Update app.py

app.py

@@ -18,6 +18,7 @@ def load_pdf_and_generate_embeddings(pdf_doc, open_ai_key, relevant_pages):
         #Load the pdf file
         loader = OnlinePDFLoader(pdf_doc.name)
         pages = loader.load_and_split()
+        print("PDF has been loaded and split")
         
         #Create an instance of OpenAIEmbeddings, which is responsible for generating embeddings for text
         embeddings = OpenAIEmbeddings()
@@ -35,10 +36,12 @@ def load_pdf_and_generate_embeddings(pdf_doc, open_ai_key, relevant_pages):
         #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()
+        print(len(pages_to_be_loaded))
             
              
         #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)
+        print("Vectordb has been created")
         
         #Finally, we create the bot using the RetrievalQA class
         global pdf_qa
@@ -55,7 +58,7 @@ def load_pdf_and_generate_embeddings(pdf_doc, open_ai_key, relevant_pages):
                                                                         
         chain_type_kwargs = {"prompt": PROMPT}
         pdf_qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(temperature=0, model_name="gpt-4"),chain_type="stuff", retriever=vectordb.as_retriever(search_kwargs={"k": 4}), chain_type_kwargs=chain_type_kwargs, return_source_documents=False)
-       
+        print("GPT-4 is ready to take questions!")
         return "Ready"
     else:
         return "Please provide an OpenAI gpt-4 API key"