Update app.py

app.py

@@ -82,9 +82,10 @@ def invoke(openai_api_key, use_rag, prompt):
     return result
 
 description = """<strong>Overview:</strong> The app demonstrates how to use a Large Language Model (LLM) with Retrieval Augmented Generation (RAG) on external data 
-                 (YouTube videos, PDFs, URLs, or other <a href='https://raw.githubusercontent.com/bstraehle/ai-ml-dl/c38b224c196fc984aab6b6cc6bdc666f8f4fbcff/langchain/document-loaders.png'>data sources</a>).\n\n
+                 (see <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, summarization, translation, etc.) on 
-                 <strong>YouTube videos about GPT-4</strong>, created after its training cutoff.
+                 <strong><a href='""" + YOUTUBE_URL_1 + """'>YouTube videos</a></strong>, <strong><a href='""" + PDF_URL + """'>PDFs</a></strong>, and 
+                 <strong><a href='""" + WEB_URL + """'>URLs</a></strong> about GPT-4, created after its training cutoff.
                  <ul style="list-style-type:square;">
                  <li>Set "Retrieval Augmented Generation" to "<strong>False</strong>" and submit prompt "What is GPT-4?" The LLM <strong>without</strong> RAG does not know the answer.</li>
                  <li>Set "Retrieval Augmented Generation" to "<strong>True</strong>" and submit prompt "What is GPT-4?" The LLM <strong>with</strong> RAG knows the answer.</li>