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| import streamlit as st | |
| from transformers import pipeline | |
| sentiment_analysis = pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment") | |
| def convert_label_to_sentiment(label): | |
| if label in ["4", "5"]: | |
| return "Positive" | |
| elif label in ["1", "2"]: | |
| return "Negative" | |
| else: | |
| return "Neutral" | |
| def perform_sentiment_analysis(text): | |
| result = sentiment_analysis(text) | |
| return {'label': result[0]['label'], 'score': result[0]['score']} | |
| def main(): | |
| st.title("Financial Sentiment Analysis") | |
| # Input for financial content | |
| financial_content = st.text_area("Enter Financial Content:", "With the launch of Apple Silicon, Apple shares have increased") | |
| # Perform sentiment analysis on button click | |
| if st.button("Submit"): | |
| if financial_content.strip(): | |
| sentiment_result = perform_sentiment_analysis(financial_content) | |
| sentiment = convert_label_to_sentiment(sentiment_result['label'][0]) | |
| st.success(f"Sentiment: {sentiment} | Score: {sentiment_result['score']:.2f}") | |
| else: | |
| st.warning("Please enter financial content before submitting.") | |
| if __name__ == "__main__": | |
| main() | |