Update app.py

app.py

@@ -3,16 +3,20 @@ from transformers import pipeline
 
 # Load the text summarization pipeline
 try:
-    model3_p1 = pipeline("summarization", model="syndi-models/titlewave-t5-base")
+    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 news classification pipeline
-model_name2_p2 = "elozano/bert-base-cased-news-category"
+model_name = "elozano/bert-base-cased-news-category"
 try:
-    classifier = pipeline("text-classification", model=model_name2_p2, return_all_scores=True)
+    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("Summarization and News Classification")
@@ -24,26 +28,33 @@ with tab1:
     st.header("Text Summarization")
     # Input text for summarization
     text_to_summarize = st.text_area("Enter text to summarize:", "")
-    if st.button("Summarize") and 'model3_p1' in globals():
-        # 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'])
+    if st.button("Summarize"):
+        if summarizer_loaded and text_to_summarize:
+            try:
+                # Perform text summarization
+                summary = summarizer(text_to_summarize, max_length=130, min_length=30, do_sample=False)
+                # Display the summary result
+                st.write("Summary:", summary[0]['summary_text'])
+            except Exception as e:
+                st.error(f"Error during summarization: {e}")
+        else:
+            st.warning("Please enter text to summarize and ensure the model is loaded.")
 
 with tab2:
     st.header("News Classification")
     # Input text for news classification
     text_to_classify = st.text_area("Enter text to classify:", "")
-    if st.button("Classify") and 'classifier' in globals():
-        # Perform news 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)
+    if st.button("Classify"):
+        if classifier_loaded and text_to_classify:
+            try:
+                # Perform news classification
+                results = classifier(text_to_classify)[0]
+                # Find the category with the highest score
+                max_score = max(results, key=lambda x: x['score'])
+                st.write("Text:", text_to_classify)
+                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 classify and ensure the model is loaded.")