Update app.py

app.py

@@ -27,13 +27,16 @@ def load_pdf_and_generate_embeddings(pdf_doc, open_ai_key, relevant_pages='all')
             pages_to_be_loaded = pages.copy()
         else:
             page_numbers = relevant_pages.split(",")
-            for page_number in page_numbers:
-                pageIndex = int(page_number)-1
-                if pageIndex >=0 and pageIndex <len(pages):
-                    pages_to_be_loaded.append(pages[pageIndex])
-            #In the scenario where none of the page numbers supplied exist in the PDF, we will revert to using the entire PDF.
-            if len(pages_to_be_loaded) ==0:
-                 pages_to_be_loaded = pages.copy()
+            if len(page_numbers) != 0:
+                for page_number in page_numbers:
+                    pageIndex = int(page_number)-1
+                    if pageIndex >=0 and pageIndex <len(pages):
+                        pages_to_be_loaded.append(pages[pageIndex])
+                #In the scenario where none of the page numbers supplied exist in the PDF, we will revert to using the entire PDF.
+                if len(pages_to_be_loaded) ==0:
+                     pages_to_be_loaded = pages.copy()
+            else:
+                pages_to_be_loaded = pages.copy()
              
         #To create a vector store, we use the Chroma class, which takes the documents (pages in our case) and the embeddings instance
         vectordb = Chroma.from_documents(pages_to_be_loaded, embedding=embeddings)