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| # 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.ghg_classifier import load_ghgClassifier, ghg_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 = 'ghg' | |
| params = get_classifier_params(classifier_identifier) | |
| # Labels dictionary ### | |
| _lab_dict = { | |
| 'NEGATIVE':'NO GHG TARGET', | |
| 'NA':'NOT APPLICABLE', | |
| 'TARGET':'GHG TARGET', | |
| } | |
| 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']}) | |
| 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 | |
| # Load the classifier model | |
| classifier = load_ghgClassifier(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 = ghg_classification(haystack_doc=df, | |
| threshold= params['threshold']) | |
| st.session_state.key1 = df | |
| def netzero_display(): | |
| if 'key1' in st.session_state: | |
| df = st.session_state.key2 | |
| hits = df[df['GHG Label'] == 'TARGET'] | |
| range_val = min(5,len(hits)) | |
| if range_val !=0: | |
| count_df = df['GHG Label'].value_counts() | |
| count_df = count_df.rename('count') | |
| count_df = count_df.rename_axis('GHG Label').reset_index() | |
| count_df['Label_def'] = count_df['GHG 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.plotly_chart(fig,use_container_width= True) | |
| hits = hits.sort_values(by=['GHG Score'], ascending=False) | |
| st.write("") | |
| st.markdown("###### Top few GHG 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 {}** `page {}` (Relevancy Score: {:.2f})'.format(i+1,hits.iloc[i]['page'],hits.iloc[i]['GHG Score'])) | |
| st.write("\t Text: \t{}".format(hits.iloc[i]['text'])) | |
| else: | |
| st.info("🤔 No GHG target found") | |