sentiment analysis

app.py

@@ -5,6 +5,7 @@ import datetime
 from transformers import pipeline
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
+import pandas as pd
 import streamlit as st
 
 
@@ -87,8 +88,19 @@ st.write(news)
 
 st.header('Financial News Sentiment')
 with st.spinner('finbert model getting sentiment...'):
-    postive, neutral, negative = get_news_sentiment(news)
-st.bar_chart(x=['positive', 'neutral', 'negative'], y=[postive, neutral, negative], color=[0,1,2])
+    positive, neutral, negative = get_news_sentiment(news)
+
+# white: #D0D3D4
+# red: #E74C3C
+# green: #2ECC71
+sentiment_df = pd.DataFrame(
+    {
+        'sentiment_labels':['positive', 'neutral', 'negative'],
+        'sentiment':[positive, neutral, negative],
+        'color':['#2ECC71', '#D0D3D4', '#E74C3C']
+    }
+)
+st.bar_chart(sentiment_df, x='sentiment_labels', y='sentiment', color='color', horizontal=True)
 
 st.header('Financial News Summary')
 with st.spinner('facebook bart model is summarizing the news...'):

requirements.txt

@@ -7,6 +7,7 @@ fsspec==2024.10.0
 huggingface-hub==0.26.2
 idna==3.10
 numpy==2.1.3
+pandas
 packaging==24.2
 pipreqs==0.4.13
 PyYAML==6.0.2