sentiment analysis

app.py

@@ -3,6 +3,8 @@ import toml
 import finnhub
 import datetime
 from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
 import streamlit as st
 
 
@@ -54,9 +56,29 @@ def get_news_summary(news):
     news_summary = SUMMARIZER(news)
     return news_summary[0]['summary_text']
 
+# get financial news sentiment
+@st.cache_resource
+def load_tokenizer():
+    return AutoTokenizer.from_pretrained("ProsusAI/finbert")
+
+@st.cache_resource
+def load_sentiment_classification_model():
+    return AutoModelForSequenceClassification.from_pretrained("ProsusAI/finbert")
+
+FINBERT_TOKENIZER = load_tokenizer()
+FINBERT_CLASSIFIER = load_sentiment_classification_model()
+
+def get_news_sentiment(news):
+    news = news.replace('\n\n', ' ')
+    inputs = FINBERT_TOKENIZER([news.replace('\n\n', '')], padding = True, truncation = True, return_tensors='pt')
+    outputs = FINBERT_CLASSIFIER(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    postive, neutral, negative = tuple(predictions.tolist()[0])
+    return postive, neutral, negative
+
 # APP
-st.title('Financial News Summarization and Sentiment')
-st.write('Enter the stock ticker and period for which you want financial news.')
+st.title('Financial News Headlines Summarization and Sentiment')
+st.write('Enter the stock ticker and period for which you want financial news headlines.')
 stock_ticker = st.text_input('Stock Ticker:', 'AAPL')
 start_date = st.date_input('Start Date:', datetime.datetime.today()-datetime.timedelta(days=20))
 end_date = st.date_input('End Date:', datetime.datetime.today())
@@ -64,6 +86,9 @@ news = financial_news(stock_ticker, start_date, end_date)
 st.write(news)
 
 st.header('Financial News Sentiment')
+with st.spinner('facebook bart model is summarizing the news...'):
+    postive, neutral, negative = get_news_sentiment(news)
+st.bar_chart(x=['positive', 'neutral', 'negative'], y=[postive, neutral, negative], color=[0,1,2])
 
 st.header('Financial News Summary')
 with st.spinner('facebook bart model is summarizing the news...'):