Update app.py

app.py

@@ -16,16 +16,12 @@ tokenizer = AutoTokenizer.from_pretrained("tareknaous/bert2bert-empathetic-respo
 model = EncoderDecoderModel.from_pretrained("tareknaous/bert2bert-empathetic-response-msa")
 model.eval()
 
-def generate_response(text, minimum_length, k, p, temperature):
+def generate_response(text):
   text_clean = arabert_prep.preprocess(text)
   inputs = tokenizer.encode_plus(text_clean,return_tensors='pt')
   outputs = model.generate(input_ids = inputs.input_ids,
                    attention_mask = inputs.attention_mask,
-                   do_sample = True,
-                   min_length=minimum_length,
-                   top_k = k,
-                   top_p = p,
-                   temperature = temperature)
+                   do_sample = True)
   preds = tokenizer.batch_decode(outputs) 
   response = str(preds)
   response = response.replace("\'", '')
@@ -39,10 +35,6 @@ description = 'This demo is for a BERT2BERT model trained for single-turn open-d
 gr.Interface(fn=generate_response,
               inputs=[
           gr.inputs.Textbox(),
-          gr.inputs.Slider(5, 20, step=1, label='Minimum Output Length'),
-          gr.inputs.Slider(0, 1000, step=10, label='Top-K'),
-          gr.inputs.Slider(0, 1, step=0.1, label='Top-P'),
-          gr.inputs.Slider(0, 3, step=0.1, label='Temperature'),
           ],
              outputs="text",
              title=title,