🎨

Signed-off-by: peter szemraj <peterszemraj@gmail.com>

app.py

@@ -75,7 +75,7 @@ def proc_submission(
         msg = f"""
         <div style="background-color: #FFA500; color: white; padding: 20px;">
         <h3>Warning</h3>
-        <p>Input text was truncated to {max_input_length} words. This is about {100*max_input_length/len(input_wc):.2f}% of the submission.</p>
+        <p>Input text was truncated to {max_input_length} words. That's about {100*max_input_length/len(input_wc):.2f}% of the submission.</p>
         </div>
         """
         logging.warning(msg)
@@ -188,7 +188,9 @@ if __name__ == "__main__":
 
     logging.info("Loading summ models")
     with contextlib.redirect_stdout(None):
-        model, tokenizer = load_model_and_tokenizer("pszemraj/pegasus-x-large-book-summary")
+        model, tokenizer = load_model_and_tokenizer(
+            "pszemraj/pegasus-x-large-book-summary"
+        )
         model_sm, tokenizer_sm = load_model_and_tokenizer(
             "pszemraj/long-t5-tglobal-base-16384-book-summary"
         )
@@ -240,9 +242,9 @@ if __name__ == "__main__":
                 )
             with gr.Row():
                 input_text = gr.Textbox(
-                lines=4,
-                label="Input Text (for summarization)",
-                placeholder="Enter text to summarize, the text will be cleaned and truncated on Spaces. Narrative, academic (both papers and lecture transcription), and article text work well. May take a bit to generate depending on the input text :)",
+                    lines=4,
+                    label="Input Text (for summarization)",
+                    placeholder="Enter text to summarize, the text will be cleaned and truncated on Spaces. Narrative, academic (both papers and lecture transcription), and article text work well. May take a bit to generate depending on the input text :)",
                 )
                 with gr.Column(min_width=100, scale=0.5):
                     load_examples_button = gr.Button(

utils.py

@@ -8,6 +8,7 @@ from pathlib import Path
 from natsort import natsorted
 import subprocess
 
+
 def truncate_word_count(text, max_words=512):
     """
     truncate_word_count - a helper function for the gradio module
@@ -40,7 +41,9 @@ def load_examples(src, filetypes=[".txt", ".pdf"]):
     src = Path(src)
     src.mkdir(exist_ok=True)
 
-    pdf_url = "https://www.dropbox.com/s/y92xy7o5qb88yij/all_you_need_is_attention.pdf?dl=1"
+    pdf_url = (
+        "https://www.dropbox.com/s/y92xy7o5qb88yij/all_you_need_is_attention.pdf?dl=1"
+    )
     subprocess.run(["wget", pdf_url, "-O", src / "all_you_need_is_attention.pdf"])
     examples = [f for f in src.iterdir() if f.suffix in filetypes]
     examples = natsorted(examples)