Update app.py

app.py

@@ -39,7 +39,7 @@ def invoke(openai_api_key, youtube_url, process_video, prompt):
         splits = text_splitter.split_documents(docs)
         vectordb = Chroma.from_documents(documents = splits, embedding = OpenAIEmbeddings(), persist_directory = CHROMA_DIR)
         llm = ChatOpenAI(model_name = MODEL_NAME, temperature = 0)
-        QA_CHAIN = RetrievalQA.from_chain_type(llm, retriever = vectordb.as_retriever(), return_source_documents = True, chain_type_kwargs = {"prompt": QA_CHAIN_PROMPT})
+        global QA_CHAIN = RetrievalQA.from_chain_type(llm, retriever = vectordb.as_retriever(), return_source_documents = True, chain_type_kwargs = {"prompt": QA_CHAIN_PROMPT})
     else:
         print(222)
     result = QA_CHAIN({"query": prompt})
@@ -56,7 +56,7 @@ description = """The app demonstrates how to use a <strong>Large Language Model<
 
 gr.close_all()
 demo = gr.Interface(fn=invoke, 
-                    inputs = [gr.Textbox(label = "OpenAI API Key", value = "sk-", lines = 1), gr.Textbox(label = "YouTube URL", value = "https://www.youtube.com/watch?v=--khbXchTeE", lines = 1), gr.Radio([True, False], label="Process Video", value = True), gr.Textbox(label = "Prompt", value = "GPT-4 human level performance", lines = 1)],
+                    inputs = [gr.Textbox(label = "OpenAI API Key", value = "sk-", lines = 1), gr.Textbox(label = "YouTube URL", value = "https://www.youtube.com/watch?v=--khbXchTeE", lines = 1), gr.Radio([True, False], label="Process Video", value = True), gr.Textbox(label = "Prompt", value = "What is GPT-4", lines = 1)],
                     outputs = [gr.Textbox(label = "Completion", lines = 1)],
                     title = "Generative AI - LLM & RAG",
                     description = description)