Spacy-Stremlit App

app.py

def load_model():
    # declare global variables
    global nlp
    global textcat

nlp = spacy.load('C:/Project')  ## will load the model from the model_path
textcat = nlp.get_pipe('textcat')  ## will load the model file

def main():
    """Wikipedia Citation Needed NLP app with Spacy-Streamlit"""
    st.title("Wikipedia Citation Needed Predictor")
    our_image = Image.open(os.path.join('wiki_logo.png'))
    st.image(our_image)
    menu = ["Home", "NER", "Textcat","Explain Prediction"]
    choice = st.sidebar.selectbox("Menu", menu)
    if choice == "Home":
        st.subheader("Tokenization")
        raw_text = st.text_area("Your Text", "Enter Text Here")
        docx = nlp(raw_text)
        if st.button("Tokenize"):
            spacy_streamlit.visualize_tokens(docx, attrs=['text', 'pos_', 'dep_', 'ent_type_'])
    elif choice == "NER":
        st.subheader("Named Entity Recognition")
        raw_text = st.text_area("Your Text", "Enter Text Here")
        docx = nlp(raw_text)
        spacy_streamlit.visualize_ner(docx, labels=nlp.get_pipe('ner').labels)
    elif choice == "Textcat":
        st.subheader("Citation Needed ")
        raw_text = st.text_area("Your Text", "Enter Text Here")
        docx = nlp(raw_text)
        spacy_streamlit.visualize_textcat(docx, title="Sentence Need Citation" )
    elif choice == "Explain Prediction":
        st.subheader("Why this predicted")
        raw_text = st.text_area("Your Text", "Enter Text Here")
        # get number of features input
        num_features_input = st.number_input(label="Num of features to visualize",
                                             min_value=1, max_value=7, step=1)



if __name__ == '__main__':
    main()