Create app.py

app.py

@@ -0,0 +1,36 @@
+import streamlit as st
+from transformers import pipeline
+
+# Summarization
+def summarization(text):
+    text_model = pipeline("text-generation", model="ainize/bart-base-cnn")
+    summary = text_model(text, max_length=100, do_sample=False)[0]["generated_text"]
+    return summary
+
+# Sentiment Classification
+def sentiment_classification(summary):
+    sentiment_model = pipeline("text-classification", model="wxrrrrrrr/finetuned_sentiment_analysis")
+    result = sentiment_model(summary, max_length=100, do_sample=False)[0]['label']
+    return result
+
+def main():
+    st.set_page_config(page_title="Your Text Analysis", page_icon="🦜")
+    st.header("Tell me your comments!")
+    text_input = st.text_input("Enter your text here:")
+
+    if text_input:
+        # Stage 1: Summarization
+        st.text('Processing text...')
+        summary = summarization(text_input)
+        # st.write(summary)
+
+        # Stage 2: Sentiment Classification
+        st.text('Analyzing sentiment...')
+        sentiment = sentiment_classification(summary)
+        st.write(sentiment)
+
+        # Display the classification result
+        st.write("Sentiment:", sentiment)
+
+if __name__ == '__main__':
+    main()