Initial

README.md

@@ -1,8 +1,8 @@
 ---
-title: Fin Perceiver Demo
-emoji: 🦀
+title: FINPerceiver Demo
+emoji: 💸
 colorFrom: blue
-colorTo: pink
+colorTo: green
 sdk: streamlit
 app_file: app.py
 pinned: false

app.py

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+import feedparser
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+
+
+@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',
+    '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('📰 Loading articles...'):
+    target_articles = load_news(rss_feeds.get(target_source))
+
+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):
+    img_url = article.media_content[0].get('url')
+
+    st.image(img_url, width=300)
+    st.markdown(
+        f'''
+        #### {article.title}
+        **Sentiment:** {sentiment.get('label').capitalize()}
+        '''
+    )

requirements.txt

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+torch
+transformers
+streamlit
+feedparser