Update app.py

app.py

@@ -18,13 +18,13 @@ model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-xl", device_m
 
 """###Interface"""
 
-def generate(input_text, minimum_length, maximum_length, beam_amount, temperature, repetition_penalty):
+def generate(input_text, minimum_length, maximum_length, temperature, repetition_penalty):
   input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)
   outputs = model.generate(input_ids,
                           min_length=minimum_length,
                           max_new_tokens=maximum_length,
                           length_penalty=1.4,
-                          num_beams=beam_amount,
+                          num_beams=6,
                           no_repeat_ngram_size=3,
                           temperature=temperature,
                           top_k=100,
@@ -36,12 +36,12 @@ def generate(input_text, minimum_length, maximum_length, beam_amount, temperatur
 
 title = "Flan-T5-XL Inference on GRADIO GUI"
 
-def inference(input_text, minimum_length, maximum_length, beam_amount, temperature, repetition_penalty):
-  return generate(input_text, minimum_length, maximum_length, beam_amount, temperature, repetition_penalty)
+def inference(input_text, minimum_length, maximum_length, temperature, repetition_penalty):
+  return generate(input_text, minimum_length, maximum_length, temperature, repetition_penalty)
 
 gr.Interface(
     fn=inference,
-    inputs=[gr.Textbox(lines=4), gr.Slider(0, 300), gr.Slider(100, 2000), gr.Slider(1, 12, step=1), gr.Slider(1, 3, step=0.1)],
+    inputs=[gr.Textbox(lines=4), gr.Slider(0, 300, value=20), gr.Slider(100, 2000, value=1000), gr.Slider(1, 3, step=0.1, value=2.1)],
     outputs=[
         gr.Textbox(lines=2, label="Flan-T5-XL Inference")
     ],