Update app.py

app.py

@@ -48,9 +48,8 @@ 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 
-                 <a href='https://github.com/bstraehle/ai-ml-dl/blob/667f92d0a4cf2a35e2861faefe97d1f094cd79e2/langchain/langchain-document-loaders.png'>
-                 structured/unstructured private/public data sources</a>).\n\n
+                 </strong> (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://github.com/bstraehle/ai-ml-dl/blob/c38b224c196fc984aab6b6cc6bdc666f8f4fbcff/langchain/document-loaders.png'>data sources</a>).\n\n
                  <strong>Instructions:</strong> Enter an OpenAI API key, YouTube URL, and prompt to perform semantic search, sentiment analysis, summarization, 
                  translation, etc. To ask multiple questions related to the same video, set "Process Video" to "True" on the first submission 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,