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"])
@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"])