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| import streamlit as st | |
| from transformers import pipeline | |
| # Create a title and a text input for the user to enter the text | |
| st.title('Sentiment Analysis App') | |
| text = st.text_input('Enter text here') | |
| # Create a dropdown for the user to select the pre-trained model | |
| model_name = st.selectbox( | |
| 'Select a pre-trained model', | |
| ['distilbert-base-uncased', 'distilbert-base-cased', 'bert-base-uncased', 'bert-base-cased', | |
| 'cardiffnlp/twitter-roberta-base-sentiment-latest', | |
| 'cardiffnlp/twitter-xlm-roberta-base-sentiment', | |
| 'j-hartmann/emotion-english-distilroberta-base', | |
| 'ProsusAI/finbert' | |
| ] | |
| ) | |
| # Create a button to perform the sentiment analysis | |
| if st.button('Analyze Sentiment'): | |
| # Load the selected model | |
| model = pipeline('sentiment-analysis', model=model_name) | |
| # Perform sentiment analysis on the input text | |
| result = model(text)[0] | |
| # Print the sentiment label and score | |
| st.write(f'Sentiment: {result["label"]}') | |
| st.write(f'Score: {result["score"]}') | |