Delete app.py

app.py

@@ -1,37 +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']
-    return label
-
-
-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:
-        st.text('Your Finance news: ')
-        st.write(text)
-        st.slider(min_value=0, max_value=100,value=[0,100])
-        st.divider()  # Draws a horizontal rule
-        #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)
-        st.slider(min_value=0, max_value=100,value=[0,100])
-        st.divider()  # Draws a horizontal rule
-        #Stage 2: Sentiment Analytics
-        st.text('Processing Sentiment Analytics...')
-        label = sentiment(summary)
-        label=dicts[label]
-        st.slider(min_value=0, max_value=100,value=[0,100])
-        st.divider()  # Draws a horizontal rule
-        st.text('The sentiment of finance news is: ')
-        st.write(label)
-
-if __name__ == "__main__":
-    main()