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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") | |
| st.set_option('deprecation.showPyplotGlobalUse', False) | |
| def main(): | |
| menu = ["Home","Storage","About"] | |
| create_table() | |
| choice = st.sidebar.selectbox("Menu",menu) | |
| if choice == "Home": | |
| st.title("Demo") | |
| st.sidebar.subheader("Tuning/Settings") | |
| # max_length= st.sidebar.slider("Maximum length of the generated text ",30,100) | |
| # top_k= st.sidebar.slider(" limits the sampled tokens to the top k values ",1,100) | |
| # temperature= st.sidebar.slider("Controls the craziness of the text ",0.7,100.0) | |
| model_type = st.sidebar.selectbox("Model type", options=["Bart","T5"]) | |
| upload_doc = st.file_uploader("Upload a .txt, .pdf, .docx file for summarization") | |
| st.markdown("<h3 style='text-align: center; color: red;'>OR</h3>",unsafe_allow_html=True,) | |
| plain_text = st.text_area("Type your Message...",height=200) | |
| 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=100, min_length=30) | |
| summarized_text = ' '.join([summ['summary_text'] for summ in summarized_text]) | |
| st.success("Data Submitted for model retraining") | |
| postdate = datetime.now() | |
| # Add Data To Database | |
| add_data(text_to_summarize,summarized_text,postdate) | |
| 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=100, min_length=30) | |
| summarized_text = ' '.join([summ['summary_text'] for summ in summarized_text]) | |
| st.success("Data Submitted for model retraining") | |
| postdate = datetime.now() | |
| # Add Data To Database | |
| add_data(text_to_summarize,summarized_text,postdate) | |
| # else: | |
| # text_to_summarize = clean_text | |
| # with st.spinner( | |
| # text="Loading Pegasus Model and Extracting summary. This might take a few seconds depending on the length of your text..."): | |
| # summarizer_model = pegasus() | |
| # summarized_text = summarizer_model(text_to_summarize, max_length=100, min_length=30) | |
| # # summarized_text = ' '.join([summ['summary_text'] for summ in summarized_text]) | |
| # st.success("Data Submitted for model retraining") | |
| # postdate = datetime.now() | |
| # # Add Data To Database | |
| # # add_data(text_to_summarize,summarized_text,postdate) | |
| 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) | |
| elif choice == "Storage": | |
| st.title("Manage & Monitor Results") | |
| # stored_data = view_all_data() | |
| # new_df = pd.DataFrame(stored_data,columns=["text_to_summarize","summarized_text","postdate"]) | |
| # st.dataframe(new_df) | |
| # new_df['postdate'] = pd.to_datetime(new_df['postdate']) | |
| else: | |
| st.subheader("About") | |
| # html_temp ="""<div> | |
| # <p></p> | |
| # <p></p> | |
| # </div>""" | |
| # st.markdown(html_temp, unsafe_allow_html=True) | |
| if __name__ == '__main__': | |
| main() |