Update app.py

app.py

@@ -48,13 +48,13 @@ def invoke(openai_api_key, youtube_url, process_video, prompt):
     return result["result"]
 
 description = """<strong>Overview:</strong> The app demonstrates how to use a <strong>Large Language Model</strong> (LLM) with <strong>Retrieval Augmented Generation</strong> 
-                 (RAG) on external data (YouTube videos in this case, but it could be PDFs, URLs, databases, or other structured/unstructured and private/public 
+                 (RAG) on external data (YouTube videos in this case, but it could be PDFs, URLs, databases, or other structured/unstructured private/public 
                  <a href='https://raw.githubusercontent.com/bstraehle/ai-ml-dl/c38b224c196fc984aab6b6cc6bdc666f8f4fbcff/langchain/document-loaders.png'>data sources</a>).\n\n
                  <strong>Instructions:</strong> Enter an OpenAI API key and perform LLM use cases (semantic search, sentiment analysis, summarization, translation, etc.)
                  <ul style="list-style-type:square;">
                  <li>Set "Process Video" to "False" and submit prompt "what is gpt-4". The LLM <strong>without</strong> RAG does not know the answer.</li>
                  <li>Set "Process Video" to "True" and submit prompt "what is gpt-4". The LLM <strong>with</strong> RAG knows the answer.</li>
-                 <li>Experiment with different prompts, for example "what is gpt-4, answer in german" or "write a haiku about gpt-4".</li>
+                 <li>Set "Process Video" to "False" and experiment with different prompts, for example "what is gpt-4, answer in german" or "write a haiku about gpt-4".</li>
                  </ul>
                  In a production system processing external data would be done in a batch process, while prompting is done in a user interaction.\n\n
                  <strong>Technology:</strong> <a href='https://www.gradio.app/'>Gradio</a> UI using <a href='https://platform.openai.com/'>OpenAI</a> API