import streamlit as st from transformers import pipeline st.set_page_config(page_title="Common NLP Tasks") st.title("Common NLP Tasks") st.subheader(":point_left: Use the menu on the left to select a NLP task (click on > if closed).") """ [![](https://img.shields.io/github/followers/OOlajide?label=OOlajide&style=social)](https://gitHub.com/OOlajide) [![](https://img.shields.io/twitter/follow/sageOlamide?label=@sageOlamide&style=social)](https://twitter.com/sageOlamide) """ expander = st.sidebar.expander("About") expander.write("This web app allows you to perform common Natural Language Processing tasks, select a task below to get started.") st.sidebar.header("What will you like to do?") option = st.sidebar.radio("", ["Text summarization", "Extractive question answering", "Text generation"]) @st.cache(show_spinner=False, allow_output_mutation=True) def question_model(): model_name = "deepset/tinyroberta-squad2" question_answerer = pipeline(model=model_name, tokenizer=model_name, task="question-answering") return question_answerer @st.cache(show_spinner=False, allow_output_mutation=True) def summarization_model(): model_name = "google/pegasus-xsum" summarizer = pipeline(model=model_name, tokenizer=model_name, task="summarization") return summarizer @st.cache(show_spinner=False, allow_output_mutation=True) def generation_model(): model_name = "distilgpt2" generator = pipeline(model=model_name, tokenizer=model_name, task="text-generation") return generator if option == "Extractive question answering": st.markdown("

Extractive Question Answering

", unsafe_allow_html=True) st.markdown("

What is extractive question answering about?

", unsafe_allow_html=True) st.write("Extractive question answering is a Natural Language Processing task where text is provided for a model so that the model can refer to it and make predictions about where the answer to a question is.") st.markdown('___') source = st.radio("How would you like to start? Choose an option below", ["I want to input some text", "I want to upload a file"]) sample_question = "What did the shepherd boy do to amuse himself?" if source == "I want to input some text": with open("sample.txt", "r") as text_file: sample_text = text_file.read() context = st.text_area("Use the example below or input your own text in English (10,000 characters max)", value=sample_text, max_chars=10000, height=330) question = st.text_input(label="Use the question below or enter your own question", value=sample_question) button = st.button("Get answer") if button: with st.spinner(text="Loading question model..."): question_answerer = question_model() with st.spinner(text="Getting answer..."): answer = question_answerer(context=context, question=question) answer = answer["answer"] st.text(answer) elif source == "I want to upload a file": uploaded_file = st.file_uploader("Choose a .txt file to upload", type=["txt"]) if uploaded_file is not None: raw_text = str(uploaded_file.read(),"utf-8") context = st.text_area("", value=raw_text, height=330) question = st.text_input(label="Enter your question", value=sample_question) button = st.button("Get answer") if button: with st.spinner(text="Loading summarization model..."): question_answerer = question_model() with st.spinner(text="Getting answer..."): answer = question_answerer(context=context, question=question) answer = answer["answer"] st.text(answer) elif option == "Text summarization": st.markdown("

Text Summarization

", unsafe_allow_html=True) st.markdown("

What is text summarization about?

", unsafe_allow_html=True) st.write("Text summarization is producing a shorter version of a given text while preserving its important information.") st.markdown('___') source = st.radio("How would you like to start? Choose an option below", ["I want to input some text", "I want to upload a file"]) if source == "I want to input some text": with open("sample.txt", "r") as text_file: sample_text = text_file.read() text = st.text_area("Input a text in English (10,000 characters max) or use the example below", value=sample_text, max_chars=10000, height=330) button = st.button("Get summary") if button: with st.spinner(text="Loading summarization model..."): summarizer = summarization_model() with st.spinner(text="Summarizing text..."): summary = summarizer(text, max_length=130, min_length=30) st.text(summary[0]["summary_text"]) elif source == "I want to upload a file": uploaded_file = st.file_uploader("Choose a .txt file to upload", type=["txt"]) if uploaded_file is not None: raw_text = str(uploaded_file.read(),"utf-8") text = st.text_area("", value=raw_text, height=330) button = st.button("Get summary") if button: with st.spinner(text="Loading summarization model..."): summarizer = summarization_model() with st.spinner(text="Summarizing text..."): summary = summarizer(text, max_length=130, min_length=30) st.text(summary[0]["summary_text"]) elif option == "Text generation": st.markdown("

Text Generation

", unsafe_allow_html=True) st.markdown("

What is text generation about?

", unsafe_allow_html=True) st.write("Text generation is the task of generating text with the goal of appearing indistinguishable to human-written text.") st.markdown('___') text = st.text_input(label="Enter one line of text and let the NLP model generate the rest for you") button = st.button("Generate text") if button: with st.spinner(text="Loading text generation model..."): generator = generation_model() with st.spinner(text="Generating text..."): generated_text = generator(text, max_length=50) st.text(generated_text[0]["generated_text"])