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| from time import strftime | |
| import feedparser | |
| import streamlit as st | |
| from transformers import AutoTokenizer, pipeline, \ | |
| AutoModelForSequenceClassification | |
| def load_model(): | |
| return AutoModelForSequenceClassification.from_pretrained("warwickai/fin-perceiver") | |
| def load_news(feed): | |
| return feedparser.parse(feed).get('entries') | |
| def filter_with_sentiment(articles, sentiments): | |
| return filter( | |
| lambda article: article[1].get('label') in sentiments, | |
| articles | |
| ) | |
| tokenizer = AutoTokenizer.from_pretrained("warwickai/fin-perceiver") | |
| with st.spinner('📈 Loading model...'): | |
| model = load_model() | |
| pipe = pipeline('text-classification', model=model, tokenizer=tokenizer) | |
| def classify_articles(articles, target_pipeline): | |
| headlines = [article.title for article in articles] | |
| sentiment = target_pipeline(headlines) | |
| return list(zip(articles, sentiment)) | |
| rss_feeds = { | |
| 'yahoo': 'https://finance.yahoo.com/news/rssindex' | |
| } | |
| sentiment_distribution = { | |
| 'positive': 0, | |
| 'negative': 0, | |
| 'neutral': 0 | |
| } | |
| st.title('FINPerceiver') | |
| target_source = st.sidebar.selectbox( | |
| 'Select a financial news source', | |
| rss_feeds.keys()) | |
| target_sentiments = st.sidebar.multiselect( | |
| label='Select the target sentiments', | |
| options=sentiment_distribution.keys(), | |
| default=sentiment_distribution.keys()) | |
| with st.spinner('📰 Loading articles...'): | |
| target_articles = sorted( | |
| load_news( | |
| rss_feeds.get(target_source) | |
| ), | |
| key=lambda article: article.published_parsed, | |
| reverse=True | |
| ) | |
| with st.spinner('⚙️ Analysing articles...'): | |
| classified_articles = classify_articles(target_articles, pipe) | |
| total_articles = 0 | |
| for article, sentiment in classified_articles: | |
| total_articles += 1 | |
| sentiment_distribution[sentiment.get('label')] += 1 | |
| for sentiment in sentiment_distribution.keys(): | |
| sentiment_distribution[sentiment] /= total_articles * 0.01 | |
| st.sidebar.subheader('Summary') | |
| st.sidebar.metric("Positive", f"👍 {sentiment_distribution.get('positive'):.2f}%") | |
| st.sidebar.metric("Neutral", f"😐 {sentiment_distribution.get('neutral'):.2f}%") | |
| st.sidebar.metric("Negative", f"👎 {sentiment_distribution.get('negative'):.2f}%") | |
| for article, sentiment in filter_with_sentiment(classified_articles, target_sentiments): | |
| if 'media_content' in article: | |
| img_url = article.media_content[0].get('url') | |
| st.image(img_url, width=300) | |
| st.markdown( | |
| f''' | |
| #### {article.title} | |
| Published on {strftime('%H:%M %d/%m/%Y', article.published_parsed)} | |
| **Sentiment:** {sentiment.get('label').capitalize()} | |
| ''' | |
| ) | |