use LukasStankevicius/t5-base-lithuanian-news-summaries-175 model for aggregation

app.py

@@ -62,6 +62,7 @@ nltk.download("popular", force=True, quiet=True)
 
 # Constants & Globals
 MODEL_OPTIONS = [
+    "LukasStankevicius/t5-base-lithuanian-news-summaries-175"
     "pszemraj/long-t5-tglobal-base-16384-book-summary",
     "pszemraj/long-t5-tglobal-base-sci-simplify",
     "pszemraj/long-t5-tglobal-base-sci-simplify-elife",
@@ -78,12 +79,12 @@ TOKEN_BATCH_OPTIONS = [
 ]  # token batch sizes users can choose from
 
 SUMMARY_PLACEHOLDER = "<p><em>Output will appear below:</em></p>"
-AGGREGATE_MODEL = "MBZUAI/LaMini-Flan-T5-783M"  # model to use for aggregation
+AGGREGATE_MODEL = "LukasStankevicius/t5-base-lithuanian-news-summaries-175"  # model to use for aggregation
 
 # if duplicating space: uncomment this line to adjust the max words
-# os.environ["APP_MAX_WORDS"] = str(2048)  # set the max words to 2048
-# os.environ["APP_OCR_MAX_PAGES"] = str(40)  # set the max pages to 40
-# os.environ["APP_AGG_FORCE_CPU"] = str(1)  # force cpu for aggregation
+os.environ["APP_MAX_WORDS"] = str(2048)  # set the max words to 2048
+os.environ["APP_OCR_MAX_PAGES"] = str(40)  # set the max pages to 40
+os.environ["APP_AGG_FORCE_CPU"] = str(1)  # force cpu for aggregation
 
 aggregator = BatchAggregator(
     AGGREGATE_MODEL, force_cpu=os.environ.get("APP_AGG_FORCE_CPU", False)