app.py

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

model = tf.keras.models.load_model('toxic-detect.h5')

df = pd.read_csv('train.csv')

X = df.comment_text

vectorizer = TextVectorization(max_tokens=200000, 
                               output_sequence_length=1800, 
                               output_mode='int')

vectorizer.adapt(X.values)


def evaluate_comment(Comment):
    processed_Comment = vectorizer([Comment])
    res = model.predict(processed_Comment)
    text = ''
    for i, col in enumerate(df.columns[2:]):
        text += '{}: \t\t{}\n'.format(col.upper(), '🆘🆘🆘'.upper() if res[0][i] > 0.5 else '😄😄😄'.upper())
    
    return text
    
interface = gr.Interface(fn = evaluate_comment, title='ToxClass', inputs = gr.inputs.Textbox(lines = 4, label='Comment', placeholder='Comment to evaluate'), 
                        outputs = gr.Textbox(lines=4, label='Evaluation'), description="An NLP model that classifies level of toxicity of the sentence.")
                         
interface.launch()