Create app.py

app.py

@@ -0,0 +1,59 @@
+import streamlit as st
+import spacy
+from spacytextblob.spacytextblob import SpacyTextBlob
+
+st.set_page_config(layout='wide', initial_sidebar_state='expanded')
+st.title('Text Analysis using Spacy Textblob')
+st.markdown('Type a sentence in the below text box and choose the desired option in the adjacent menu.')
+side = st.sidebar.selectbox("Select an option below", ("Sentiment", "Subjectivity", "NER"))
+Text = st.text_input("Enter the sentence")
+
+
+@st.cache
+def sentiment(text):
+    nlp = spacy.load('en_core_web_sm')
+    nlp.add_pipe('spacytextblob')
+    doc = nlp(text)
+    if doc._.polarity<0:
+        return "Negative"
+    elif doc._.polarity==0:
+        return "Neutral"
+    else:
+        return "Positive"
+
+@st.cache
+def subjectivity(text):
+    nlp = spacy.load('en_core_web_sm')
+    nlp.add_pipe('spacytextblob')
+    doc = nlp(text)
+    if doc._.subjectivity > 0.5:
+        return "Highly Opinionated sentence"
+    elif doc._.subjectivity < 0.5:
+        return "Less Opinionated sentence"
+    else:
+        return "Neutral sentence"
+
+@st.cache
+def ner(sentence):
+    nlp = spacy.load("en_core_web_sm")
+    doc = nlp(sentence)
+    ents = [(e.text, e.label_) for e in doc.ents]
+    return ents
+
+
+
+def run():
+
+    if side == "Sentiment":
+        st.write(sentiment(Text))
+    if side == "Subjectivity":
+        st.write(subjectivity(Text))
+
+    if side == "NER":
+        st.write(ner(Text))
+    
+    
+
+    
+if __name__ == '__main__':
+    run()