| 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() |