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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
import docx
from docx.shared import Inches
from docx.shared import Pt
from docx.enum.style import WD_STYLE_TYPE
from utils.sdg_classifier import sdg_classification
from utils.sdg_classifier import runSDGPreprocessingPipeline
from utils.keyword_extraction import keywordExtraction, textrank
import logging
logger = logging.getLogger(__name__)
def app():
with st.container():
st.markdown("<h1 style='text-align: center; color: black;'> SDSN x GIZ Policy Action Tracking v0.1</h1>", unsafe_allow_html=True)
st.write(' ')
st.write(' ')
with st.expander("ℹ️ - About this app", expanded=False):
st.write(
"""
The *Analyse Policy Document* app is an easy-to-use interface built \
in Streamlit for analyzing policy documents with respect to SDG \
Classification for the paragraphs/texts in the document - \
developed by GIZ Data and the Sustainable Development Solution Network. \n
""")
st.markdown("")
with st.container():
if st.button("RUN SDG Analysis"):
if 'filepath' in st.session_state:
file_name = st.session_state['filename']
file_path = st.session_state['filepath']
allDocuments = runSDGPreprocessingPipeline(file_path,file_name)
if len(allDocuments['documents']) > 100:
warning_msg = ": This might take sometime, please sit back and relax."
else:
warning_msg = ""
with st.spinner("Running SDG Classification{}".format(warning_msg)):
df, x = sdg_classification(allDocuments['documents'])
sdg_labels = df.SDG.unique()
keywordList = []
for label in sdg_labels:
sdgdata = " ".join(df[df.SDG == label].text.to_list())
tfidflist_ = keywordExtraction(label,[sdgdata])
textranklist_ = textrank(sdgdata, words = 20)
keywordList.append({'SDG':label, 'TFIDF Keywords':tfidflist_, 'TEXT RANK':textranklist_})
keywordsDf = pd.DataFrame(keywordList)
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])
with c5:
st.pyplot(fig)
st.markdown("##### What keywords are present under SDG classified text? #####")
st.write("TFIDF BASED")
c1, c2, c3 = st.columns([1, 10, 1])
with c2:
st.table(keywordsDf)
c7, c8, c9 = st.columns([1, 10, 1])
with c8:
st.table(df)
else:
st.info("🤔 No document found, please try to upload it at the sidebar!")
logging.warning("Terminated as no document provided")
# 1. Keyword heatmap \n
# 2. SDG Classification for the paragraphs/texts in the document
#
# with st.container():
# if 'docs' in st.session_state:
# docs = st.session_state['docs']
# docs_processed, df, all_text, par_list = clean.preprocessingForSDG(docs)
# # paraList = st.session_state['paraList']
# logging.info("keybert")
# with st.spinner("Running Key bert"):
# 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) |