app.py

import pandas as pd
import streamlit as st
from streamlit_text_rating.st_text_rater import st_text_rater
from sentiment import classify_sentiment

st.set_page_config(  # Alternate names: setup_page, page, layout
    layout="wide",  # Can be "centered" or "wide". In the future also "dashboard", etc.
    initial_sidebar_state="auto",  # Can be "auto", "expanded", "collapsed"
    page_title='None',  # String or None. Strings get appended with "• Streamlit".
)

padding_top = 0
st.markdown(f"""
    <style>
        .reportview-container .main .block-container{{
            padding-top: {padding_top}rem;
        }}
    </style>""",
    unsafe_allow_html=True,
)

def set_page_title(title):
    st.sidebar.markdown(unsafe_allow_html=True, body=f"""
        <iframe height=0 srcdoc="<script>
            const title = window.parent.document.querySelector('title') \

            const oldObserver = window.parent.titleObserver
            if (oldObserver) {{
                oldObserver.disconnect()
            }} \

            const newObserver = new MutationObserver(function(mutations) {{
                const target = mutations[0].target
                if (target.text !== '{title}') {{
                    target.text = '{title}'
                }}
            }}) \

            newObserver.observe(title, {{ childList: true }})
            window.parent.titleObserver = newObserver \

            title.text = '{title}'
        </script>" />
    """)


set_page_title('NLP use cases')

# Hide Menu Option
hide_streamlit_style = """
            <style>
            #MainMenu {visibility: hidden;}
            footer {visibility: hidden;}
            </style>
            """
st.markdown(hide_streamlit_style, unsafe_allow_html=True)


st.title("NLP use cases")

with st.sidebar:
    st.title("NLP tasks")
    select_task=st.selectbox(label="Select task from drop down menu",
                 options=['Detect Sentiment','Zero Shot Classification'])


if select_task=='Detect Sentiment':
    st.header("You are now performing Sentiment Analysis")
    input_texts = st.text_input(label="Input texts separated by comma")

    if input_texts!='':
        sentiments = classify_sentiment(input_texts)
        for i,t in enumerate(input_texts.split(',')):
            if sentiments[i]=='Positive':
                response=st_text_rater(t + f"--> This statement is {sentiments[i]}",
                                       color_background='rgb(154,205,50)')
            else:
                response = st_text_rater(t + f"--> This statement is {sentiments[i]}",
                                         color_background='rgb(233, 116, 81)')