app.py

import streamlit as st
from transformers import pipeline

def sentiment_funtion(text):
    model = pipeline("sentiment-analysis", model="stevhliu/my_awesome_model")
    output = model(text)[0]
    label = output["label"] 
    score = output["score"]
    return label , score

def main():
    st.title("Sentiment Analysis app")
    st.sidebar.image("https://huggingface.co/spaces/Dineth98/TA_1/resolve/main/sentiment-analysis-1280x720.jpg", use_column_width=True)
    st.sidebar.subheader("About This App")
    st.sidebar.write("The app takes feedbacks as input and automatically identifies whether the feedback is positive, negative, or neutral. This analysis can be useful for businesses to gain insights into customer opinions and improve their products or services accordingly. The app can also be used by individuals to analyze social media posts, emails, and other forms of written communication to better understand the emotional tone of the message.")
    user_input = st.text_area("Input Text Here")
    if st.button("Sentiment"):
        label , score = sentiment_funtion(user_input)
        if label == "LABEL_1":
            label = "Positive"
            postive_score = score
            negative_score = 1 - score
            color = "green"
        else:
            label = "Negative"
            postive_score = 1 - score
            negative_score = score
            color = "red"
            
        style = f'color:{color}'
        text = f'Sentiment Label: {label}'
        sentiment_text = f'<h4 style="{style}">{text}</h4>'
        st.write("Sentiment:")
        st.write(sentiment_text, unsafe_allow_html=True)
        st.write(f"Positive Score: {postive_score:.2f}")
        st.write(f"Negative Score: {negative_score:.2f}")

if __name__ == "__main__":
    main()