widged/text-classification

1import streamlit as st2from transformers import pipeline3import spacy4from spacy import displacy5import plotly.express as px6import numpy as np7st.set_page_config(page_title="Text Classification")8st.title("Text Classification'")9st.write("_This web application is intended for educational use, please do not upload any sensitive information._")10st.write("Placing a piece of text into one or more categories.")11 12@st.cache(allow_output_mutation=True, show_spinner=False)13def Loading_Classifier():14    class1 = pipeline("zero-shot-classification",framework="pt")15    return class116 17def plot_result(top_topics, scores):18    top_topics = np.array(top_topics)19    scores = np.array(scores)20    scores *= 10021    fig = px.bar(x=scores, y=top_topics, orientation='h',22                 labels={'x': 'Probability', 'y': 'Category'},23                 text=scores,24                 range_x=(0,115),25                 title='Top Predictions',26                 color=np.linspace(0,1,len(scores)),27                 color_continuous_scale="Bluered")28    fig.update(layout_coloraxis_showscale=False)29    fig.update_traces(texttemplate='%{text:0.1f}%', textposition='outside')30    st.plotly_chart(fig)31 32with st.spinner(text="Please wait for the models to load. This could take up to 60 seconds."):33    class1 = Loading_Classifier()34 35cat1 = st.text_input('Enter each possible category name (separated by a comma). Maximum 5 categories.')36text = st.text_area('Enter Text Below:', height=200)37submit = st.button('Generate')38if submit:39    st.subheader("Classification Results:")40    labels1 = cat1.strip().split(',')41    result = class1(text, candidate_labels=labels1)42    cat1name = result['labels'][0]43    cat1prob = result['scores'][0]44    st.write('Category: {} | Probability: {:.1f}%'.format(cat1name,(cat1prob*100)))45    plot_result(result['labels'][::-1][-10:], result['scores'][::-1][-10:])46