Create app.py

app.py

@@ -0,0 +1,49 @@
+import streamlit as st
+from transformers import pipeline
+
+# Load the text summarization pipeline
+try:
+    summarizer = pipeline("summarization", model="syndi-models/titlewave-t5-base")
+    summarizer_loaded = True
+except ValueError as e:
+    st.error(f"Error loading summarization model: {e}")
+    summarizer_loaded = False
+
+# Load the Question classification pipeline
+model_name = "elozano/bert-base-cased-news-category"
+try:
+    classifier = pipeline("text-classification", model=model_name, return_all_scores=True)
+    classifier_loaded = True
+except ValueError as e:
+    st.error(f"Error loading classification model: {e}")
+    classifier_loaded = False
+
+# Streamlit app title
+st.title("Question Summarization and Classification")
+
+# Input text for summarization and classification
+text_input = st.text_area("Enter long question to summarize and classify:", "")
+
+if st.button("Process"):
+    if summarizer_loaded and classifier_loaded and text_input:
+        try:
+            # Perform text summarization
+            summary = summarizer(text_input, max_length=130, min_length=30, do_sample=False)
+            summarized_text = summary[0]['summary_text']
+            # Display the summary result
+            st.write("Summary:", summarized_text)
+        except Exception as e:
+            st.error(f"Error during summarization: {e}")
+
+        try:
+            # Perform question classification on the summarized text
+            results = classifier(summarized_text)[0]
+            # Find the category with the highest score
+            max_score = max(results, key=lambda x: x['score'])
+            st.write("Summarized Text:", summarized_text)
+            st.write("Category:", max_score['label'])
+            st.write("Score:", max_score['score'])
+        except Exception as e:
+            st.error(f"Error during classification: {e}")
+    else:
+        st.warning("Please enter text to process and ensure both models are loaded.")