Create app.py

app.py

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+import streamlit as st
+from flair.models import TextClassifier
+from flair.data import Sentence
+import numpy as np
+global tagger
+
+def load_flair():
+    return TextClassifier.load('en-sentiment')
+
+def main():
+    tagger = load_flair()
+
+    st.markdown("<h1 style = 'textalign:center; color:blue;'> Sentiment Detection </h1>", unsafe_allow_html = True)
+    st.write("Sentiment Detection from text is a classical problem. This is used when you try to predict the sentiment of comments.")
+
+    input_sent = st.text_input("Input Sentence", "Although not well rated, the food in this restaurant was tasty and I enjoyed the meal!")
+
+    s = Sentence(input_sent)
+    tagger.predict(s)
+    st.write("### Your Sentence is ", str(s.labels))
+
+if __name__ == '__main__':
+    main()