Update app.py

app.py

@@ -25,21 +25,23 @@ QA_CHAIN_PROMPT = PromptTemplate(input_variables = ["context", "question"], temp
 CHROMA_DIR  = "docs/chroma"
 YOUTUBE_DIR = "docs/youtube"
 
+YOUTUBE_URL = " playlist"
+
 MODEL_NAME  = "gpt-4"
 
-def invoke(openai_api_key, youtube_url, process_video, prompt):
-    if (process_video):
-        if (os.path.isdir(CHROMA_DIR)):
-            shutil.rmtree(CHROMA_DIR)
-        if (os.path.isdir(YOUTUBE_DIR)):
-            shutil.rmtree(YOUTUBE_DIR)
-        loader = GenericLoader(YoutubeAudioLoader([youtube_url], YOUTUBE_DIR), OpenAIWhisperParser())
+def invoke(openai_api_key, use_rag, prompt):
+    if (os.path.isdir(CHROMA_DIR)):
+        shutil.rmtree(CHROMA_DIR)
+    if (os.path.isdir(YOUTUBE_DIR)):
+        shutil.rmtree(YOUTUBE_DIR)
+    if (use_rag):
+        loader = GenericLoader(YoutubeAudioLoader([YOUTUBE_URL], YOUTUBE_DIR), OpenAIWhisperParser())
         docs = loader.load()
         text_splitter = RecursiveCharacterTextSplitter(chunk_size = 1500, chunk_overlap = 150)
         splits = text_splitter.split_documents(docs)
         vector_db = Chroma.from_documents(documents = splits, embedding = OpenAIEmbeddings(), persist_directory = CHROMA_DIR)
-    else:
-        vector_db = Chroma(persist_directory = CHROMA_DIR, embedding_function = OpenAIEmbeddings())
+    #else:
+    #    vector_db = Chroma(persist_directory = CHROMA_DIR, embedding_function = OpenAIEmbeddings())
     llm = ChatOpenAI(model_name = MODEL_NAME, openai_api_key = openai_api_key, temperature = 0)
     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})
@@ -50,14 +52,14 @@ description = """<strong>Overview:</strong> The app demonstrates how to use a La
                  (in this case YouTube videos, but it could be PDFs, URLs, or other structured/unstructured private/public 
                  <a href='https://raw.githubusercontent.com/bstraehle/ai-ml-dl/c38b224c196fc984aab6b6cc6bdc666f8f4fbcff/langchain/document-loaders.png'>data sources</a>).\n\n
                  <strong>Instructions:</strong> Enter an OpenAI API key and perform LLM use cases on a YouTube video (semantic search, sentiment analysis, summarization, 
-                 translation, etc.) The example is a short video about GPT-4.
+                 translation, etc.) The example is a <a href='c'>short video about GPT-4</a>.
                  <ul style="list-style-type:square;">
                  <li>Set "Process Video" to "False" and submit prompt "what is gpt-4". The LLM <strong>without</strong> RAG does not know the answer.</li>
                  <li>Set "Process Video" to "True" and submit prompt "what is gpt-4". The LLM <strong>with</strong> RAG knows the answer.</li>
                  <li>Set "Process Video" to "False" and experiment with different prompts, for example "what is gpt-4, answer in german" or "write a haiku about gpt-4".</li>
                  </ul>
                  In a production system processing external data would be done in a batch process. An idea for a production system would be to perform LLM use cases on the 
-                 <a href='https://www.youtube.com/playlist?list=PL2yQDdvlhXf_hIzmfHCdbcXj2hS52oP9r'>AWS re:Invent</a> playlist.\n\n
+                 <a href='https://www.youtube.com/playlist?list=PL2yQDdvlhXf_hIzmfHCdbcXj2hS52oP9r'>AWS re:Invent playlist</a>.\n\n
                  <strong>Technology:</strong> <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) foundation models as well as AI-native <a href='https://www.trychroma.com/'>Chroma</a> 
@@ -65,7 +67,7 @@ description = """<strong>Overview:</strong> The app demonstrates how to use a La
 
 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 = False), gr.Textbox(label = "Prompt", value = "what is gpt-4", lines = 1)],
+                    inputs = [gr.Textbox(label = "OpenAI API Key", value = "sk-", lines = 1), gr.Radio([True, False], label="Use RAG", value = False), 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)