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# set path
import glob, os, sys; sys.path.append('../udfPreprocess')
#import helper
import udfPreprocess.docPreprocessing as pre
import udfPreprocess.cleaning as clean
#import needed libraries
import seaborn as sns
from pandas import DataFrame
from sentence_transformers import SentenceTransformer, CrossEncoder, util
from sklearn.metrics.pairwise import cosine_similarity
# from keybert import KeyBERT
from transformers import pipeline
import matplotlib.pyplot as plt
import numpy as np
import streamlit as st
import pandas as pd
from rank_bm25 import BM25Okapi
from sklearn.feature_extraction import _stop_words
import string
from tqdm.autonotebook import tqdm
import numpy as np
import urllib.request
import ast
import tempfile
import sqlite3
import json
import urllib.request
import ast
import docx
from docx.shared import Inches
from docx.shared import Pt
from docx.enum.style import WD_STYLE_TYPE
def app():
# Sidebar
st.sidebar.title('Check Coherence')
st.sidebar.write(' ')
with open('ndcs/countryList.txt') as dfile:
countryList = dfile.read()
countryList = ast.literal_eval(countryList)
countrynames = list(countryList.keys())
option = st.sidebar.selectbox('Select Country', (countrynames))
countryCode = countryList[option]
with st.container():
st.markdown("<h1 style='text-align: center; color: black;'> Check Coherence of Policy Document with NDCs</h1>", unsafe_allow_html=True)
st.write(' ')
st.write(' ')
with st.expander("ℹ️ - About this app", expanded=True):
st.write(
"""
The *Check Coherence* app is an easy-to-use interface built in Streamlit for doing analysis of policy document and finding the coherence between NDCs/New-Updated NDCs- developed by GIZ Data and the Sustainable Development Solution Network.
"""
)
st.markdown("")
st.markdown("")
st.markdown("## 📌 Step One: Upload document of the country selected ")
with st.container():
docs = None
# asking user for either upload or select existing doc
choice = st.radio(label = 'Select the Document',
help = 'You can upload the document \
or else you can try a example document.',
options = ('Upload Document', 'Try Example'),
horizontal = True)
if choice == 'Upload Document':
uploaded_file = st.file_uploader('Upload the File', type=['pdf', 'docx', 'txt'])
if uploaded_file is not None:
with tempfile.NamedTemporaryFile(mode="wb") as temp:
bytes_data = uploaded_file.getvalue()
temp.write(bytes_data)
st.write("Uploaded Filename: ", uploaded_file.name)
file_name = uploaded_file.name
file_path = temp.name
docs = pre.load_document(file_path, file_name)
haystackDoc, dataframeDoc, textData, paraList = clean.preprocessing(docs)
else:
# listing the options
option = st.selectbox('Select the example document',
('South Africa:Low Emission strategy',
'Ethiopia: 10 Year Development Plan'))
if option is 'South Africa:Low Emission strategy':
file_name = file_path = 'sample/South Africa_s Low Emission Development Strategy.txt'
countryCode = countryList['South Africa']
st.write("Selected document:", file_name.split('/')[1])
# with open('sample/South Africa_s Low Emission Development Strategy.txt') as dfile:
# file = open('sample/South Africa_s Low Emission Development Strategy.txt', 'wb')
else:
# with open('sample/Ethiopia_s_2021_10 Year Development Plan.txt') as dfile:
file_name = file_path = 'sample/Ethiopia_s_2021_10 Year Development Plan.txt'
countryCode = countryList['Ethiopia']
st.write("Selected document:", file_name.split('/')[1])
if option is not None:
docs = pre.load_document(file_path,file_name)
haystackDoc, dataframeDoc, textData, paraList = clean.preprocessing(docs)
with open('ndcs/cca.txt', encoding='utf-8', errors='ignore') as dfile:
cca_sent = dfile.read()
cca_sent = ast.literal_eval(cca_sent)
with open('ndcs/ccm.txt', encoding='utf-8', errors='ignore') as dfile:
ccm_sent = dfile.read()
ccm_sent = ast.literal_eval(ccm_sent)
with open('ndcs/countryList.txt') as dfile:
countryList = dfile.read()
countryList = ast.literal_eval(countryList)
def get_document(countryCode: str):
link = "https://klimalog.die-gdi.de/ndc/open-data/dataset.json"
with urllib.request.urlopen(link) as urlfile:
data = json.loads(urlfile.read())
categoriesData = {}
categoriesData['categories']= data['categories']
categoriesData['subcategories']= data['subcategories']
keys_sub = categoriesData['subcategories'].keys()
documentType= 'NDCs'
if documentType in data.keys():
if countryCode in data[documentType].keys():
get_dict = {}
for key, value in data[documentType][countryCode].items():
if key not in ['country_name','region_id', 'region_name']:
get_dict[key] = value['classification']
else:
get_dict[key] = value
else:
return None
else:
return None
country = {}
for key in categoriesData['categories']:
country[key]= {}
for key,value in categoriesData['subcategories'].items():
country[value['category']][key] = get_dict[key]
return country
# country_ndc = get_document('NDCs', countryList[option])
def countrySpecificCCA(cca_sent, threshold, countryCode):
temp = {}
doc = get_document(countryCode)
for key,value in cca_sent.items():
id_ = doc['climate change adaptation'][key]['id']
if id_ >threshold:
temp[key] = value['id'][id_]
return temp
def countrySpecificCCM(ccm_sent, threshold, countryCode):
temp = {}
doc = get_document(countryCode)
for key,value in ccm_sent.items():
id_ = doc['climate change mitigation'][key]['id']
if id_ >threshold:
temp[key] = value['id'][id_]
return temp
if docs is not None:
sent_cca = countrySpecificCCA(cca_sent,1,countryCode)
sent_ccm = countrySpecificCCM(ccm_sent,1,countryCode)
#st.write(sent_ccm)
@st.cache(allow_output_mutation=True)
def load_sentenceTransformer(name):
return SentenceTransformer(name)
model = load_sentenceTransformer('all-MiniLM-L6-v2')
document_embeddings = model.encode(paraList, show_progress_bar=True)
genre = st.radio( "Select Category",('Climate Change Adaptation', 'Climate Change Mitigation'))
if genre == 'Climate Change Adaptation':
sent_dict = sent_cca
sent_labels = []
for key,sent in sent_dict.items():
sent_labels.append(sent)
label_embeddings = model.encode(sent_labels, show_progress_bar=True)
similarity_high_threshold = 0.55
similarity_matrix = cosine_similarity(label_embeddings, document_embeddings)
label_indices, paragraph_indices = np.where(similarity_matrix>similarity_high_threshold)
positive_indices = list(zip(label_indices.tolist(), paragraph_indices.tolist()))
else:
sent_dict = sent_ccm
sent_labels = []
for key,sent in sent_dict.items():
sent_labels.append(sent)
label_embeddings = model.encode(sent_labels, show_progress_bar=True)
similarity_high_threshold = 0.55
similarity_matrix = cosine_similarity(label_embeddings, document_embeddings)
label_indices, paragraph_indices = np.where(similarity_matrix>similarity_high_threshold)
positive_indices = list(zip(label_indices.tolist(), paragraph_indices.tolist()))
# sent_labels = []
# for key,sent in sent_dict.items():
# sent_labels.append(sent)
# label_embeddings = model.encode(sent_labels, show_progress_bar=True)
#similarity_high_threshold = 0.55
# similarity_matrix = cosine_similarity(label_embeddings, document_embeddings)
#label_indices, paragraph_indices = np.where(similarity_matrix>similarity_high_threshold)
#positive_indices = list(zip(label_indices.tolist(), paragraph_indices.tolist()))
document = docx.Document()
document.add_heading('Document name:{}'.format(file_name), 2)
section = document.sections[0]
# Calling the footer
footer = section.footer
# Calling the paragraph already present in
# the footer section
footer_para = footer.paragraphs[0]
font_styles = document.styles
font_charstyle = font_styles.add_style('CommentsStyle', WD_STYLE_TYPE.CHARACTER)
font_object = font_charstyle.font
font_object.size = Pt(7)
# Adding the centered zoned footer
footer_para.add_run('''\tPowered by GIZ Data and the Sustainable Development Solution Network hosted at Hugging-Face spaces: https://huggingface.co/spaces/ppsingh/streamlit_dev''', style='CommentsStyle')
document.add_paragraph("Country Code for which NDC is carried out {}".format(countryCode))
for _label_idx, _paragraph_idx in positive_indices:
st.write("This paragraph: \n")
document.add_paragraph("This paragraph: \n")
st.write(paraList[_paragraph_idx])
st.write(f"Is relevant to: \n {list(sent_dict.keys())[_label_idx]}")
document.add_paragraph(f"Is relevant to: \n {list(sent_dict.keys())[_label_idx]}")
st.write('-'*10)
document.add_paragraph('-'*10)
document.save('demo.docx')
with open("demo.docx", "rb") as file:
btn = st.download_button(
label="Download file",
data=file,
file_name="demo.docx",
mime="txt/docx"
)
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