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| 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://gitHub.com/OOlajide) | |
| [](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"]) | |
| def question_model(): | |
| model_name = "deepset/tinyroberta-squad2" | |
| question_answerer = pipeline(model=model_name, tokenizer=model_name, task="question-answering") | |
| return question_answerer | |
| def summarization_model(): | |
| model_name = "google/pegasus-xsum" | |
| summarizer = pipeline(model=model_name, tokenizer=model_name, task="summarization") | |
| return summarizer | |
| 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("<h2 style='text-align: center; color:grey;'>Extractive Question Answering</h2>", unsafe_allow_html=True) | |
| st.markdown("<h3 style='text-align: left; color:#F63366; font-size:18px;'><b>What is extractive question answering about?<b></h3>", 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("<h2 style='text-align: center; color:grey;'>Text Summarization</h2>", unsafe_allow_html=True) | |
| st.markdown("<h3 style='text-align: left; color:#F63366; font-size:18px;'><b>What is text summarization about?<b></h3>", 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("<h2 style='text-align: center; color:grey;'>Text Generation</h2>", unsafe_allow_html=True) | |
| st.markdown("<h3 style='text-align: left; color:#F63366; font-size:18px;'><b>What is text generation about?<b></h3>", 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"]) |