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| import torch | |
| import streamlit as st | |
| from transformers import pipeline | |
| st.set_page_config(page_title="Common NLP Tasks") | |
| st.title("Common NLP Tasks") | |
| st.subheader("Use the menu on the left to select a NLP task to do (click on > if closed).") | |
| 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('', ['Extractive question answering', 'Text summarization', 'Text generation', 'Sentiment analysis']) | |
| def question_model(): | |
| model_name = "deepset/roberta-base-squad2" | |
| question_answerer = pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="question-answering") | |
| return question_answerer | |
| def summarization_model(): | |
| summarizer = pipeline("summarization") | |
| return summarizer | |
| def generation_model(): | |
| generator = pipeline("text-generation") | |
| return generator | |
| def sentiment_model(): | |
| sentiment_analysis = pipeline("sentiment-analysis") | |
| return sentiment_analysis | |
| if option == 'Extractive question answering': | |
| st.markdown("<h2 style='text-align: center; color:red;'>Extract answer from text</h2>", unsafe_allow_html=True) | |
| with open(sample.txt, "r") as text_file: | |
| sample_text = text_file.read() | |
| 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": | |
| 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='Enter your question') | |
| button = st.button('Get answer') | |
| if button: | |
| question_answerer = question_model() | |
| with st.spinner(text="Getting answer..."): | |
| answer = question_answerer(context=context, question=question) | |
| st.write(answer["answer"]) | |
| elif source == "I want to upload a file": | |
| uploaded_file = st.file_uploader("Choose a .txt file to upload", type=["txt"]) | |
| question = st.text_input(label='Enter your question') | |
| button = st.button('Get answer') | |
| if button: | |
| question_answerer = question_model() | |
| with st.spinner(text="Getting answer..."): | |
| answer = question_answerer(context=context, question=question) | |
| st.write(answer["answer"]) | |
| elif option == 'Text summarization': | |
| st.markdown("<h2 style='text-align: center; color:red;'>Summarize text</h2>", unsafe_allow_html=True) | |
| sample_text = "sample text" | |
| 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": | |
| text = st.text_area('Input a text in English (between 1,000 and 10,000 characters)', value=sample_text, max_chars=10000, height=330) | |
| button = st.button('Get summary') | |
| if button: | |
| summarizer = summarization_model() | |
| with st.spinner(text="Summarizing text..."): | |
| summary = summarizer(text, max_length=130, min_length=30) | |
| st.write(summary) | |
| elif source == "I want to upload a file": | |
| uploaded_file = st.file_uploader("Choose a .txt file to upload", type=["txt"]) | |
| button = st.button('Get summary') | |
| if button: | |
| summarizer = summarization_model() | |
| with st.spinner(text="Summarizing text..."): | |
| summary = summarizer(text, max_length=130, min_length=30) | |
| st.write(summary) | |
| elif option == 'Text generation': | |
| st.markdown("<h2 style='text-align: center; color:grey;'>Generate text</h2>", unsafe_allow_html=True) | |
| 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: | |
| generator = generation_model() | |
| with st.spinner(text="Generating text..."): | |
| generated_text = generator(text, max_length=50) | |
| st.write(generated_text[0]["generated_text"]) | |
| elif option == 'Sentiment analysis': | |
| st.markdown("<h2 style='text-align: center; color:grey;'>Classify review</h2>", unsafe_allow_html=True) | |
| text = st.text_input(label='Enter a sentence to get its sentiment analysis') | |
| button = st.button('Get sentiment analysis') | |
| if button: | |
| sentiment_analysis = sentiment_model() | |
| with st.spinner(text="Getting sentiment analysis..."): | |
| sentiment = sentiment_analysis(text) | |
| st.write(sentiment[0]["label"]) |