Update app.py

app.py

@@ -21,10 +21,22 @@ def load_pdf_and_generate_embeddings(pdf_doc, open_ai_key, relevant_pages='all')
         #Create an instance of OpenAIEmbeddings, which is responsible for generating embeddings for text
         embeddings = OpenAIEmbeddings()
 
+        pages_to_be_loaded =[]
+
         if relevant_pages == 'all':
-            pages = pages
+            pages_to_be_loaded = pages.copy()
+        else:
+            page_numbers = relevant_pages.split(",")
+            for page_number in page_numbers:
+                pageIndex = 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, embedding=embeddings)
+        vectordb = Chroma.from_documents(pages_to_be_loaded, embedding=embeddings)
         
         #Finally, we create the bot using the RetrievalQA class
         global pdf_qa