Update app.py

app.py

@@ -483,40 +483,16 @@ with gr.Blocks(
     ),
     css=STYLE,
 ) as demo:
-    gr.Markdown(
-        """# <span style='color:var(--primary-500)!important'>Beam Search Visualizer</span>
 
-Play with the parameters below to understand how beam search decoding works!
-
-Here's GPT2 doing beam search decoding for you.
-
-#### <span style='color:var(--primary-500)!important'>Parameters:</span>
-- **Sentence to decode from** (`inputs`): the input sequence to your decoder.
-- **Number of steps** (`max_new_tokens`): the number of tokens to generate.
-- **Number of beams** (`num_beams`): the number of beams to use.
-- **Length penalty** (`length_penalty`): the length penalty to apply to outputs. `length_penalty` > 0.0 promotes longer sequences, while `length_penalty` < 0.0 encourages shorter sequences.
-This parameter will not impact the beam search paths, but only influence the choice of sequences in the end towards longer or shorter sequences.
-- **Number of return sequences** (`num_return_sequences`): the number of sequences to be returned at the end of generation. Should be `<= num_beams`.
-"""
-    )
     text = gr.Textbox(
         label="Sentence to decode from",
-        value="Conclusion: thanks a lot. That's all for today",
+        value="Hugging Face is",
     )
     with gr.Row():
-        n_steps = gr.Slider(
-            label="Number of steps", minimum=1, maximum=12, step=1, value=5
-        )
-        n_beams = gr.Slider(
-            label="Number of beams", minimum=1, maximum=4, step=1, value=4
-        )
-        length_penalty = gr.Slider(
-            label="Length penalty", minimum=-3, maximum=3, step=0.5, value=1
-        )
-        num_return_sequences = gr.Slider(
-            label="Number of return sequences", minimum=1, maximum=4, step=1, value=3
-        )
-
+    n_steps=12
+    n_beams=1
+    length_penalty=1
+    num_return_sequences=3
     n_beams.change(
         fn=change_num_return_sequences, inputs=n_beams, outputs=num_return_sequences
     )