pages/10 WordCloud.py

import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
from wordcloud import WordCloud
from tools import sourceformat as sf

# ===config===
st.set_page_config(
    page_title="Coconut",
    page_icon="🥥",
    layout="wide",
    initial_sidebar_state="collapsed"
)

hide_streamlit_style = """
            <style>
            #MainMenu 
            {visibility: hidden;}
            footer {visibility: hidden;}
            [data-testid="collapsedControl"] {display: none}
            </style>
            """
st.markdown(hide_streamlit_style, unsafe_allow_html=True)

with st.popover("🔗 Menu"):
    st.page_link("https://www.coconut-libtool.com/", label="Home", icon="🏠")
    st.page_link("pages/1 Scattertext.py", label="Scattertext", icon="1️⃣")
    st.page_link("pages/2 Topic Modeling.py", label="Topic Modeling", icon="2️⃣")
    st.page_link("pages/3 Bidirected Network.py", label="Bidirected Network", icon="3️⃣")
    st.page_link("pages/4 Sunburst.py", label="Sunburst", icon="4️⃣")
    st.page_link("pages/5 Burst Detection.py", label="Burst Detection", icon="5️⃣")
    st.page_link("pages/6 Keywords Stem.py", label="Keywords Stem", icon="6️⃣")
    st.page_link("pages/7 Sentiment Analysis.py", label="Sentiment Analysis", icon="7️⃣")
    st.page_link("pages/8 Shifterator.py", label="Shifterator", icon="8️⃣")
    st.page_link("pages/9 Summarization.py", label = "Summarization",icon ="9️⃣")
    st.page_link("pages/10 WordCloud.py", label = "WordCloud", icon = "🔟")
    
st.header("Wordcloud", anchor=False)
st.subheader('Put your file here...', anchor=False)

#========unique id========
@st.cache_resource(ttl=3600)
def create_list():
    l = [1, 2, 3]
    return l

l = create_list()
first_list_value = l[0]
l[0] = first_list_value + 1
uID = str(l[0])

@st.cache_data(ttl=3600)
def get_ext(uploaded_file):
    extype = uID+uploaded_file.name
    return extype

#===clear cache===
def reset_all():
    st.cache_data.clear()

#===text reading===
@st.cache_data(ttl=3600)
def read_txt(intext):
    return (intext.read()).decode()

@st.cache_data(ttl=3600)
def conv_txt(extype):
    if("PMID" in (uploaded_file.read()).decode()):
        uploaded_file.seek(0)
        papers = sf.medline(uploaded_file)
        print(papers)
        return papers
    col_dict = {'TI': 'Title',
            'SO': 'Source title',
            'DE': 'Author Keywords',
            'DT': 'Document Type',
            'AB': 'Abstract',
            'TC': 'Cited by',
            'PY': 'Year',
            'ID': 'Keywords Plus',
            'rights_date_used': 'Year'}
    uploaded_file.seek(0)
    papers = pd.read_csv(uploaded_file, sep='\t')
    
    #if text just has one column (or is not csv) return nothing
    if(len(papers.columns)==1):
        return

    if("htid" in papers.columns):
        papers = sf.htrc(papers)
    papers.rename(columns=col_dict, inplace=True)
    print(papers)
    return papers

#===csv reading===
@st.cache_data(ttl=3600)
def upload(file):
    papers = pd.read_csv(uploaded_file)
    if "About the data" in papers.columns[0]:
        papers = sf.dim(papers)
        col_dict = {'MeSH terms': 'Keywords',
        'PubYear': 'Year',
        'Times cited': 'Cited by',
        'Publication Type': 'Document Type'
        }
        papers.rename(columns=col_dict, inplace=True)
    
    return papers

#===Read data===
uploaded_file = st.file_uploader('', type=['txt','csv'], on_change=reset_all)
    
if uploaded_file is not None:
    
    tab1, tab2, tab3 = st.tabs(["📈 Generate visualization", "📃 Reference", "⬇️ Download Help"])
    
    with tab1:
        c1, c2 = st.columns(2)

        
        with c1:
            max_font = st.number_input("Max Font Size", min_value = 1, value = 100)
            max_words = st.number_input("Max Word Count", min_value = 1, value = 250)
            background = st.selectbox("Background color", ["white","black"])

        
        with c2:
            words_to_remove = st.text_input("Remove specific words. Separate words by semicolons (;)")
            stopwords = words_to_remove.split(';')
            image_width = st.number_input("Image width", value = 400)
            image_height = st.number_input("Image height", value = 200)
            scale = st.number_input("Scale", value = 1)
        
        try:
            extype = get_ext(uploaded_file)

            if extype.endswith(".txt"):
                
                try:
                    texts = conv_txt(uploaded_file)
                    colcho = c1.selectbox("Choose Column", list(texts))
                    fulltext = " ".join(list(texts[colcho]))

                except:
                    fulltext = read_txt(uploaded_file)
                
                if st.button("Submit"):
                    
                    
                    wordcloud = WordCloud(max_font_size = max_font,
                    max_words = max_words,
                    background_color=background,
                    stopwords = stopwords,
                    height = image_height,
                    width = image_width,
                    scale = scale).generate(fulltext)
                    img = wordcloud.to_image()

                    with st.container(border=True):
                        st.image(img, use_container_width=True)

            elif extype.endswith(".csv"):
                texts = upload(uploaded_file)

                colcho = c1.selectbox("Choose Column", list(texts))

                fullcolumn = " ".join(list(texts[colcho]))

                if st.button("Submit"):

                    wordcloud = WordCloud(max_font_size = max_font,
                    max_words = max_words,
                    background_color=background,
                    stopwords = stopwords,
                    height = image_height,
                    width = image_width,
                    scale = scale).generate(fullcolumn)
                    img = wordcloud.to_image()

                    with st.container(border=True):
                        st.image(img, use_container_width=True)

        except Exception as e:
            st.write(e)