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| import pandas as pd | |
| import streamlit as st | |
| from annotated_text import annotated_text | |
| from streamlit_option_menu import option_menu | |
| from sentiment_analysis import SentimentAnalysis | |
| from keyword_extraction import KeywordExtractor | |
| from part_of_speech_tagging import POSTagging | |
| from emotion_detection import EmotionDetection | |
| from named_entity_recognition import NamedEntityRecognition | |
| hide_streamlit_style = """ | |
| <style> | |
| #MainMenu {visibility: hidden;} | |
| footer {visibility: hidden;} | |
| </style> | |
| """ | |
| st.markdown(hide_streamlit_style, unsafe_allow_html=True) | |
| def load_sentiment_model(): | |
| return SentimentAnalysis() | |
| def load_keyword_model(): | |
| return KeywordExtractor() | |
| def load_pos_model(): | |
| return POSTagging() | |
| def load_emotion_model(): | |
| return EmotionDetection() | |
| def load_ner_model(): | |
| return NamedEntityRecognition() | |
| sentiment_analyzer = load_sentiment_model() | |
| keyword_extractor = load_keyword_model() | |
| pos_tagger = load_pos_model() | |
| emotion_detector = load_emotion_model() | |
| ner = load_ner_model() | |
| example_text = "This is example text that contains both names of organizations like Hugging Face and cities like New York, all while portraying an upbeat attitude." | |
| with st.sidebar: | |
| page = option_menu(menu_title='Menu', | |
| menu_icon="robot", | |
| options=["Welcome!", | |
| "Sentiment Analysis", | |
| "Keyword Extraction", | |
| "Part of Speech Tagging", | |
| "Emotion Detection", | |
| "Named Entity Recognition"], | |
| icons=["house-door", | |
| "chat-dots", | |
| "key", | |
| "tag", | |
| "emoji-heart-eyes", | |
| "building"], | |
| default_index=0 | |
| ) | |
| st.title('NLP Tookit') | |
| if page == "Welcome!": | |
| st.header('Welcome!') | |
| st.markdown("") | |
| st.write( | |
| """ | |
| """ | |
| ) | |
| st.subheader("Quickstart") | |
| st.write( | |
| """ | |
| Replace the example text below and flip through the pages in the menu to perform NLP tasks on-demand! | |
| Feel free to use the example text for a test run. | |
| """ | |
| ) | |
| text = st.text_area("Paste text here", value=example_text) | |
| st.subheader("Introduction") | |
| st.write(""" | |
| This application is a part of our mini project, built to showcase the capabilities of modern Natural Language | |
| Processing using open-source tools. It reflects the collaborative effort of our team and highlights what’s possible | |
| thanks to the generous contributions of the developer community. | |
| We developed this tool using Streamlit, Hugging Face Transformers, Transformers-Interpret, NLTK, SpaCy, and several other | |
| powerful open-source Python libraries and models. | |
| Utilizing this tool you will be able to perform a multitude of Natural Language Processing Tasks on a range of | |
| different tasks. All you need to do is paste your input, select your task, and hit the start button! | |
| * This application currently supports: | |
| * Sentiment Analysis | |
| * Keyword Extraction | |
| * Part of Speech Tagging | |
| * Emotion Detection | |
| * Named Entity Recognition | |
| My Team | |
| 1) Diwansing Girase | |
| 2) Kiran Patil | |
| 3) Krishita Patil | |
| 4) Rohit Bedse | |
| If you would like to contribute yourself, feel free to fork the Github repository listed below and submit a merge request. | |
| """ | |
| ) | |
| st.subheader("Notes") | |
| st.write( | |
| """ | |
| * This dashboard was constructed by myself, but every resource used is open-source! If you are interested in my other works you can view them here: | |
| [Project Github](https://github.com/GiraseDeva01/NLP_Project_2k25) | |
| * The prediction justification for some of the tasks are printed as the model views them. For this reason the text may contain special tokens like [CLS] or [SEP] or even hashtags splitting words. If you are are familiar with language models you will recognize these, if you do not have prior experience with language models you can ignore these characters. | |
| """ | |
| ) | |
| elif page == "Sentiment Analysis": | |
| st.header('Sentiment Analysis') | |
| st.markdown("") | |
| st.write( | |
| """ | |
| """ | |
| ) | |
| text = st.text_area("Paste text here", value=example_text) | |
| if st.button('🔥 Run!'): | |
| with st.spinner("Loading..."): | |
| preds, html = sentiment_analyzer.run(text) | |
| st.success('All done!') | |
| st.write("") | |
| st.subheader("Sentiment Predictions") | |
| st.bar_chart(data=preds, width=0, height=0, use_container_width=True) | |
| st.write("") | |
| st.subheader("Sentiment Justification") | |
| raw_html = html._repr_html_() | |
| st.components.v1.html(raw_html, height=500) | |
| elif page == "Keyword Extraction": | |
| st.header('Keyword Extraction') | |
| st.markdown("") | |
| st.write( | |
| """ | |
| """ | |
| ) | |
| text = st.text_area("Paste text here", value=example_text) | |
| max_keywords = st.slider('# of Keywords Max Limit', min_value=1, max_value=10, value=5, step=1) | |
| if st.button('🔥 Run!'): | |
| with st.spinner("Loading..."): | |
| annotation, keywords = keyword_extractor.generate(text, max_keywords) | |
| st.success('All done!') | |
| if annotation: | |
| st.subheader("Keyword Annotation") | |
| st.write("") | |
| annotated_text(*annotation) | |
| st.text("") | |
| st.subheader("Extracted Keywords") | |
| st.write("") | |
| df = pd.DataFrame(keywords, columns=['Extracted Keywords']) | |
| csv = df.to_csv(index=False).encode('utf-8') | |
| st.download_button('Download Keywords to CSV', csv, file_name='news_intelligence_keywords.csv') | |
| data_table = st.table(df) | |
| elif page == "Part of Speech Tagging": | |
| st.header('Part of Speech Tagging') | |
| st.markdown("") | |
| st.write( | |
| """ | |
| """ | |
| ) | |
| text = st.text_area("Paste text here", value=example_text) | |
| if st.button('🔥 Run!'): | |
| with st.spinner("Loading..."): | |
| preds = pos_tagger.classify(text) | |
| st.success('All done!') | |
| st.write("") | |
| st.subheader("Part of Speech tags") | |
| annotated_text(*preds) | |
| st.write("") | |
| st.components.v1.iframe('https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html', height=1000) | |
| elif page == "Emotion Detection": | |
| st.header('Emotion Detection') | |
| st.markdown("") | |
| st.write( | |
| """ | |
| """ | |
| ) | |
| text = st.text_area("Paste text here", value=example_text) | |
| if st.button('🔥 Run!'): | |
| with st.spinner("Loading..."): | |
| preds, html = emotion_detector.run(text) | |
| st.success('All done!') | |
| st.write("") | |
| st.subheader("Emotion Predictions") | |
| st.bar_chart(data=preds, width=0, height=0, use_container_width=True) | |
| raw_html = html._repr_html_() | |
| st.write("") | |
| st.subheader("Emotion Justification") | |
| st.components.v1.html(raw_html, height=500) | |
| elif page == "Named Entity Recognition": | |
| st.header('Named Entity Recognition') | |
| st.markdown("") | |
| st.write( | |
| """ | |
| """ | |
| ) | |
| text = st.text_area("Paste text here", value=example_text) | |
| if st.button('🔥 Run!'): | |
| with st.spinner("Loading..."): | |
| preds, ner_annotation = ner.classify(text) | |
| st.success('All done!') | |
| st.write("") | |
| st.subheader("NER Predictions") | |
| annotated_text(*ner_annotation) | |
| st.write("") | |
| st.subheader("NER Prediction Metadata") | |
| st.write(preds) | |