details

app.py

@@ -1,10 +1,9 @@
 import logging
-from pathlib import Path
-import os
 import re
+from pathlib import Path
+
 import gradio as gr
 import nltk
-import torch
 from cleantext import clean
 
 from summarize import load_model_and_tokenizer, summarize_via_tokenbatches
@@ -78,8 +77,7 @@ def proc_submission(
     processed = truncate_word_count(clean_text, max_input_length)
     if processed["was_truncated"]:
         tr_in = processed["truncated_text"]
-        history["was_truncated"] = True
-        msg = f"Input text was truncated to {max_input_length} characters."
+        msg = f"Input text was truncated to {max_input_length} words (based on whitespace)"
         logging.warning(msg)
         history["WARNING"] = msg
     else:
@@ -129,9 +127,9 @@ def load_examples(examples_dir="examples"):
 if __name__ == "__main__":
 
     model, tokenizer = load_model_and_tokenizer("pszemraj/led-large-book-summary")
-    title = "Long-form Summarization: LED & BookSum"
+    title = "Long-Form Summarization: LED & BookSum"
     description = (
-        "This is a simple example of using the LED model to summarize a long-form text. This model is a fine-tuned version of [allenai/led-large-16384](https://huggingface.co/allenai/led-large-16384) on the booksum dataset. the goal was to create a model that can generalize well and is useful in summarizing lots of text in academic and daily usage."
+        "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."
     )
 
     gr.Interface(
@@ -162,6 +160,7 @@ if __name__ == "__main__":
         examples_per_page=4,
         title=title,
         description=description,
+        article="The model can be used with tag [pszemraj/led-large-book-summary](https://huggingface.co/pszemraj/led-large-book-summary). See the model card for details on usage & a notebook for a tutorial.",
         examples=load_examples(),
         cache_examples=False,
     ).launch(enable_queue=True, )