Update app.py

Hmmm... Almost done😫

app.py

@@ -31,12 +31,12 @@ if pdf_file is not None:
     st.write("Processing the PDF...")
     text = extract_text_from_pdf(pdf_file)
     if text.strip():
-        st.write("PDF Text Extracted. Performing Genre Classification...")
+        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"]
         # Perform zero-shot classification
-        result = classifier(text[:1000], candidate_labels) #[:1000]), candidate_labels, multi_label=True)
+        result = classifier(text[:1000], candidate_labels, multi_label=True) #[:1000]), candidate_labels, multi_label=True)
         genres = sorted(zip(result["labels"], result["scores"]), key=lambda x: x[1], reverse=Truest.subheader("Top 20 Detected Genres:"))
         top_genres = genres[:20]
         for genre, score in top_genres: