hits.iloc[i]['page'], hits.iloc[i]['Sector Label'],
-                            hits.iloc[i]['GHG 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_xlsx = to_excel(df)
-            
-            with st.sidebar:
-                st.write('-------------')
-                st.download_button(label='📥 Download Result',
-                            data=df_xlsx ,
-                            file_name= 'cpu_analysis.xlsx')
+            tapp_classification(haystack_doc=df,
+                                    threshold= param1['threshold'])
 
-        else:
-            st.info("🤔 No Targets found")
+            #st.session_state.key1 = df