# 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 keybert import KeyBERT from transformers import pipeline import matplotlib.pyplot as plt import numpy as np import streamlit as st import pandas as pd import docx from docx.shared import Inches from docx.shared import Pt from docx.enum.style import WD_STYLE_TYPE import tempfile import sqlite3 def app(): with st.container(): st.markdown("

SDSN x GIZ Policy Action Tracking v0.1

", unsafe_allow_html=True) st.write(' ') st.write(' ') with st.expander("ℹī¸ - About this app", expanded=True): st.write( """ The *Analyse Policy Document* app is an easy-to-use interface built in Streamlit for analyzing policy documents - developed by GIZ Data and the Sustainable Development Solution Network. \n 1. Keyword heatmap \n 2. SDG Classification for the paragraphs/texts in the document """ ) st.markdown("") st.markdown("") st.markdown("## 📌 Step One: Upload document ") 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) docs_processed, df, all_text, par_list = clean.preprocessingForSDG(docs) #haystackDoc, dataframeDoc, textData, paraList = clean.preprocessing(docs) else: # listing the options option = st.selectbox('Select the example document', ('Ethiopia: 10 Year Development Plan', 'South Africa:Low Emission strategy')) if option is 'South Africa:Low Emission strategy': file_name = file_path = 'sample/South Africa_s Low Emission Development Strategy.txt' 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' 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) docs_processed, df, all_text, par_list = clean.preprocessingForSDG(docs) if docs is not None: @st.cache(allow_output_mutation=True) def load_keyBert(): return KeyBERT() kw_model = load_keyBert() keywords = kw_model.extract_keywords( all_text, keyphrase_ngram_range=(1, 3), use_mmr=True, stop_words="english", top_n=10, diversity=0.7, ) st.markdown("## 🎈 What is my document about?") df = ( DataFrame(keywords, columns=["Keyword/Keyphrase", "Relevancy"]) .sort_values(by="Relevancy", ascending=False) .reset_index(drop=True) ) df1 = ( DataFrame(keywords, columns=["Keyword/Keyphrase", "Relevancy"]) .sort_values(by="Relevancy", ascending=False) .reset_index(drop=True) ) df.index += 1 # Add styling cmGreen = sns.light_palette("green", as_cmap=True) cmRed = sns.light_palette("red", as_cmap=True) df = df.style.background_gradient( cmap=cmGreen, subset=[ "Relevancy", ], ) c1, c2, c3 = st.columns([1, 3, 1]) format_dictionary = { "Relevancy": "{:.1%}", } df = df.format(format_dictionary) with c2: st.table(df) ######## SDG classiciation # @st.cache(allow_output_mutation=True) # def load_sdgClassifier(): # classifier = pipeline("text-classification", model= "../models/osdg_sdg/") # return classifier # load from disc (github repo) for performance boost @st.cache(allow_output_mutation=True) def load_sdgClassifier(): classifier = pipeline("text-classification", model= "jonas/sdg_classifier_osdg") return classifier classifier = load_sdgClassifier() # # not needed, par list comes from pre_processing function already # word_list = all_text.split() # len_word_list = len(word_list) # par_list = [] # par_len = 130 # for i in range(0,len_word_list // par_len): # string_part = ' '.join(word_list[i*par_len:(i+1)*par_len]) # par_list.append(string_part) labels = classifier(par_list) labels_= [(l['label'],l['score']) for l in labels] df2 = DataFrame(labels_, columns=["SDG", "Relevancy"]) df2['text'] = par_list df2 = df2.sort_values(by="Relevancy", ascending=False).reset_index(drop=True) df2.index += 1 df2 =df2[df2['Relevancy']>.85] x = df2['SDG'].value_counts() df3 = df2.copy() plt.rcParams['font.size'] = 25 colors = plt.get_cmap('Blues')(np.linspace(0.2, 0.7, len(x))) # plot fig, ax = plt.subplots() ax.pie(x, colors=colors, radius=2, center=(4, 4), wedgeprops={"linewidth": 1, "edgecolor": "white"}, frame=False,labels =list(x.index)) fig.savefig('temp.png', bbox_inches='tight',dpi= 100) st.markdown("## 🎈 Anything related to SDGs?") c4, c5, c6 = st.columns([2, 2, 2]) # Add styling cmGreen = sns.light_palette("green", as_cmap=True) cmRed = sns.light_palette("red", as_cmap=True) df2 = df2.style.background_gradient( cmap=cmGreen, subset=[ "Relevancy", ], ) format_dictionary = { "Relevancy": "{:.1%}", } df2 = df2.format(format_dictionary) with c5: st.pyplot(fig) c7, c8, c9 = st.columns([1, 10, 1]) with c8: st.table(df2) document = docx.Document() document.add_heading('Document name:{}'.format(file_name), 2) # Choosing the top most section of the page 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') #footer_para.text = "\tPowered by GIZ Data and the Sustainable Development Solution Network\ # hosted at Hugging-Face spaces: https://huggingface.co/spaces/ppsingh/streamlit_dev" #footer_para.font.size = docx.shared.Pt(6) document.add_heading('What is the document about', level=1) t = document.add_table(df1.shape[0]+1, df1.shape[1]) # add the header rows. for j in range(df1.shape[-1]): t.cell(0,j).text = df1.columns[j] # add the rest of the data frame for i in range(df1.shape[0]): for j in range(df1.shape[-1]): t.cell(i+1,j).text = str(df1.values[i,j]) document.add_heading('Anything Related to SDG', level=1) document.add_picture('temp.png', width=Inches(3), height=Inches(3)) t = document.add_table(df3.shape[0]+1, df3.shape[1]) widths = [Inches(0.4), Inches(0.4), Inches(4.5)] # add the header rows. for j in range(df3.shape[-1]): t.cell(0,j).text = df3.columns[j] t.cell(0,j).width = widths[j] # add the rest of the data frame for i in range(df3.shape[0]): for j in range(df3.shape[-1]): t.cell(i+1,j).width = widths[j] t.cell(i+1,j).text = str(df3.values[i,j]) document.save('demo.docx') #with open('summary.txt', 'w') as f: # f.write(df1.to_string()) # f.write(fig) #f.write(df2) # f.write(df3.to_string()) with open("demo.docx", "rb") as file: btn = st.download_button( label="Download file", data=file, file_name="demo.docx", mime="txt/docx" ) #with document st.download_button( # label="Download data as docx", # data=document, #file_name='test.docx', #mime='text/docx', # )