| # Import necessary libraries | |
| import streamlit as st | |
| import transformers | |
| import torch | |
| from transformers import pipeline | |
| # Set up the Streamlit app | |
| st.title("Emotion Detection with Transformers") | |
| # Create a text input widget | |
| user_input = st.text_area("Enter your text:") | |
| # Define a function for sentiment analysis using transformers | |
| def load_model(): | |
| return pipeline("sentiment-analysis") | |
| # Load the sentiment analysis model | |
| sentiment_analyzer = load_model() | |
| # Create a button to analyze the emotion | |
| if st.button("Analyze Emotion"): | |
| if user_input: | |
| # Perform sentiment analysis on user input | |
| result = sentiment_analyzer(user_input) | |
| # Display the result | |
| emotion = result[0]['label'] | |
| st.write(f"Emotion: {emotion}") | |
| else: | |
| st.warning("Please enter some text to analyze.") | |