Create app.py

app.py

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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("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'])
+
+@st.cache(show_spinner=False, allow_output_mutation=True)
+def question_model():
+    question_answerer = pipeline("question-answering")
+    return question_answerer
+
+@st.cache(show_spinner=False, allow_output_mutation=True)
+def summarization_model():
+    summarizer = pipeline("summarization")
+    return summarizer
+
+@st.cache(show_spinner=False, allow_output_mutation=True)
+def generation_model():
+    generator = pipeline("text-generation")
+    return generator
+
+@st.cache(show_spinner=False, allow_output_mutation=True)
+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)
+    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":
+        context = st.text_area('Use the example below or input your own text in English (between 1,000 and 10,000 characters)', 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 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"])