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| import streamlit as st | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| from wordcloud import WordCloud | |
| # ===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.page_link("pages/Rtest.py",label = "rtesting") | |
| st.header("Wordcloud", anchor=False) | |
| st.subheader('Put your file here...', anchor=False) | |
| #========unique id======== | |
| 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]) | |
| def get_ext(uploaded_file): | |
| extype = uID+uploaded_file.name | |
| return extype | |
| #===clear cache=== | |
| def reset_all(): | |
| st.cache_data.clear() | |
| #===text reading=== | |
| def read_txt(intext): | |
| return (intext.read()).decode() | |
| #===csv reading=== | |
| def read_csv(uploaded_file): | |
| fulltexts = pd.read_csv(uploaded_file) | |
| fulltexts.rename(columns={fulltexts.columns[0]: "texts"}, inplace = True) | |
| return fulltexts | |
| #===Read data=== | |
| uploaded_file = st.file_uploader('', type=['txt'], 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, on_change=reset_all) | |
| max_words = st.number_input("Max Word Count", min_value = 1, value = 250, on_change=reset_all) | |
| background = st.selectbox("Background color", ["white","black"], on_change=reset_all) | |
| 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, on_change=reset_all) | |
| image_height = st.number_input("Image height", value = 200, on_change=reset_all) | |
| scale = st.number_input("Scale", value = 1, on_change=reset_all) | |
| try: | |
| extype = get_ext(uploaded_file) | |
| if extype.endswith(".txt"): | |
| if st.button("Submit"): | |
| fulltext = read_txt(uploaded_file) | |
| 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 = read_csv(uploaded_file) | |
| except Exception as e: | |
| st.write(e) |