Update app.py

app.py

@@ -50,9 +50,9 @@ def invoke(openai_api_key, youtube_url, process_video, prompt):
 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, etc.)\n\n
                  <strong>Instructions:</strong> Enter an OpenAI API key, YouTube URL, and prompt to perform semantic search, sentiment analysis, summarization, 
-                 translation, etc. "Process Video" specifies whether or not to perform speech-to-text processing. To ask multiple questions related to the same video, 
-                 typically set it to "True" the first run and then to "False". The example is a 3:12 min. video about GPT-4 and takes less than 30 sec. to process. 
-                 Experiment with different prompts, for example "what is gpt-4, answer in german" or "write a haiku about gpt-4".\n\n
+                 translation, etc. To ask multiple questions related to the same video, set "Process Video" to "True" on the first run and then to "False". 
+                 The example is a 3:12 min. video about GPT-4 and takes less than 30 sec. to process. Experiment with different prompts, for example "what is gpt-4, 
+                 answer in german" or "write a haiku about gpt-4".\n\n
                  <strong>Technology:</strong> <a href='https://www.gradio.app/'>Gradio</a> UI using <a href='https://platform.openai.com/'>OpenAI</a> API 
                  via AI-first <a href='https://www.langchain.com/'>LangChain</a> toolkit with <a href='https://openai.com/research/whisper'>Whisper</a> (speech-to-text) 
                  and <a href='https://openai.com/research/gpt-4'>GPT-4</a> (LLM) foundation models as well as AI-native