Update app.py

app.py

@@ -120,7 +120,7 @@ def wandb_trace(rag_option, prompt, prompt_template, result, completion, chain_n
     trace = Trace(
         name = chain_name,
         kind = "chain",
-        status_code = "SUCCESS",
+        status_code = "SUCCESS" if (status_msg == "") else "ERROR",
         status_message = status_msg,
         metadata={
             "chunk_overlap": config["chunk_overlap"],
@@ -129,8 +129,8 @@ def wandb_trace(rag_option, prompt, prompt_template, result, completion, chain_n
             "model": config["model"],
             "temperature": config["temperature"],
         },
-        start_time_ms = 123,
-        end_time_ms = 456,
+        #start_time_ms = 123,
+        #end_time_ms = 456,
         inputs = {"rag_option": rag_option, "prompt": prompt, "prompt_template": prompt_template},
         outputs = {"result": str(result), "completion": str(completion)},
     )
@@ -157,7 +157,7 @@ def invoke(openai_api_key, rag_option, prompt):
             completion = rag_chain(llm, prompt, db)
             result = completion["result"]
             prompt_template = rag_template
-            chain_name = RetrievalQA.__class__.__name__
+            chain_name = RetrievalQA.__class__
         elif (rag_option == "MongoDB"):
             #splits = document_loading_splitting()
             #document_storage_mongodb(splits)
@@ -165,12 +165,12 @@ def invoke(openai_api_key, rag_option, prompt):
             completion = rag_chain(llm, prompt, db)
             result = completion["result"]
             prompt_template = rag_template
-            chain_name = RetrievalQA.__class__.__name__
+            chain_name = RetrievalQA.__class__
         else:
             result = llm_chain(llm, prompt)
             completion = result
             prompt_template = llm_template
-            chain_name = LLMChain.__class__.__name__
+            chain_name = LLMChain.__class__
     except Exception as e:
         status_msg = e
         raise gr.Error(e)
@@ -200,7 +200,7 @@ description = """<strong>Overview:</strong> Context-aware multimodal reasoning a
                  <strong>Speech-to-text</strong> via <a href='https://openai.com/research/whisper'>whisper-1</a> model, <strong>text embedding</strong> via 
                  <a href='https://openai.com/blog/new-and-improved-embedding-model'>text-embedding-ada-002</a> model, and <strong>text generation</strong> via 
                  <a href='""" + WEB_URL + """'>gpt-4</a> model. Implementation via AI-first <a href='https://www.langchain.com/'>LangChain</a> toolkit. 
-                 RAG evaluation via <a href='https://wandb.ai/bstraehle/openai-llm-rag/table'>Weights & Biases</a>."""
+                 RAG evaluation via <a href='https://wandb.ai/bstraehle/openai-llm-rag/workspace'>Weights & Biases</a>."""
 
 gr.close_all()
 demo = gr.Interface(fn=invoke,