Update app.py

app.py

@@ -1,15 +1,43 @@
 import streamlit as st
 from transformers import pipeline
-def main():
-  sentiment_pipeline = pipeline("sentiment-analysis")
-  st.title("Sentiment Analysis with HuggingFace Spaces")
-  st.write("Enter a sentence to analyze its sentiment:")
-  user_input = st.text_input("")
-  if user_input:
-    result = sentiment_pipeline(user_input)
-    sentiment = result[0]["label"]
-    confidence = result[0]["score"]
-    st.write(f"Sentiment: {sentiment}")
-    st.write(f"Confidence: {confidence:.2f}")
-if __name__ == "__main__":
-  main()
+
+# Load the text summarization pipeline
+model3_p1 = pipeline("summarization", model="syndi-models/titlewave-t5-base")
+
+# Load the classification pipeline
+model_name2_p2 = "elozano/bert-base-cased-news-category"
+classifier = pipeline("text-classification", model=model_name2_p2, return_all_scores=True)
+
+# Streamlit app title
+st.title("Question Summarization and Classification")
+
+# Tab layout
+tab1, tab2 = st.tabs(["Question Summarization", "Question Classification"])
+
+with tab1:
+    st.header("Question Summarization")
+    # Input text for summarization
+    text_to_summarize = st.text_area("Enter question to summarize:", "")
+    if st.button("Summarize"):
+        # Perform text summarization
+        summary = model3_p1(text_to_summarize, max_length=130, min_length=30, do_sample=False)
+        # Display the summary result
+        st.write("Summary:", summary[0]['summary_text'])
+
+with tab2:
+    st.header("Question Classification")
+    # Input text for news classification
+    text_to_classify = st.text_area("Enter question title to classify:", "")
+    if st.button("Classify"):
+        # Perform question classification
+        results = classifier(text_to_classify)[0]
+        # Display the classification result
+        max_score = float('-inf')
+        max_label = ''
+        for result in results:
+            if result['score'] > max_score:
+                max_score = result['score']
+                max_label = result['label']
+        st.write("Text:", text_to_classify)
+        st.write("Category:", max_label)
+        st.write("Score:", max_score)