app.py

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



   ##@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,
)

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 ")