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| from os import write | |
| import time | |
| import pandas as pd | |
| import base64 | |
| from typing import Sequence | |
| import streamlit as st | |
| from sklearn.metrics import classification_report | |
| # from models import create_nest_sentences, load_summary_model, summarizer_gen, load_model, classifier_zero | |
| import models as md | |
| from utils import plot_result, plot_dual_bar_chart, examples_load, example_long_text_load | |
| import json | |
| ex_text, ex_license, ex_labels, ex_glabels = examples_load() | |
| ex_long_text = example_long_text_load() | |
| # if __name__ == '__main__': | |
| st.markdown("### Long Text Summarization & Multi-Label Classification") | |
| st.write("This app summarizes and then classifies your long text(s) with multiple labels using [BART Large MNLI](https://huggingface.co/facebook/bart-large-mnli). The keywords are generated using [KeyBERT](https://github.com/MaartenGr/KeyBERT).") | |
| st.write("__Inputs__: User enters their own custom text(s) and labels.") | |
| st.write("__Outputs__: A summary of the text, likelihood percentages for each label and a downloadable csv of the results. \ | |
| Includes additional options to generate a list of keywords and/or evaluate results against a list of ground truth labels, if available.") | |
| example_button = st.button(label='See Example') | |
| if example_button: | |
| example_text = ex_long_text #ex_text | |
| display_text = 'Excerpt from Frankenstein:' + example_text + '"\n\n' + "[This is an excerpt from Project Gutenberg's Frankenstein. " + ex_license + "]" | |
| input_labels = ex_labels | |
| input_glabels = ex_glabels | |
| else: | |
| display_text = '' | |
| input_labels = '' | |
| input_glabels = '' | |
| with st.form(key='my_form'): | |
| st.markdown("##### Step 1: Upload Text") | |
| text_input = st.text_area("Input any text you want to summarize & classify here (keep in mind very long text will take a while to process):", display_text) | |
| text_csv_expander = st.expander(label=f'Want to upload multiple texts at once? Expand to upload your text files below.', expanded=False) | |
| with text_csv_expander: | |
| st.markdown('##### Choose one of the options below:') | |
| st.write("__Option A:__") | |
| uploaded_text_files = st.file_uploader(label="Upload file(s) that end with the .txt suffix", | |
| accept_multiple_files=True, key = 'text_uploader', | |
| type = 'txt') | |
| st.write("__Option B:__") | |
| uploaded_csv_text_files = st.file_uploader(label='Upload a CSV file with columns: "title" and "text"', | |
| accept_multiple_files=False, key = 'csv_text_uploader', | |
| type = 'csv') | |
| if text_input == display_text and display_text != '': | |
| text_input = example_text | |
| st.text("\n\n\n") | |
| st.markdown("##### Step 2: Enter Labels") | |
| labels = st.text_input('Enter possible topic labels, which can be either keywords and/or general themes (comma-separated):',input_labels, max_chars=2000) | |
| labels = list(set([x.strip() for x in labels.strip().split(',') if len(x.strip()) > 0])) | |
| labels_csv_expander = st.expander(label=f'Prefer to upload a list of labels instead? Click here to upload your CSV file.',expanded=False) | |
| with labels_csv_expander: | |
| uploaded_labels_file = st.file_uploader("Choose a CSV file with one column and no header, where each cell is a separate label", | |
| key='labels_uploader') | |
| gen_keywords = st.radio( | |
| "Generate keywords from text (independent from the above labels)?", | |
| ('Yes', 'No') | |
| ) | |
| st.text("\n\n\n") | |
| st.markdown("##### Step 3: Provide Ground Truth Labels (_Optional_)") | |
| glabels = st.text_input('If available, enter ground truth topic labels to evaluate results, otherwise leave blank (comma-separated):',input_glabels, max_chars=2000) | |
| glabels = list(set([x.strip() for x in glabels.strip().split(',') if len(x.strip()) > 0])) | |
| glabels_csv_expander = st.expander(label=f'Have a file with labels for the text? Click here to upload your CSV file.', expanded=False) | |
| with glabels_csv_expander: | |
| st.markdown('##### Choose one of the options below:') | |
| st.write("__Option A:__") | |
| uploaded_onetext_glabels_file = st.file_uploader("Choose a CSV file with one column and no header, where each cell is a separate label", | |
| key = 'onetext_glabels_uploader') | |
| st.write("__Option B:__") | |
| uploaded_multitext_glabels_file = st.file_uploader('Or Choose a CSV file with two columns "title" and "label", with the cells in the title column matching the name of the files uploaded in step #1.', | |
| key = 'multitext_glabels_uploader') | |
| threshold_value = st.slider( | |
| 'Select a threshold cutoff for matching percentage (used for ground truth label evaluation)', | |
| 0.0, 1.0, (0.5)) | |
| submit_button = st.form_submit_button(label='Submit') | |
| st.write("_For improvments/suggestions, please file an issue here: https://github.com/pleonova/multi-label-summary-text_") | |
| with st.spinner('Loading pretrained models...'): | |
| start = time.time() | |
| summarizer = md.load_summary_model() | |
| s_time = round(time.time() - start,4) | |
| start = time.time() | |
| classifier = md.load_model() | |
| c_time = round(time.time() - start,4) | |
| start = time.time() | |
| kw_model = md.load_keyword_model() | |
| k_time = round(time.time() - start,4) | |
| st.spinner(f'Time taken to load various models: {k_time}s for KeyBERT model & {s_time}s for BART summarizer mnli model & {c_time}s for BART classifier mnli model.') | |
| # st.success(None) | |
| if submit_button or example_button: | |
| if len(text_input) == 0 and uploaded_text_files is None and uploaded_csv_text_files is None: | |
| st.error("Enter some text to generate a summary") | |
| else: | |
| if len(text_input) != 0: | |
| text_df = pd.DataFrame.from_dict({'title': ['sample'], 'text': [text_input]}) | |
| # OPTION A: | |
| elif uploaded_text_files is not None: | |
| st.markdown("### Text Inputs") | |
| st.write('Files concatenated into a dataframe:') | |
| file_names = [] | |
| raw_texts = [] | |
| for uploaded_file in uploaded_text_files: | |
| text = str(uploaded_file.read(), "utf-8") | |
| raw_texts.append(text) | |
| title_file_name = uploaded_file.name.replace('.txt','') | |
| file_names.append(title_file_name) | |
| text_df = pd.DataFrame({'title': file_names, | |
| 'text': raw_texts}) | |
| st.dataframe(text_df.head()) | |
| st.download_button( | |
| label="Download data as CSV", | |
| data=text_df.to_csv().encode('utf-8'), | |
| file_name='title_text.csv', | |
| mime='title_text/csv', | |
| ) | |
| # OPTION B: [TO DO: DIRECT CSV UPLOAD INSTEAD] | |
| if len(text_input) != 0: | |
| text_df = pd.DataFrame.from_dict({'title': ['sample'], 'text': [text_input]}) | |
| with st.spinner('Breaking up text into more reasonable chunks (transformers cannot exceed a 1024 token max)...'): | |
| # For each body of text, create text chunks of a certain token size required for the transformer | |
| text_chunks_lib = dict() | |
| for i in range(0, len(text_df)): | |
| nested_sentences = md.create_nest_sentences(document=text_df['text'][i], token_max_length=1024) | |
| # For each chunk of sentences (within the token max) | |
| text_chunks = [] | |
| for n in range(0, len(nested_sentences)): | |
| tc = " ".join(map(str, nested_sentences[n])) | |
| text_chunks.append(tc) | |
| title_entry = text_df['title'][i] | |
| text_chunks_lib[title_entry] = text_chunks | |
| if gen_keywords == 'Yes': | |
| st.markdown("### Top Keywords") | |
| with st.spinner("Generating keywords from text..."): | |
| kw_dict = dict() | |
| text_chunk_counter = 0 | |
| for key in text_chunks_lib: | |
| keywords_list = [] | |
| for text_chunk in text_chunks_lib[key]: | |
| text_chunk_counter += 1 | |
| keywords_list += md.keyword_gen(kw_model, text_chunk) | |
| kw_dict[key] = dict(keywords_list) | |
| # Display as a dataframe | |
| kw_df0 = pd.DataFrame.from_dict(kw_dict).reset_index() | |
| kw_df0.rename(columns={'index': 'keyword'}, inplace=True) | |
| kw_df = pd.melt(kw_df0, id_vars=['keyword'], var_name='title', value_name='score').dropna() | |
| if len(text_input) != 0: | |
| title_element = [] | |
| else: | |
| title_element = ['title'] | |
| kw_column_list = ['keyword', 'score'] | |
| kw_df = kw_df[kw_df['score'] > 0.25][title_element + kw_column_list].sort_values(title_element + ['score'], ascending=False).reset_index().drop(columns='index') | |
| st.dataframe(kw_df) | |
| st.download_button( | |
| label="Download data as CSV", | |
| data=kw_df.to_csv().encode('utf-8'), | |
| file_name='title_keywords.csv', | |
| mime='title_keywords/csv', | |
| ) | |
| st.markdown("### Summary") | |
| with st.spinner(f'Generating summaries for {text_chunk_counter} text chunks (this may take a minute)...'): | |
| my_summary_expander = st.expander(label=f'Expand to see intermediate summary generation details for {len(text_chunks)} text chunks') | |
| with my_summary_expander: | |
| summary = [] | |
| st.markdown("_Once the original text is broken into smaller chunks (totaling no more than 1024 tokens, \ | |
| with complete sentences), each block of text is then summarized separately using BART NLI \ | |
| and then combined at the very end to generate the final summary._") | |
| for num_chunk, text_chunk in enumerate(text_chunks): | |
| st.markdown(f"###### Original Text Chunk {num_chunk+1}/{len(text_chunks)}" ) | |
| st.markdown(text_chunk) | |
| chunk_summary = md.summarizer_gen(summarizer, sequence=text_chunk, maximum_tokens = 300, minimum_tokens = 20) | |
| summary.append(chunk_summary) | |
| st.markdown(f"###### Partial Summary {num_chunk+1}/{len(text_chunks)}") | |
| st.markdown(chunk_summary) | |
| # Combine all the summaries into a list and compress into one document, again | |
| final_summary = " \n\n".join(list(summary)) | |
| st.markdown(final_summary) | |
| if len(text_input) == 0 or len(labels) == 0: | |
| st.error('Enter some text and at least one possible topic to see label predictions.') | |
| else: | |
| st.markdown("### Top Label Predictions on Summary vs Full Text") | |
| with st.spinner('Matching labels...'): | |
| topics, scores = md.classifier_zero(classifier, sequence=final_summary, labels=labels, multi_class=True) | |
| # st.markdown("### Top Label Predictions: Combined Summary") | |
| # plot_result(topics[::-1][:], scores[::-1][:]) | |
| # st.markdown("### Download Data") | |
| data = pd.DataFrame({'label': topics, 'scores_from_summary': scores}) | |
| # st.dataframe(data) | |
| # coded_data = base64.b64encode(data.to_csv(index = False). encode ()).decode() | |
| # st.markdown( | |
| # f'<a href="data:file/csv;base64, {coded_data}" download = "data.csv">Download Data</a>', | |
| # unsafe_allow_html = True | |
| # ) | |
| topics_ex_text, scores_ex_text = md.classifier_zero(classifier, sequence=text_input, labels=labels, multi_class=True) | |
| plot_dual_bar_chart(topics, scores, topics_ex_text, scores_ex_text) | |
| data_ex_text = pd.DataFrame({'label': topics_ex_text, 'scores_from_full_text': scores_ex_text}) | |
| data2 = pd.merge(data, data_ex_text, on = ['label']) | |
| if len(glabels) > 0: | |
| gdata = pd.DataFrame({'label': glabels}) | |
| gdata['is_true_label'] = int(1) | |
| data2 = pd.merge(data2, gdata, how = 'left', on = ['label']) | |
| data2['is_true_label'].fillna(0, inplace = True) | |
| st.markdown("### Data Table") | |
| with st.spinner('Generating a table of results and a download link...'): | |
| st.dataframe(data2) | |
| def convert_df(df): | |
| # IMPORTANT: Cache the conversion to prevent computation on every rerun | |
| return df.to_csv().encode('utf-8') | |
| csv = convert_df(data2) | |
| st.download_button( | |
| label="Download data as CSV", | |
| data=csv, | |
| file_name='text_labels.csv', | |
| mime='text/csv', | |
| ) | |
| # coded_data = base64.b64encode(data2.to_csv(index = False). encode ()).decode() | |
| # st.markdown( | |
| # f'<a href="data:file/csv;base64, {coded_data}" download = "data.csv">Click here to download the data</a>', | |
| # unsafe_allow_html = True | |
| # ) | |
| if len(glabels) > 0: | |
| st.markdown("### Evaluation Metrics") | |
| with st.spinner('Evaluating output against ground truth...'): | |
| section_header_description = ['Summary Label Performance', 'Original Full Text Label Performance'] | |
| data_headers = ['scores_from_summary', 'scores_from_full_text'] | |
| for i in range(0,2): | |
| st.markdown(f"###### {section_header_description[i]}") | |
| report = classification_report(y_true = data2[['is_true_label']], | |
| y_pred = (data2[[data_headers[i]]] >= threshold_value) * 1.0, | |
| output_dict=True) | |
| df_report = pd.DataFrame(report).transpose() | |
| st.markdown(f"Threshold set for: {threshold_value}") | |
| st.dataframe(df_report) | |
| st.success('All done!') | |
| st.balloons() | |