Create app.py

app.py

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+#Core Pkgs
+import streamlit as st
+
+#NLP Pkgs
+import spacy_streamlit
+import spacy
+# spacy.load('en_core_web_sm')
+
+nlp = spacy.load("en_core_web_sm")
+
+
+#Web Scraping Pkgs
+from bs4 import BeautifulSoup
+from urllib.request import urlopen
+
+@st.cache
+def get_text(raw_url):
+    page = urlopen(raw_url)
+    soup = BeautifulSoup(page)
+    fetched_text = " ".join(map(lambda p:p.text, soup.find_all('p')))
+    return fetched_text
+
+
+def main():
+    """A Simple NLP App with Spacy-Streamlit"""
+    st.title("Named Entity Recognition")
+
+    menu = ["NER", "NER for URL"]
+    choice = st.sidebar.radio("Pick a choice", menu)
+
+
+    if choice == "NER":
+        raw_text = st.text_area("Enter Text","")
+        if raw_text != "":
+            docx = nlp(raw_text)
+            spacy_streamlit.visualize_ner(docx, labels = nlp.get_pipe('ner').labels)
+
+    elif choice == "NER for URL":
+        raw_url = st.text_input("Enter URL","")
+        text_length = st.slider("Length to Preview", 50,200)
+        if raw_url != "":
+            result = get_text(raw_url)
+            len_of_full_text = len(result)
+            len_of_short_text = round(len(result)/text_length)
+            st.subheader("Text to be analyzed:")
+            st.write(result[:len_of_short_text])
+            preview_docx = nlp(result[:len_of_short_text])
+            spacy_streamlit.visualize_ner(preview_docx, labels = nlp.get_pipe('ner').labels)
+
+if __name__ == '__main__':
+    main()