Update app.py

app.py

@@ -46,12 +46,12 @@ def invoke(openai_api_key, use_rag, prompt):
                      temperature = 0)
     if (use_rag):
         # Document loading, splitting, and storage
-        loader = GenericLoader(YoutubeAudioLoader([YOUTUBE_URL_01,
-                                                   YOUTUBE_URL_02,
-                                                   YOUTUBE_URL_03,
-                                                   YOUTUBE_URL_04,
-                                                   YOUTUBE_URL_05,
-                                                   YOUTUBE_URL_06], YOUTUBE_DIR), 
+        loader = GenericLoader(YoutubeAudioLoader([YOUTUBE_URL_1,
+                                                   YOUTUBE_URL_2,
+                                                   YOUTUBE_URL_3,
+                                                   YOUTUBE_URL_4,
+                                                   YOUTUBE_URL_5,
+                                                   YOUTUBE_URL_6], YOUTUBE_DIR), 
                                OpenAIWhisperParser())
         docs = loader.load()
         text_splitter = RecursiveCharacterTextSplitter(chunk_overlap = 150,
@@ -75,8 +75,8 @@ 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 
-                 (in this case YouTube videos about GPT-4, but it could be PDFs, URLs, or other <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.)
+                 (in this case YouTube videos, but it could be PDFs, URLs, or other <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.) on YouTube videos about GPT-4.
                  <ul style="list-style-type:square;">
                  <li>Set "Retrieval Augmented Generation" to "<strong>False</strong>" and submit prompt "explain 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 "explain gpt-4". The LLM <strong>with</strong> RAG knows the answer.</li>