Delete app.py

app.py

@@ -1,38 +0,0 @@
-import streamlit as st
-from transformers import pipeline
-import time
-
-def sentiment(summary):
-    pipe = pipeline("text-classification", model="WillWEI0103/CustomModel_finance_sentiment_analytics")
-    label = pipe(summary)[0]['label']
-    score = pipe(summary)[0]['score']
-    return label,score
-
-
-def main():
-    dicts={"bullish":'Positive📈',"bearish":'Negative📉','neutral':"Neutral😐"}
-    st.header("Summarize Your Finance News and Analyze Sentiment📰")
-    text=st.text_input('Input your Finance news(Max lenth<=3000): ',None,max_chars=3000)
-    #if text is not None:
-    if st.button('Conduct'):
-        st.text_area('Your Finance News: ',text,height=100)
-
-        #Stage 1: Text Summarization
-        with st.status("Processing Finance News Summarization...") as status:
-            text_summarize=pipeline("summarization", model="nickmuchi/fb-bart-large-finetuned-trade-the-event-finance-summarizer")
-            summary=text_summarize(text)[0]['summary_text']
-            status.update(label="Summarization Completed", state="complete", expanded=False)
-        st.text_area('Your Finance News Summary',summary,height=30)
-
-        #Stage 2: Sentiment Analytics
-        with st.status("Processing Sentiment Analytics..") as status:
-            label,score = sentiment(summary)
-            label=dicts[label]
-            status.update(label="Sentiment Analytics Completed", state="complete", expanded=False)
-        st.text('The Sentiment of the Finance News is: ')
-        st.text(label)
-        st.text('The Sentiment Score: ')
-        st.text(round(score,3))
-        
-if __name__ == "__main__":
-    main()