:art: apply formatting

app.py

@@ -128,9 +128,7 @@ if __name__ == "__main__":
 
     model, tokenizer = load_model_and_tokenizer("pszemraj/led-large-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."
 
     gr.Interface(
         proc_submission,
@@ -140,7 +138,11 @@ if __name__ == "__main__":
                 minimum=1, maximum=6, label="num_beams", default=4, step=1
             ),
             gr.inputs.Slider(
-                minimum=512, maximum=2048, label="token_batch_length", default=1024, step=512,
+                minimum=512,
+                maximum=2048,
+                label="token_batch_length",
+                default=1024,
+                step=512,
             ),
             gr.inputs.Slider(
                 minimum=0.5, maximum=1.1, label="length_penalty", default=0.7, step=0.05
@@ -163,4 +165,6 @@ if __name__ == "__main__":
         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, )
+    ).launch(
+        enable_queue=True,
+    )

summarize.py

@@ -93,7 +93,9 @@ def summarize_via_tokenbatches(
     if batch_length < 512:
         batch_length = 512
         print("WARNING: batch_length was set to 512")
-    print(f"input parameters: {kwargs}, batch_length={batch_length}, batch_stride={batch_stride}")
+    print(
+        f"input parameters: {kwargs}, batch_length={batch_length}, batch_stride={batch_stride}"
+    )
     encoded_input = tokenizer(
         input_text,
         padding="max_length",