Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+
+def sentiment(summary):
+    pipe = pipeline("text-classification", model="WillWEI0103/CustomModel_finance_sentiment_analytics")
+    label = pipe(summary)[0]['label']
+    return label
+
+
+def main():
+    dicts={"bullish":'Positive📈',"bearish":'Negative📉','neutral':"Neutral😐"}
+    #st.set_page_config(page_title="Your Finance news", page_icon="📰")
+    st.header("Summarize Your Finance News and Analyze Sentiment")
+    text=st.text_input('Input your Finance news(Max lenth<=3000): ',max_chars=3000)
+    if isinstance(text,str):
+        st.text('Your Finance news📰": ')
+        st.write(str(text))
+    
+        #Stage 1: Text Summarization
+        st.text('Processing Finance News Summarization...')
+        text_summarize=pipeline("summarization", model="nickmuchi/fb-bart-large-finetuned-trade-the-event-finance-summarizer")
+        summary=text_summarize(text)[0]['summary_text']
+        st.write(summary)
+
+        #Stage 2: Sentiment Analytics
+        st.text('Processing Sentiment Analytics...')
+        label = sentiment(summary)
+        label=dicts[label]
+        st.text('The sentiment of finance news is: ')
+        st.write(label)
+
+if __name__ == "__main__":
+    main()