Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+
+# Load the text summarization model pipeline
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+# Streamlit application title
+st.title("Sentiment Analysis with text summarization for Singapore Airline")
+st.write("Summarization first, then sentiment analysis")
+
+# Text input for user to enter the text to summarize
+text = st.text_area("Enter the text to analyze", "")
+
+# Perform text summarization when the user clicks the "Go!" button
+if st.button("Go!"):
+    # Perform text summarization on the input text
+    results = summarizer(text)[0]['summary_text']
+    st.write("Step 1: Text after summarization:", results)
+
+    # Sentiment analysis as the second step
+    classifier = pipeline("text-classification", model="Rrrrrrrita/Custom_Sentiment", return_all_scores=True)
+    st.write("Classification for 3 emotions: positve, neutral, and negative")
+
+    labels = classifier(text)[0]
+    max_score = float('-inf')
+    max_label = ''
+    
+    for label in labels:
+        if label['score'] > max_score:
+            max_score = label['score']
+            max_label = label['label']
+            
+    st.write('Step 2: Sentiment Analysis:')
+    st.write("Label:", max_label)
+    st.write("Score:", max_score)