Update app.py

app.py

@@ -1,5 +1,5 @@
 import gradio as gr
-import openai, os, random, shutil
+import openai, os, shutil
 
 from langchain.chains import RetrievalQA
 from langchain.chat_models import ChatOpenAI
@@ -14,7 +14,7 @@ from langchain.vectorstores import Chroma
 from dotenv import load_dotenv, find_dotenv
 _ = load_dotenv(find_dotenv())
 
-openai.api_key = "" #os.environ["OPENAI_API_KEY"]
+#openai.api_key = os.environ["OPENAI_API_KEY"]
 
 template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up 
               an answer. Keep the answer as concise as possible. Always say "🔥 Thanks for using the app - Bernd Straehle." at the end of the answer. 
@@ -22,10 +22,8 @@ template = """Use the following pieces of context to answer the question at the
 
 QA_CHAIN_PROMPT = PromptTemplate(input_variables = ["context", "question"], template = template)
 
-rng = str(random.randrange(1000000))
-
-CHROMA_DIR  = "docs/chroma" + rng
-YOUTUBE_DIR = "docs/youtube" + rng
+CHROMA_DIR  = "docs/chroma"
+YOUTUBE_DIR = "docs/youtube"
 
 MODEL_NAME  = "gpt-4"
 
@@ -47,6 +45,7 @@ def invoke(openai_api_key, youtube_url, process_video, prompt):
     qa_chain = RetrievalQA.from_chain_type(llm, retriever = vector_db.as_retriever(search_kwargs = {"k": 3}), return_source_documents = True, chain_type_kwargs = {"prompt": QA_CHAIN_PROMPT})
     result = qa_chain({"query": prompt})
     #print(result)
+    openai.api_key = ""
     return result["result"]
 
 description = """<strong>Overview:</strong> The app demonstrates how to use a Large Language Model (LLM) with Retrieval Augmented Generation (RAG) on external data