Update app.py

app.py

@@ -26,21 +26,17 @@ pdf_file = st.file_uploader("Upload PDF", type=["pdf"])
 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...")
-    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, multi_label=True)  # Using the first 1000 characters
-    genres = sorted(zip(result["labels"], result["scores"]), key=lambda x: x[1], reverse=True)
-    
-    st.subheader("Top 20 Detected Genres:")
-    top_genres = genres[:20]
-    for genre, score in top_genres:
-        st.write(f"**{genre.capitalize()}**: {score:.2f}")
-else:
-    st.error("No text could be extracted from the PDF. Please try another file.")
+    if text.strip():
+        st.write("PDF Text Extracted. Performing Genre Classification...")
+        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, multi_label=True)  # Using the first 1000 characters
+        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:
+            st.write(f"**{genre.capitalize()}**: {score:.2f}")
+    else:
+        st.error("No text could be extracted from the PDF. Please try another file.")