Update app.py

app.py

@@ -111,11 +111,10 @@ def rag_chain(llm, prompt, db):
     completion = rag_chain({"query": prompt})
     return completion["result"]
 
-def evaluate(prompt, completion, rag_option):
+def log_wandb(prompt, completion, rag_option):
     wandb.login(key = wandb_api_key)
     wandb.init(project = "openai-llm-rag", config = config)
     wandb.log({"prompt": prompt, "completion": completion, "rag_option": rag_option})
-    wandb.logout()
 
 def invoke(openai_api_key, rag_option, prompt):
     if (openai_api_key == ""):
@@ -142,7 +141,7 @@ def invoke(openai_api_key, rag_option, prompt):
             completion = llm_chain(llm, prompt)
     except Exception as e:
         raise gr.Error(e)
-    evaluate(prompt, completion, rag_option)
+    log_wandb(prompt, completion, rag_option)
     return completion
 
 description = """<strong>Overview:</strong> Context-aware multimodal reasoning application that demonstrates a <strong>large language model (LLM)</strong> with 
@@ -167,7 +166,7 @@ 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. 
-                 Performance evaluation via <a href='https://wandb.ai/bstraehle'>Weights & Biases</a>."""
+                 RAG evaluation via <a href='https://wandb.ai/bstraehle'>Weights & Biases</a>."""
 
 gr.close_all()
 demo = gr.Interface(fn=invoke,