Update app.py

app.py

@@ -1,27 +1,23 @@
 import streamlit as st
-import pandas as pd
+from transformers import pipeline
 
-def main():
-    st.set_page_config(page_title="Emotion analysis", page_icon="🦜")
-    st.header("Turn Your Input Into Sentiment Score")
-    uploaded_file = st.file_uploader("Upload CSV File", type="csv")
+# Load the summarization & translation model pipeline
+tran_sum_pipe = pipeline("translation", model='utrobinmv/t5_summary_en_ru_zh_base_2048')
+sentiment_pipeline = pipeline("text-classification", model="Howosn/Sentiment_Model")
 
-    if uploaded_file is not None:
-        # Process the uploaded file
-        processed_data = process_uploaded_file(uploaded_file)
-        # Display the processed data or perform further actions
-        st.write(processed_data)
+# Streamlit application title
+st.title("Emotion analysis")
+st.write("Turn Your Input Into Sentiment Score")
 
-        #Stage 1: Translate and summary the text from Chinese to English and summarize
-        st.text('Processing translation...')
-        trans = tras_sum(uploaded_file.name)
-        st.write(trans)
+# Text input for the user to enter the text to analyse
+text = st.text_area("Enter the text", "")
 
-        #Stage 2: Sentiment analysis
-        st.text('Generating a Sentiment result...')
-        senti = sentiment(trans)
-        st.write(senti)
-
-
-if __name__ == "__main__":
-    main()
+# Perform analysis result when the user clicks the "Analyse" button
+if st.button("Analyse"):
+    # Perform text classification on the input text
+    trans_sum = tran_sum_pipe(text)
+    result = sentiment_pipeline(trans_sum)
+    
+    # Display the analysis result
+    st.write("Text:", text)
+    st.write("result:", result)