Create app.py

app.py

@@ -0,0 +1,59 @@
+import streamlit as st
+from transformers import T5TokenizerFast, T5ForConditionalGeneration
+import nltk
+import math
+import torch
+
+model_name = "abokbot/t5-end2end-questions-generation"
+
+st.header("Generate questions for short Wikipedia-like articles")
+
+st_model_load = st.text('Loading question generator model...')
+
+@st.cache(allow_output_mutation=True)
+def load_model():
+    print("Loading model...")
+    tokenizer = T5TokenizerFast.from_pretrained("t5-base")
+    model = T5ForConditionalGeneration.from_pretrained(model_name)
+    nltk.download('punkt')
+    print("Model loaded!")
+    return tokenizer, model
+
+tokenizer, model = load_model()
+st.success('Model loaded!')
+st_model_load.text("")
+
+if 'text' not in st.session_state:
+    st.session_state.text = ""
+st_text_area = st.text_area('Text to generate the questions for', value=st.session_state.text, height=500)
+
+def generate_questions():
+    st.session_state.text = st_text_area
+
+    generator_args = {
+        "max_length": 256,
+        "num_beams": 4,
+        "length_penalty": 1.5,
+        "no_repeat_ngram_size": 3,
+        "early_stopping": True,
+    }
+    input_string = "generate questions: " + st_text_area + " </s>"
+    input_ids = tokenizer.encode(input_string, return_tensors="pt")
+    res = model.generate(input_ids, **generator_args)
+    output = tokenizer.batch_decode(res, skip_special_tokens=True)
+    output = [question.strip() + "?" for question in output[0].split("?") if question != ""]
+
+    st.session_state.questions = output
+
+# generate title button
+st_generate_button = st.button('Generate questions', on_click=generate_questions)
+
+# title generation labels
+if 'questions' not in st.session_state:
+    st.session_state.questions = []
+
+if len(st.session_state.questions) > 0:
+    with st.container():
+        st.subheader("Generated questions")
+        for title in st.session_state.questions:
+            st.markdown("__" + title + "__")