Update app.py

app.py

@@ -22,9 +22,12 @@ template = """Use the following pieces of context to answer the question at the
 
 QA_CHAIN_PROMPT = PromptTemplate(input_variables = ["context", "question"], template = template)
 
+print(0)
+
 def invoke(openai_api_key, youtube_url, prompt):
     openai.api_key = openai_api_key
     if (os.path.isdir("docs/chroma/") == False):
+        print(1)
         youtube_dir = "docs/youtube/"
         loader = GenericLoader(YoutubeAudioLoader([youtube_url], youtube_dir), OpenAIWhisperParser())
         docs = loader.load()
@@ -34,13 +37,14 @@ def invoke(openai_api_key, youtube_url, prompt):
         vectordb = Chroma.from_documents(documents = splits, embedding = OpenAIEmbeddings(), persist_directory = chroma_dir)
         llm = ChatOpenAI(model_name = "gpt-4", temperature = 0)
         qa_chain = RetrievalQA.from_chain_type(llm, retriever = vectordb.as_retriever(), return_source_documents = True, chain_type_kwargs = {"prompt": QA_CHAIN_PROMPT})
+    print(2)
     result = qa_chain({"query": prompt})
     shutil.rmtree(youtube_dir)
     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, and/or translation, etc.\n\n
+                 Enter an OpenAI API key, YouTube URL (external data), and prompt to perform semantic search, sentiment analysis, summarization, translation, etc.\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