Update app.py

app.py

@@ -18,8 +18,7 @@ from transformers import pipeline
 # citation_generator = pipeline("text-generation", model="gpt2")
 
 # from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
-from transformers import T5Tokenizer, T5ForConditionalGeneration
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
 
 def search_related_articles_crossref(query, max_results=3):
@@ -65,9 +64,9 @@ def extract_text_from_html(url):
     except Exception as e:
         return f"Error extracting text: {str(e)}"
 
-
-tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-small")
-model = T5ForConditionalGeneration.from_pretrained("google-t5/t5-small")
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("pszemraj/pegasus-large-summary-explain")
+model = AutoModelForSeq2SeqLM.from_pretrained("pszemraj/pegasus-large-summary-explain")
 
 def summarize_article(article_text):
     """Summarize a given article's text."""
@@ -187,11 +186,11 @@ def research_assistant(research_topic, citation_style):
             # Fetching article content might not be feasible; consider using metadata
             article_content += f"{extract_text_from_html(article['link'])}.\n"  # Simplified; actual content may require other methods
 
-            # citation, error = generate_citation_t5(article['title'], citation_style, article['link'])
-            # if error:
-            #     citations.append(f"Error generating citation for '{article['title']}': {error}")
-            # else:
-            #     citations.append(citation)
+            citation, error = generate_citation_t5(article['title'], citation_style, article['link'])
+            if error:
+                citations.append(f"Error generating citation for '{article['title']}': {error}")
+            else:
+                citations.append(citation)
 
         except Exception as e:
             summaries.append(f"Error processing article '{article['title']}': {str(e)}")