Upload app.py

app.py

@@ -108,6 +108,7 @@ def predict(user_input,company):
 
     # Create messages
     try:
+      model_name = "mistralai/Mixtral-8x7B-Instruct-v0.1"
       response = client.chat.completions.create(
           model=model_name,
           messages=prompt,
@@ -122,7 +123,9 @@ def predict(user_input,company):
 
 
     # Get response from the LLM
-    
+
+    model_output = predict(user_input,company)
+
 
     # 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
@@ -148,7 +151,7 @@ company_input = gr.Radio(
     label = 'company'
     )
 
-model_output = gr.Label (label = 'Response')
+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.