Update app.py

app.py

@@ -23,18 +23,18 @@ template = """Use the following pieces of context to answer the question at the
 QA_CHAIN_PROMPT = PromptTemplate(input_variables = ["context", "question"], template = template)
 
 CHROMA_DIR  = "docs/chroma/"
-YOUTUBE_DIR = "docs/youtube/" 
+YOUTUBE_DIR = "docs/youtube/"
 
-MODEL_NAME = "gpt-4"
+MODEL_NAME  = "gpt-4"
 
 def invoke(openai_api_key, youtube_url, process_video, prompt):
     openai.api_key = openai_api_key
     if (process_video):
         if (os.path.isdir(CHROMA_DIR)):
-            os.listdir(CHROMA_DIR)
+            print(os.listdir(CHROMA_DIR))
             shutil.rmtree(CHROMA_DIR)
         if (os.path.isdir(YOUTUBE_DIR)):
-            os.listdir(YOUTUBE_DIR)
+            print(os.listdir(YOUTUBE_DIR))
             shutil.rmtree(YOUTUBE_DIR)
         loader = GenericLoader(YoutubeAudioLoader([youtube_url], YOUTUBE_DIR), OpenAIWhisperParser())
         docs = loader.load()
@@ -43,9 +43,9 @@ def invoke(openai_api_key, youtube_url, process_video, prompt):
         vector_db = Chroma.from_documents(documents = splits, embedding = OpenAIEmbeddings(), persist_directory = CHROMA_DIR)
     else:
         if (os.path.isdir(CHROMA_DIR)):
-            os.listdir(CHROMA_DIR)
+            print(os.listdir(CHROMA_DIR))
         if (os.path.isdir(YOUTUBE_DIR)):
-            os.listdir(YOUTUBE_DIR)
+            print(os.listdir(YOUTUBE_DIR))
         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})