Update app.py

app.py

@@ -31,7 +31,11 @@ pipe = pipeline(
 def infer(prompt):
     return pipe(prompt, max_new_tokens=1, do_sample=True, temperature=1.0, return_full_text=False)[0]["generated_text"]
 
-default_experiment = 'text'
+default_experiment = """Test
+1
+2
+3
+"""
 
 with gr.Blocks(
     fill_width=True,
@@ -45,19 +49,20 @@ with gr.Blocks(
     # ---------- NEW: info banner ----------
     gr.Markdown(
         """
-        ### About this Space
-        - **Model:** Llama-3.1 Minitaur-8B quantised to 4-bit  
-        - **Speed-up:** Flash-Attention 2 & automatic device-mapping  
-        - **Memory:** Fits on a single consumer GPU (~6 GB VRAM)
+        ### How to prompt:
+        - We did not employ a particular prompt template – just phrase everything in natural language.  
+        - Human choices are encapsulated by "<<" and ">>" tokens.
+        - Most experiments in the training data are framed in terms of button presses. If possible, it is recommended to use that style.
+        - You can find examples in the Supporting Information of our paper.
         """,
         elem_id="info-box",
     )
 
     # (optional) add a logo or hero image
     gr.Image(
-        value="https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.png",
+        value="https://marcelbinz.github.io/imgs/centaur.png",
         show_label=False,
-        height=80,
+        height=100,
         elem_classes="mx-auto",          # centres the image
     )
     # --------------------------------------
@@ -65,7 +70,7 @@ with gr.Blocks(
     with gr.Row(equal_height=True):
         inp = gr.Textbox(
             label="Prompt", elem_id="prompt-box",
-            lines=12, max_lines=12, scale=3, value="text"
+            lines=12, max_lines=12, scale=3, value=default_experiment
         )
         outp = gr.Textbox(
             label="Response", elem_id="answer-box",