Update app.py

app.py

@@ -11,7 +11,7 @@ from langchain.chains import RetrievalQA # for conversing with chatGPT
 from langchain.chat_models import ChatOpenAI # the LLM model we'll use (ChatGPT)
 from langchain import PromptTemplate
 
-def load_pdf_and_generate_embeddings(pdf_doc, open_ai_key, relevant_pages='all'):
+def load_pdf_and_generate_embeddings(pdf_doc, open_ai_key, relevant_pages):
     if openai_key is not None:
         os.environ['OPENAI_API_KEY'] = open_ai_key
         #Load the pdf file
@@ -23,9 +23,7 @@ def load_pdf_and_generate_embeddings(pdf_doc, open_ai_key, relevant_pages='all')
 
         pages_to_be_loaded =[]
 
-        if relevant_pages == 'all':
-            pages_to_be_loaded = pages.copy()
-        else:
+        if relevant_pages:
             page_numbers = relevant_pages.split(",")
             if len(page_numbers) != 0:
                 for page_number in page_numbers:
@@ -37,6 +35,8 @@ def load_pdf_and_generate_embeddings(pdf_doc, open_ai_key, relevant_pages='all')
                      pages_to_be_loaded = pages.copy()
             else:
                 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)