Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+
+
+
+# txt2Story
+def text_summarize(text):
+    pipe = pipeline("summarization", model="human-centered-summarization/financial-summarization-pegasus")
+    summary = pipe(text)[0]['summary_text']
+    print(summary)
+    return summary
+
+# Story2Audio
+def sentiment(story_text):
+    pipe = pipeline("text-classification", model="WillWEI0103/CustomModel_finance_sentiment_analytics")
+    label = pipe(story_text)[0]['label']
+    return label
+
+
+def main(text):
+    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: ')
+    
+    #Stage 1: Text Summarization
+    st.text('Processing Finance News Summarization...')
+    summary = text_summarize(text)
+    st.write(summary)
+
+    #Stage 2: Sentiment Analytics
+    st.text('Processing Sentiment Analytics...')
+    label = sentiment(summary)
+    st.text('The sentiment of finance news is: ')
+    st.write(label)
+
+if __name__ == "__main__":
+    main()