Update app.py

app.py

@@ -16,14 +16,10 @@ from langchain.vectorstores import MongoDBAtlasVectorSearch
 
 from pymongo import MongoClient
 
-from wandb.integration.openai import autolog
-
 from dotenv import load_dotenv, find_dotenv
 _ = load_dotenv(find_dotenv())
 
-#openai.api_key = os.environ["OPENAI_API_KEY"]
 WANDB_API_KEY = os.environ["WANDB_API_KEY"]
-
 os.environ["LANGCHAIN_WANDB_TRACING"] = "true"
 os.environ["WANDB_PROJECT"] = "openai-llm-rag"
 
@@ -119,12 +115,6 @@ def rag_chain(llm, prompt, db):
     completion = rag_chain({"query": prompt})
     return completion
 
-def wandb_log(prompt, completion, rag_option):
-    wandb.login(key = wandb_api_key)
-    wandb.init(project = "openai-llm-rag", config = config)
-    wandb.log({"prompt": str(prompt), "completion": str(completion), "rag_option": rag_option})
-    wandb.finish()
-
 def invoke(openai_api_key, rag_option, prompt):
     if (openai_api_key == ""):
         raise gr.Error("OpenAI API Key is required.")
@@ -133,9 +123,6 @@ def invoke(openai_api_key, rag_option, prompt):
     if (prompt == ""):
         raise gr.Error("Prompt is required.")
     completion = ""
-    #wandb.login(key = wandb_api_key)
-    #wandb.init(project = "openai-llm-rag", config = config)
-    #autolog()
     try:
         llm = ChatOpenAI(model_name = config["model"], 
                          openai_api_key = openai_api_key, 
@@ -158,7 +145,6 @@ def invoke(openai_api_key, rag_option, prompt):
         completion = e
         raise gr.Error(e)
     finally:
-        #wandb_log(prompt, completion, rag_option)
         wandb.finish()
     return completion