updates-v2

app.py

@@ -26,7 +26,6 @@ logging.basicConfig(
 
 MODEL_OPTIONS = [
     "pszemraj/led-large-book-summary",
-    "pszemraj/led-large-book-summary-continued",
     "pszemraj/led-base-book-summary",
 ]
 
@@ -341,7 +340,7 @@ if __name__ == "__main__":
                 "- 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 Colab notebook for a tutorial."
             )
             gr.Markdown(
-                "- **Update May 1, 2023:** Enabled faster inference times via `use_cache=True`, the number of words the model will processed has been increased! New [test model](https://huggingface.co/pszemraj/led-large-book-summary-continued) as an extension of `led-large-book-summary`."
+                "- **Update May 1, 2023:** Enabled faster inference times via `use_cache=True`, the number of words the model will processed has been increased! Not on this demo, but there is a [test model](https://huggingface.co/pszemraj/led-large-book-summary-continued) available: an extension of `led-large-book-summary`."
             )
             gr.Markdown("---")
 

summarize.py

@@ -127,7 +127,7 @@ def summarize_via_tokenbatches(
     in_id_arr, att_arr = encoded_input.input_ids, encoded_input.attention_mask
     gen_summaries = []
 
-    pbar = tqdm(total=len(in_id_arr))
+    pbar = tqdm(total=len(in_id_arr), desc="Summarizing")
 
     for _id, _mask in zip(in_id_arr, att_arr):
         result, score = summarize_and_score(
@@ -144,9 +144,9 @@ def summarize_via_tokenbatches(
             "summary_score": score,
         }
         gen_summaries.append(_sum)
-        print(f"\t{result[0]}\nScore:\t{score}")
+        logger.info(f"SCore {score} for summary:\n\t{result}")
         pbar.update()
 
     pbar.close()
-
+    logger.debug(f"Generated summaries:\n{pp.pformat(gen_summaries)}")
     return gen_summaries