Update app.py

app.py

@@ -27,16 +27,14 @@ def load_pdf_and_generate_embeddings(pdf_doc, open_ai_key, relevant_pages):
             page_numbers = relevant_pages.split(",")
             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()
-        else:
+                    if page_number.isdigit():
+                        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()
+            
              
         #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)