Update app.py

app.py

@@ -137,14 +137,12 @@ def invoke(openai_api_key, rag_option, prompt):
             db = document_retrieval_chroma(llm, prompt)
             completion = rag_chain(llm, prompt, db)
             result = completion["result"]
-            completion = completion.toJSON()
         elif (rag_option == "MongoDB"):
             #splits = document_loading_splitting()
             #document_storage_mongodb(splits)
             db = document_retrieval_mongodb(llm, prompt)
             completion = rag_chain(llm, prompt, db)
             result = completion["result"]
-            completion = completion.toJSON()
         else:
             result = llm_chain(llm, prompt)
             completion = result
@@ -155,7 +153,7 @@ def invoke(openai_api_key, rag_option, prompt):
 
 description = """<strong>Overview:</strong> Context-aware multimodal reasoning application that demonstrates a <strong>large language model (LLM)</strong> with 
                  <strong>retrieval augmented generation (RAG)</strong>. 
-                 See <a href='https://raw.githubusercontent.com/bstraehle/ai-ml-dl/main/hugging-face/openai-llm-rag.png'>architecture diagram</a> on GitHub.\n\n
+                 See <a href='https://huggingface.co/spaces/bstraehle/openai-llm-rag/blob/main/openai-llm-rag.png'>architecture diagram</a>.\n\n
                  <strong>Instructions:</strong> Enter an OpenAI API key and perform text generation use cases on <a href='""" + YOUTUBE_URL_1 + """'>YouTube</a>, 
                  <a href='""" + PDF_URL + """'>PDF</a>, and <a href='""" + WEB_URL + """'>web</a> data published after LLM knowledge cutoff (example: GPT-4 data).
                  <ul style="list-style-type:square;">