# 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.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 # 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 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 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'] st.table(hits) # # 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'] == '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.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= os.path.splitext(st.session_state['filename'])[0]+'.xlsx') # else: # st.info("🤔 No Targets found")