Upload app.py

app.py

@@ -105,19 +105,24 @@ def predict(user_input,company):
             )
         }
     ]
-    print(prompt)
+   
     # Create messages
+  try:
     response = client.chat.completions.create(
         model=model_name,
         messages=prompt,
         temperature=0
     )
 
+    prediction = response.choices[0].message.content.strip()
+  except Exception as e:
+    prediction = f'Sorry, I encountered the following error: \n {e}'
+    
+
 
     # Get response from the LLM
     prediction = response.choices[0].message.content.strip()
-    print (prediction)
-
+    
     # While the prediction is made, log both the inputs and outputs to a local log file
     # While writing to the log file, ensure that the commit scheduler is locked to avoid parallel
     # access
@@ -142,7 +147,7 @@ company_input = gr.Radio(
     label = 'company'
     ) 
 
-model_output = gr.Textbox (label = 'Response')
+model_output = gr.Label (label = 'Response')
 
 # Add text box and radio button to the interface
 # The radio button is used to select the company 10k report in which the context needs to be retrieved.
@@ -155,7 +160,7 @@ model_output = gr.Textbox (label = 'Response')
 demo = gr.Interface(
     fn=predict,
     inputs=[user_input,company_input],
-    outputs=model_output,
+    outputs=prediction,
     title="RAG on 10k-reports",
     description="This API allows you to query on annaul reports",
     concurrency_limit=16