Update app.py

app.py

@@ -1,6 +1,5 @@
 import streamlit as st
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
+from transformers import pipeline
 
 tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-small-finetuned-quora-for-paraphrasing")
 model = AutoModelForSeq2SeqLM.from_pretrained("mrm8488/t5-small-finetuned-quora-for-paraphrasing")
@@ -14,25 +13,23 @@ right_column.selectbox('Question Generator',  ['T5', 'GPT Neo-X'])
 
 input = st.text_area("Input Text")
 
+def summarize(text):
+  # Refer to https://huggingface.co/docs/transformers/v4.18.0/en/main_classes/pipelines#transformers.SummarizationPipeline
+  # for further information about configuration.
+  summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+  # Refer to https://huggingface.co/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig
+  # for further configuration of of the
+  output: list = summarizer(
+      text,
+      max_length=130,
+      min_length=30,
+      do_sample=False)
+  return output
 
 if st.button('Generate'):
     st.write(input)
+    st.write(summarize(input))
     st.success("We have generated 105 Questions for you")
     st.snow()
 ##else:
     ##nothing here
-
-def paraphrase(text, max_length=128):
-
-    input_ids = tokenizer.encode(text, return_tensors="pt", add_special_tokens=True)
-
-    generated_ids = model.generate(input_ids=input_ids, num_return_sequences=5, num_beams=5, max_length=max_length, no_repeat_ngram_size=2, repetition_penalty=3.5, length_penalty=1.0, early_stopping=True)
-
-    preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]
-
-    return preds
-
-preds = paraphrase("paraphrase: What is the best framework for dealing with a huge text dataset?")
-
-for pred in preds:
-    st.write(pred)