Update app.py

app.py

@@ -25,9 +25,7 @@ def pdf_changes(pdf_doc, open_ai_key):
         #To create a vector store, we use the Chroma class, which takes the documents (pages in our case), the embeddings instance, and a directory to store the vector data
         vectordb = Chroma.from_documents(pages, embedding=embeddings)
         
-        #Finally, we create the bot using the ConversationalRetrievalChain class
-        #A ConversationalRetrievalChain is similar to a RetrievalQAChain, except that the ConversationalRetrievalChain allows for 
-        #passing in of a chat history which can be used to allow for follow up questions.
+        #Finally, we create the bot using the RetrievalQAChain class
         global pdf_qa
         pdf_qa = RetrievalQA.from_chain_type(ChatOpenAI(temperature=0, model_name="gpt-4"), vectordb.as_retriever(), return_source_documents=False)
         
@@ -72,7 +70,7 @@ title = """
     <h1>Chatbot for PDFs - GPT-4</h1>
     <p style="text-align: center;">Upload a .PDF, click the "Load PDF to LangChain" button, <br />
     Wait for the Status to show Ready, start typing your questions. <br />
-    The app is set to store chat-history and is built on GPT-4</p>
+    The app is built on GPT-4</p>
 </div>
 """
 
@@ -82,7 +80,7 @@ with gr.Blocks(css=css) as demo:
         gr.HTML(title)
         
         with gr.Column():
-            openai_key = gr.Textbox(label="Your OpenAI API key", type="password")
+            openai_key = gr.Textbox(label="Your GPT-4 OpenAI API key", type="password")
             pdf_doc = gr.File(label="Load a pdf", file_types=['.pdf'], type="file")
             with gr.Row():
                 langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False)