app.py

import gradio as gr
import os
import pandas as pd
import tensorflow as tf
import numpy as np
from tensorflow.keras.layers import TextVectorization

# Load.
filepath = "tmp-model"
loaded_model = tf.keras.models.load_model(filepath)
vectorizer = loaded_model.layers[0]

columns = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']

model = tf.keras.models.load_model('toxicity.h5')

def score_comment(comment):
    vectorized_comment = vectorizer([comment])
    results = model.predict(vectorized_comment)
    
    text = ''
    for idx, col in enumerate(columns):
        text += '{}: {}\n'.format(col, results[0][idx]>0.5)
    return text
    
interface = gr.Interface(fn=score_comment,
                        inputs=gr.inputs.Textbox(lines=2, placeholder='Comment to score'),
                        outputs='text')


interface.launch()