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import streamlit as st
st.set_page_config(f'SDSN x GIZ Policy Tracing', layout="wide")
import seaborn as sns
import pdfplumber
from pandas import DataFrame
from keybert import KeyBERT
import matplotlib.pyplot as plt
import numpy as np
import streamlit as st
def app():
with st.container():
st.markdown("<h1 style='text-align: center; color: black;'> Policy Action Tracking</h1>", unsafe_allow_html=True)
st.write(' ')
st.write(' ')
with st.expander("ℹ️ - About this app", expanded=True):
st.write(
"""
The *Policy Action Tracker* app is an easy-to-use interface built in Streamlit for analyzing policy documents - developed by GIZ Data and the Sustainable Development Solution Network.
It uses a minimal keyword extraction technique that leverages multiple NLP embeddings and relies on [Transformers] (https://huggingface.co/transformers/) 🤗 to create keywords/keyphrases that are most similar to a document.
"""
)
st.markdown("")
st.markdown("")
st.markdown("## 📌 Step One: Upload document ")
with st.container():
file = st.file_uploader('Upload PDF File', type=['pdf'])
if file is not None:
text = []
with pdfplumber.open(file) as pdf:
for page in pdf.pages:
text.append(page.extract_text())
text_str = ' '.join([page for page in text])
st.write('Number of pages:',len(pdf.pages))
@st.cache(allow_output_mutation=True)
def load_model():
return KeyBERT()
kw_model = load_model()
keywords = kw_model.extract_keywords(
text_str,
keyphrase_ngram_range=(1, 2),
use_mmr=True,
stop_words="english",
top_n=15,
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)
)
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!
from transformers import pipeline
finetuned_checkpoint = "peter2000/roberta-base-finetuned-osdg"
classifier = pipeline("text-classification", model=finetuned_checkpoint)
word_list = text_str.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]
df = DataFrame(labels_, columns=["SDG", "Relevancy"])
df['text'] = par_list
df = df.sort_values(by="Relevancy", ascending=False).reset_index(drop=True)
df.index += 1
#df =df[df['Relevancy']>.95]
x = df['SDG'].value_counts()
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))
st.markdown("## 🎈 Anything related to SDGs?")
c4, c5, c6 = st.columns([5, 7, 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",
],
)
format_dictionary = {
"Relevancy": "{:.1%}",
}
df = df.format(format_dictionary)
with c4:
st.pyplot(fig)
with c5:
st.table(df)
app.run() |