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from time import strftime
import feedparser
import streamlit as st
from transformers import AutoTokenizer, pipeline, \
AutoModelForSequenceClassification
@st.cache(allow_output_mutation=True, show_spinner=False)
def load_model():
return AutoModelForSequenceClassification.from_pretrained("warwickai/fin-perceiver")
@st.cache(show_spinner=False)
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()}
'''
)
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