Update app.py

app.py

@@ -30,7 +30,6 @@ MODEL_NAME = "gpt-4"
 def invoke(openai_api_key, youtube_url, process_video, prompt):
     openai.api_key = openai_api_key
     if (process_video):
-        print(111)
         loader = GenericLoader(YoutubeAudioLoader([youtube_url], YOUTUBE_DIR), OpenAIWhisperParser())
         docs = loader.load()
         shutil.rmtree(YOUTUBE_DIR)
@@ -38,16 +37,16 @@ def invoke(openai_api_key, youtube_url, process_video, prompt):
         splits = text_splitter.split_documents(docs)
         vector_db = Chroma.from_documents(documents = splits, embedding = OpenAIEmbeddings(), persist_directory = CHROMA_DIR)
     else:
-        print(222)
         vector_db = Chroma(persist_directory = CHROMA_DIR, embedding_function = OpenAIEmbeddings())
     llm = ChatOpenAI(model_name = MODEL_NAME, temperature = 0)
     qa_chain = RetrievalQA.from_chain_type(llm, retriever = vector_db.as_retriever(), return_source_documents = True, chain_type_kwargs = {"prompt": QA_CHAIN_PROMPT})
     result = qa_chain({"query": prompt})
-    #shutil.rmtree(CHROMA_DIR)
     return result["result"]
 
-description = """The app demonstrates how to use a <strong>Large Language Model</strong> (LLM) with <strong>Retrieval Augmented Generation</strong> (RAG) on external data. 
-                 Enter an OpenAI API key, YouTube URL (external data), and prompt to perform semantic search, sentiment analysis, summarization, translation, etc.\n\n
+description = """The app demonstrates how to use a <strong>Large Language Model</strong> (LLM) with <strong>Retrieval Augmented Generation</strong> (RAG) on external data.\n\n
+                 Usage: Enter an OpenAI API key, YouTube URL (external data), 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 question related to the same video, typically set it to "True" 
+                 the first time, and then set it to "False". Note that persistence is not guaranteed in the Hugging Face free tier (I plan to migrate to AWS S3).\n\n
                  Implementation: <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 use cases) foundation models as well as AI-native