Upload app.py

app.py

@@ -115,8 +115,8 @@ def predict(user_input,company):
 
 
     # Get response from the LLM
-    answer = response.choices[0].message.content.strip()
-    print (answer)
+    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
@@ -137,8 +137,12 @@ def predict(user_input,company):
 
 # Set-up the Gradio UI
 user_input = gr.Textbox (label = 'Query')
-company_input = gr.Dropdown(['aws','google','IBM','Meta','msft'], label = 'company') 
-model_response = gr.Textbox (label = 'Response')
+company_input = gr.Dropdown(
+    ['aws','google','IBM','Meta','msft'], 
+    label = 'company'
+) 
+
+model_output = gr.Textbox (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.