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| # Core Pkgs | |
| import streamlit as st | |
| from function import * | |
| # EDA Pkgs | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| from wordcloud import WordCloud | |
| # Utils | |
| from datetime import datetime | |
| warnings.filterwarnings("ignore") | |
| # page info setup | |
| menu_items = { | |
| 'Get help':'https://www.linkedin.com/in/oluwaseyi-gbadamosi-41015216b/' , | |
| 'Report a bug': 'https://www.linkedin.com/in/oluwaseyi-gbadamosi-41015216b/', | |
| 'About': ''' | |
| ## My Custom App | |
| Some markdown to show in the About dialog. | |
| ''' | |
| } | |
| #page configuration | |
| st.set_page_config(page_title="Article Summerizer", page_icon="./favicon/favicon.ico",menu_items=menu_items) | |
| st.set_option('deprecation.showPyplotGlobalUse', False) | |
| def main(): | |
| # This is used to hide the made with streamlit watermark | |
| hide_streamlit_style = """ | |
| <style> | |
| footer {visibility: hidden;} | |
| </style> | |
| """ | |
| st.markdown(hide_streamlit_style, unsafe_allow_html=True) | |
| # Article Summerizer heading | |
| st.markdown("<h1 style = 'color:gold; align:center; font-size: 40px;'> Article Summerizer</h1>", unsafe_allow_html=True) | |
| # control for Model Settings | |
| st.sidebar.markdown("<h4 style = 'color:gold; align:center; font-size: 20px;'> Model Settings</h1>", unsafe_allow_html=True) | |
| max_length= st.sidebar.slider("Maximum length of the generated text is 500 tokens",min_value=100,max_value=500) | |
| min_length= st.sidebar.slider("Minimum length of the generated text",min_value=30) | |
| model_type = st.sidebar.selectbox("Model type", options=["Bart","T5"]) | |
| # This function is used to upload a .txt, .pdf, .docx file for summarization | |
| upload_doc = st.file_uploader("Upload a .txt, .pdf, .docx file for summarization") | |
| st.markdown("<h3 style='text-align: center; color: gold;'>OR</h3>",unsafe_allow_html=True) | |
| #This function is used to Type your Message... (text area) | |
| plain_text = st.text_area("Type your Message...",height=200) | |
| # this is used to control the logic of the code | |
| if upload_doc: | |
| clean_text = preprocess_plain_text(extract_text_from_file(upload_doc)) | |
| else: | |
| clean_text = preprocess_plain_text(plain_text) | |
| summarize = st.button("Summarize...") | |
| # called on toggle button [summarize] | |
| if summarize: | |
| if model_type == "Bart": | |
| text_to_summarize = clean_text | |
| with st.spinner( | |
| text="Loading Bart Model and Extracting summary. This might take a few seconds depending on the length of your text..."): | |
| summarizer_model = bart() | |
| summarized_text = summarizer_model(text_to_summarize, max_length=max_length ,min_length=min_length) | |
| summarized_text = ' '.join([summ['summary_text'] for summ in summarized_text]) | |
| elif model_type == "T5": | |
| text_to_summarize = clean_text | |
| with st.spinner( | |
| text="Loading T5 Model and Extracting summary. This might take a few seconds depending on the length of your text..."): | |
| summarizer_model = t5() | |
| summarized_text = summarizer_model(text_to_summarize, max_length=max_length, min_length=min_length) | |
| summarized_text = ' '.join([summ['summary_text'] for summ in summarized_text]) | |
| res_col1 ,res_col2 = st.columns(2) | |
| with res_col1: | |
| st.subheader("Generated Text Visualization") | |
| # Create and generate a word cloud image: | |
| wordcloud = WordCloud().generate(summarized_text) | |
| # Display the generated image: | |
| plt.imshow(wordcloud, interpolation='bilinear') | |
| plt.axis("off") | |
| plt.show() | |
| st.pyplot() | |
| summary_downloader(summarized_text) | |
| with res_col2: | |
| st.subheader("Summarized Text Output") | |
| st.success("Summarized Text") | |
| st.write(summarized_text) | |
| if __name__ == '__main__': | |
| main() |