Update app.py

app.py

@@ -34,9 +34,9 @@ if pdf_file is not None:
         st.write("PDF Text Extracted. Predicting the Genres...")
         classifier = pipeline("zero-shot-classification", model = "facebook/bart-large-mnli") #load_classifier()
         # Define candidate genres
-        candidate_labels =["Romance", "Mystery", "Thriller", "Science Fiction", "Fantasy", "Horror", "Historical Fiction", "Crime", "Western", "Dystopian", "Biography", "Autobiography", "Memoir", "History", "Self-Help", "Travel", "Essay", "Journalism", "Sonnet", "Haiku", "Free Verse", "Narrative Poetry", "Lyric Poetry", "Tragedy", "Comedy", "Melodrama", "Farce", "Graphic Novel", "Epistolary", "Magical Realism", "Satire", "Young Adult Fiction"]
+        candidate_labels ["Scientific Papers", "Technical Documentation", "Research Reports", "Academic Journals", "White Papers", "Technical Manuals", "Patents", "Software Documentation", "Engineering Specifications", "Computer Science Literature", "Machine Learning Publications", "Data Science Reports", "Network Architecture Descriptions", "Cybersecurity Analysis", "Algorithm Descriptions", "Fantasy", "Science Fiction", "Mystery", "Thriller", "Romance", "Historical Fiction", "Horror", "Adventure", "Crime", "Western", "Dystopian", "Magical Realism", "Young Adult", "Children's Literature", "Gothic", "Biography", "Autobiography", "Memoir", "Travel Writing", "History", "Philosophy", "Psychology", "Self-Help", "Political Commentary", "True Crime", "Nature Writing", "Cultural Studies", "Sociology", "Anthropology", "Religious Studies", "Poetry", "Drama", "Epic", "Short Story", "Novel", "Novella", "Satire", "Tragedy", "Comedy", "Tragicomedy", "News Reporting", "Feature Writing", "Opinion Pieces", "Investigative Journalism", "Editorial", "Profile Writing", "Sports Writing", "Political Journalism", "Dissertation", "Thesis", "Critical Analysis", "Comparative Study", "Literature Review", "Meta-Analysis", " Case Study"]  # 
         # Perform zero-shot classification
-        result = classifier(text[:1000], candidate_labels, multi_label=True) #[:1000]), candidate_labels, multi_label=True)
+        result = classifier(text[:3000], candidate_labels, multi_label=True) #[:1000]), candidate_labels, multi_label=True)
         genres = sorted(zip(result["labels"], result["scores"]), key=lambda x: x[1], reverse=True) 
         st.subheader("Top 20 Detected Genres:")
         top_genres = genres[:20]