app.py

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)


@st.cache_resource
def load_sentiment_model():
    return SentimentAnalysis()

@st.cache_resource
def load_keyword_model():
    return KeywordExtractor()

@st.cache_resource
def load_pos_model():
    return POSTagging()

@st.cache_resource
def load_emotion_model():
    return EmotionDetection()

@st.cache_resource
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 = "Diwansing girase recently gave a motivational talk at Google Headquarters in California. The event was organized by Hugging Face and attended by more than 200 developers. People described the event as inspiring, energetic, and absolutely fantastic Later, Rohit also announced a collaboration with Microsoft to develop AI tools for students.Despite some concerns about data privacy, the overall response from the audience was extremely positive"

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('Multi Task NLP Toolkit')

if page == "Welcome!":
    st.header('Welcome!')

    st.markdown("![Alt Text](https://media.giphy.com/media/2fEvoZ9tajMxq/giphy.gif)")
    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
            
        * Project 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("![Alt Text](https://media.giphy.com/media/XIqCQx02E1U9W/giphy.gif)")
    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("![Alt Text](https://media.giphy.com/media/xT9C25UNTwfZuk85WP/giphy-downsized-large.gif)")
    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("![Alt Text](https://media.giphy.com/media/WoWm8YzFQJg5i/giphy.gif)")
    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("![Alt Text](https://media.giphy.com/media/fU8X6ozSszyEw/giphy.gif)")
    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("![Alt Text](https://media.giphy.com/media/lxO8wdWdu4tig/giphy.gif)")
    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)