[NT] Use pretrained model for summarization

app.py

@@ -1,7 +1,16 @@
+__all__ = ['learn', 'get_summary', 'intf']
+
+from fastai.vision.all import *
 import gradio as gr
+from blurr.text.data.all import *
+from blurr.text.modeling.all import *
+
+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)
+#learn = load_learner(fname='cnn_model.pkl')
 
 def get_summary(text):
-    return "You summary:\n" + text
+    return hf_model.blurr_summarize(text)
 
 iface = gr.Interface(fn=get_summary, inputs="text", outputs="text")
 iface.launch()

requirements.txt

@@ -0,0 +1,2 @@
+bert_score
+ohmeow-blurr