mle10-glg-demo / app.py
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# Imports
import gradio as gr
from sklearn.linear_model import LogisticRegression
import pickle5 as pickle
# Load model from pickle file
model = pickle.load(open('lg_classifier.sav', 'rb'))
# Define function to predict
def predict(text):
return model.predict([text])[0]
# Define interface
iface = gr.Interface(fn=predict,
inputs=gr.inputs.Textbox(lines=10, label="Input Text"),
outputs=gr.outputs.Label(num_top_classes=3),
title="Text Classification",
description="Classify text as other[0], healthcare[1], or technology[2]",
examples=[
['This is a text about healthcare'],
['This is a text about technology'],
['This is a text about other']],
allow_flagging='never'
)