Create app.py

app.py

@@ -0,0 +1,38 @@
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+
+tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-small-finetuned-quora-for-paraphrasing")
+model = AutoModelForSeq2SeqLM.from_pretrained("mrm8488/t5-small-finetuned-quora-for-paraphrasing")
+
+
+st.title('Question Generator by Eddevs')
+
+left_column, right_column = st.columns(2)
+left_column.selectbox('Type',  ['Question Generator', 'Paraphrasing'])
+right_column.selectbox('Question Generator',  ['T5', 'GPT Neo-X'])
+
+input = st.text_area("Input Text")
+
+
+if st.button('Generate'):
+    st.write(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)