Update app.py

app.py

@@ -29,7 +29,8 @@ from langchain_huggingface import HuggingFaceEndpoint
 from langchain_core.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
 
 #logging.basicConfig(level=logging.DEBUG)
-HUGGINGFACE_API_TOKEN = os.environ.get("HUGGINGFACEHUB_API_TOKEN")
+HUGGINGFACEHUB_API_TOKEN = os.environ.get("HUGGINGFACEHUB_API_TOKEN")
+OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
 #HF_INFERENCE_ENDPOINT = 
 #BOOKING_ID = re.compile(r'\b[A-Z]{6}\d{6}\b')
 
@@ -98,7 +99,7 @@ api_response_prompt = PromptTemplate(
 def setup_multiple_chains():
 
     llm = OpenAI(model='gpt-3.5-turbo-instruct',
-             temperature=0.7)
+             temperature=0.7, openai_api_key=OPENAI_API_KEY)
     
     #llm = HuggingFaceEndpoint(
         #repo_id="google/gemma-2-2b", #"norallm/normistral-7b-warm-instruct", 
@@ -111,9 +112,9 @@ def setup_multiple_chains():
         #typical_p=0.95,
         #temperature=0.7,
         #repetition_penalty=1.03,
-        #huggingfacehub_api_token=HUGGINGFACE_API_TOKEN,
+        #huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
         #task="text-generation"
-    #)
+    )
 
     #llm = HuggingFacePipeline.from_model_id(
     #model_id="normistral-7b-warm-instruct",
@@ -171,6 +172,5 @@ async def handle_message(message: cl.Message):
         
 
     response_key = "output" if "output" in response else "text"
-    logging.debug({response})
     await cl.Message(response.get(response_key, "")).send()
     return message.content