:lipstick: add runtime, clean up display

app.py

@@ -1,7 +1,7 @@
 import logging
 import re
 from pathlib import Path
-
+import time
 import gradio as gr
 import nltk
 from cleantext import clean
@@ -53,7 +53,7 @@ def proc_submission(
         "early_stopping": True,
         "do_sample": False,
     }
-
+    st = time.perf_counter()
     history = {}
     clean_text = clean(input_text, lower=False)
     max_input_length = 1024 if model_size == "base" else max_input_length
@@ -65,7 +65,6 @@ def proc_submission(
         history["WARNING"] = msg
     else:
         tr_in = input_text
-        history["was_truncated"] = False
 
     _summaries = summarize_via_tokenbatches(
         tr_in,
@@ -84,7 +83,9 @@ def proc_submission(
     history["Summary Scores"] = "\n".join(sum_scores)
     history["Input"] = tr_in
     html = ""
-
+    rt = round((time.perf_counter() - st)/60, 2)
+    print(f"Runtime: {rt} minutes")
+    html += f"<p>Runtime: {rt} minutes on CPU</p>"
     for name, item in history.items():
         html += (
             f"<h2>{name}:</h2><hr><b>{item}</b><br><br>"
@@ -94,6 +95,7 @@ def proc_submission(
 
     html += ""
 
+
     return html
 
 
@@ -104,8 +106,15 @@ if __name__ == "__main__":
     model, tokenizer = load_model_and_tokenizer("pszemraj/led-large-book-summary")
     model_sm, tokenizer_sm = load_model_and_tokenizer("pszemraj/led-base-book-summary")
     title = "Long-Form Summarization: LED & BookSum"
-    description = "A simple demo of how to use a fine-tuned LED model to summarize long-form text. [This model](https://huggingface.co/pszemraj/led-large-book-summary) is a fine-tuned version of [allenai/led-large-16384](https://huggingface.co/allenai/led-large-16384) on the [BookSum dataset](https://arxiv.org/abs/2105.08209). The goal was to create a model that can generalize well and is useful in summarizing lots of text in academic and daily usage."
+    description = """
+    A simple demo of how to use a fine-tuned LED model to summarize long-form text.
+    - [This model](https://huggingface.co/pszemraj/led-large-book-summary) is a fine-tuned version of [allenai/led-large-16384](https://huggingface.co/allenai/led-large-16384) on the [BookSum dataset](https://arxiv.org/abs/2105.08209).
+    - The goal was to create a model that can generalize well and is useful in summarizing lots of text in academic and daily usage.
+    - See [model card](https://huggingface.co/pszemraj/led-large-book-summary) for a notebook with GPU inference (much faster) on Colab.
+
+    ---
 
+    """
     gr.Interface(
         proc_submission,
         inputs=[