Create app.py

app.py

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+import streamlit as st
+import torch
+from transformers import pipeline
+import spacy
+from spacy import displacy
+import plotly.express as px
+import numpy as np
+
+st.set_page_config(page_title="NIU NLP Prototype")
+
+st.title("Natural Language Processing Prototype")
+st.write("_This web application is intended for educational use, please do not upload any classified, proprietary, or sensitive information._")
+st.subheader("__Which natural language processing task would you like to try?__")
+st.write("- __Sentiment Analysis:__ Identifying whether a piece of text has a positive or negative sentiment.")
+st.write("- __Named Entity Recognition:__ Identifying all geopolitical entities, organizations, people, locations, or dates in a body of text.")
+st.write("- __Text Classification:__ Placing a piece of text into one or more categories.")
+st.write("- __Text Summarization:__ Condensing larger bodies of text into smaller bodies of text.")
+
+option = st.selectbox('Please select from the list',('','Sentiment Analysis','Named Entity Recognition', 'Text Classification','Text Summarization'))
+
+@st.cache(allow_output_mutation=True, show_spinner=False)
+def Loading_Model_1():
+    sum2 = pipeline("summarization",framework="pt")
+    return sum2
+
+@st.cache(allow_output_mutation=True, show_spinner=False)
+def Loading_Model_2():
+    class1 = pipeline("zero-shot-classification",framework="pt")
+    return class1
+
+@st.cache(allow_output_mutation=True, show_spinner=False)
+def Loading_Model_3():
+    sentiment = pipeline("sentiment-analysis", framework="pt")
+    return sentiment
+
+@st.cache(allow_output_mutation=True, show_spinner=False)
+def Loading_Model_4():
+    nlp = spacy.load('en_core_web_sm')
+    return nlp
+
+@st.cache(allow_output_mutation=True)
+def entRecognizer(entDict, typeEnt):
+    entList = [ent for ent in entDict if entDict[ent] == typeEnt]
+    return entList
+
+def plot_result(top_topics, scores):
+    top_topics = np.array(top_topics)
+    scores = np.array(scores)
+    scores *= 100
+    fig = px.bar(x=scores, y=top_topics, orientation='h', 
+                 labels={'x': 'Probability', 'y': 'Category'},
+                 text=scores,
+                 range_x=(0,115),
+                 title='Top Predictions',
+                 color=np.linspace(0,1,len(scores)),
+                 color_continuous_scale="Bluered")
+    fig.update(layout_coloraxis_showscale=False)
+    fig.update_traces(texttemplate='%{text:0.1f}%', textposition='outside')
+    st.plotly_chart(fig)    
+
+with st.spinner(text="Please wait for the models to load. This should take approximately 60 seconds."):
+    sum2 = Loading_Model_1()
+    class1 = Loading_Model_2()
+    sentiment = Loading_Model_3()
+    nlp = Loading_Model_4()
+
+if option == 'Text Classification':
+    cat1 = st.text_input('Enter each possible category name (separated by a comma). Maximum 5 categories.')
+    text = st.text_area('Enter Text Below:', height=200)
+    #uploaded_file = st.file_uploader("Choose a file", type=['txt'])
+    submit = st.button('Generate')
+    if submit:
+        st.subheader("Classification Results:")
+        labels1 = cat1.strip().split(',')
+        result = class1(text, candidate_labels=labels1)
+        cat1name = result['labels'][0]
+        cat1prob = result['scores'][0]
+        st.write('Category: {} | Probability: {:.1f}%'.format(cat1name,(cat1prob*100)))
+        plot_result(result['labels'][::-1][-10:], result['scores'][::-1][-10:])
+        
+if option == 'Text Summarization':
+    max_lengthy = st.slider('Maximum summary length (words)', min_value=30, max_value=150, value=60, step=10)
+    num_beamer = st.slider('Speed vs quality of summary (1 is fastest)', min_value=1, max_value=8, value=4, step=1)
+    text = st.text_area('Enter Text Below (maximum 800 words):', height=300) 
+    #uploaded_file = st.file_uploader("Choose a file", type=['txt'])
+    submit = st.button('Generate')  
+    if submit:
+        st.subheader("Summary:")
+        with st.spinner(text="This may take a moment..."):
+            summWords = sum2(text, max_length=max_lengthy, min_length=15, num_beams=num_beamer, do_sample=True, early_stopping=True, repetition_penalty=1.5, length_penalty=1.5)
+        text2 =summWords[0]["summary_text"] #re.sub(r'\s([?.!"](?:\s|$))', r'\1', )
+        st.write(text2)
+
+if option == 'Sentiment Analysis':
+    text = st.text_area('Enter Text Below:', height=200)
+    #uploaded_file = st.file_uploader("Choose a file", type=['txt'])
+    submit = st.button('Generate')
+    if submit:
+        st.subheader("Sentiment:")
+        result = sentiment(text)
+        sent = result[0]['label']
+        cert = result[0]['score']
+        st.write('Text Sentiment: {} | Probability: {:.1f}%'.format(sent,(cert*100)))
+
+if option == 'Named Entity Recognition':
+    text = st.text_area('Enter Text Below:', height=300)
+    #uploaded_file = st.file_uploader("Choose a file", type=['txt'])
+    submit = st.button('Generate')
+    if submit:    
+        entities = []
+        entityLabels = []
+        doc = nlp(text)
+        for ent in doc.ents:
+            entities.append(ent.text)
+            entityLabels.append(ent.label_)
+        entDict = dict(zip(entities, entityLabels)) 
+        entOrg = entRecognizer(entDict, "ORG")
+        entPerson = entRecognizer(entDict, "PERSON")
+        entDate = entRecognizer(entDict, "DATE")
+        entGPE = entRecognizer(entDict, "GPE")
+        entLoc = entRecognizer(entDict, "LOC")
+        options = {"ents": ["ORG", "GPE", "PERSON", "LOC", "DATE"]}
+        HTML_WRAPPER = """<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; margin-bottom: 2.5rem">{}</div>"""
+        
+        st.subheader("List of Named Entities:")
+        st.write("Geopolitical Entities (GPE): " + str(entGPE))
+        st.write("People (PERSON): " + str(entPerson))
+        st.write("Organizations (ORG): " + str(entOrg))
+        st.write("Dates (DATE): " + str(entDate))
+        st.write("Locations (LOC): " + str(entLoc))
+        st.subheader("Original Text with Entities Highlighted")
+        html = displacy.render(doc, style="ent", options=options)
+        html = html.replace("\n", " ")
+        st.write(HTML_WRAPPER.format(html), unsafe_allow_html=True)