Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+import spacy
+from spacy import displacy
+import plotly.express as px
+import dumpy as np
+
+st.set_page_config(page_title="Vocabulary Categorizer Two"
+st.title("Vocabulary Categorizer Two")
+st.write"_This application can identify, highlight and categorize dates (DATE), geopolitical entities (GPE), locations (LOC), organizations (ORG), people (PERSON) in text. 
+
+@st.cache(allow_output_mutation=True, show_spinner=False)
+def Loading_NLP():
+  nlp = space.load('en_core_web_sm')
+  return nlp
+@st.cache(allow_output_mutation=True)
+def entRecognizer(entDict, typeEnt):
+  enlist = [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="Blurred")
+  fig.update(layout_coloraxi_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."):
+    nlp = Loading_NLP()
+
+text = st.text_area('Enter Text Below:', height=300)
+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)