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
import matplotlib.pyplot as plt
import numpy as np
import streamlit as st
import sentence-transformers



   ##@st.cache(allow_output_mutation=True)
def load_model():
    return KeyBERT()
    
def read_(file):
     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('Document:', pdf.metadata)
            st.write('Number of pages:',len(pdf.pages))
            pdf.close()
            return text_str

            
            
st.sidebar.image(
    "https://github.com/gizdatalab/policy_tracing/blob/main/img/sdsn.png?raw=true",
    use_column_width=True
)
st.sidebar.markdown("##  📌 Step One: Upload document ")

with st.sidebar:
    file = st.file_uploader('Upload PDF File', type=['pdf'])

st.sidebar.title(
    "Options:"
)

st.sidebar.markdown(
    "You can freely browse the different chapters - ie example prompts from different people - and see the results."
)

selected_date = st.sidebar.selectbox(
    "Please select the chapter you want to read:",
    ['c1','c2']
)

with st.container():
    st.markdown("<h1 style='text-align: center; color: black;'> SDSN X GIZ - 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 with 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():
    st.markdown("##  📌 Step One: Upload document ")
    ##file = st.file_uploader('Upload PDF File', type=['pdf'])
    text_str = read_(file)
    
    
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=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)
    )

    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 = "jonas/sdg_classifier_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+' ...' for par in par_list]      
    df = df.sort_values(by="Relevancy", ascending=False).reset_index(drop=True)  
    df.index += 1
    df =df[df['Relevancy']>.9]
    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)