Super simple approach

app.py

@@ -1,42 +1,18 @@
 __all__ = ['learn', 'get_summary', 'intf']
 
 import gradio as gr
-from blurr.text.data.all import *
-from blurr.text.modeling.all import *
-from datasets import load_dataset
-from fastai.data.all import *
-from fastai.callback.all import *
-from fastai.learner import *
-from fastai.optimizer import *
+import datasets
+import pandas as pd
+from fastai.text.all import *
 from transformers import *
-import nltk
-
-nltk.download('punkt', quiet=True)
 
-pre_trained_model_name = "sshleifer/distilbart-cnn-6-6"
-hf_arch, hf_config, hf_tokenizer, hf_model = BlurrText().get_hf_objects(pre_trained_model_name, model_cls=BartForConditionalGeneration)
-summarization_metrics = {
-    "rouge": {
-        "compute_kwargs": {"rouge_types": ["rouge1", "rouge2", "rougeL", "rougeLsum"], "use_stemmer": True}, "returns": ["rouge1", "rouge2", "rougeL", "rougeLsum"],
-    },
-    "bertscore": {
-        "compute_kwargs": {"lang": "en"}, "returns": ["precision", "recall", "f1"]
-    }
-}
-translation_metrics = {
-    "bleu": {"returns": "bleu"},
-    "meteor": {"returns": "maeteor"},
-    "sacrebleu": {"returns": "score"},
-}
-
-model = BaseModelWrapper(hf_model)
-learn_cbs = [BaseModelCallback]
-fit_cbs = [Seq2SeqMetricsCallback(custom_metrics=summarization_metrics, calc_every="last_epoch")]
+from blurr.text.data.all import *
+from blurr.text.modeling.all import *
 
-learn = load_learner(fname='cnn_model.pkl')
+learn = load_learner(fname='model.pkl')
 
 def get_summary(text, sequences_num):
-    return learn.blurr_summarize(text, num_return_sequences=sequences_num)
+    return learn.blurr_summarize(text, early_stopping=True, num_beams=4, num_return_sequences=sequences_num)[0]
 
 iface = gr.Interface(fn=get_summary, inputs=["text", gr.Number(value=5, label="sequences")], outputs="text")
 iface.launch()

cnn_model.pkl → model.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4e23e9b040ee2022ccb52e2229135eb46b72c0bc2969a9016b1a4c02b6d3ffb2
-size 922705455
+oid sha256:05ff88c36679df1ede20bbd7623c7fb4d0e65ee0e8a5cbedf57979480bfca418
+size 923727853

requirements.txt

@@ -1,2 +1,6 @@
 bert_score
+datasets
+fastai
 ohmeow-blurr
+sacremoses
+transformers