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import feedparser | |
import streamlit as st | |
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline | |
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('π 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', | |
'reuters': 'https://www.reutersagency.com/feed/?best-topics=business-finance&post_type=best' | |
} | |
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('π° Loading articles...'): | |
target_articles = load_news(rss_feeds.get(target_source)) | |
with st.spinner('βοΈ 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"π {sentiment_distribution.get('positive'):.2f}%") | |
st.sidebar.metric("Neutral", f"π {sentiment_distribution.get('neutral'):.2f}%") | |
st.sidebar.metric("Negative", f"π {sentiment_distribution.get('negative'):.2f}%") | |
for article, sentiment in filter_with_sentiment(classified_articles, target_sentiments): | |
if article.media_contet: | |
img_url = article.media_content[0].get('url') | |
st.image(img_url, width=300) | |
st.markdown( | |
f''' | |
#### {article.title} | |
**Sentiment:** {sentiment.get('label').capitalize()} | |
''' | |
) | |