import os
import pandas as pd
import tensorflow as tf
import numpy as np

from tensorflow.keras.layers import TextVectorization
import gradio as gr
from tensorflow.keras.layers import TextVectorization

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

MAX_FEATURES = 200000
data = pd.read_csv(os.path.join('jigsaw-toxic-comment-classification-challenge', 'train.csv', 'train.csv'))
x = data['comment_text']
y = data[data.columns[2:]].values

vectorizer = TextVectorization(max_tokens=MAX_FEATURES, output_sequence_length=1800, output_mode='int')
vectorizer.adapt(x.values)
vectorizer('Yo Whats up')[:3]
vectorized_text = vectorizer(x.values)
vectorized_text

input_str = vectorizer('yo i fuckin hate you')
res = modelbaru.predict(np.expand_dims(input_str,0))
res > 0.5
data.columns[2:]
data.columns[2:-1]

def score_comment(comment):
    vectorized_comment = vectorizer([comment])
    results = modelbaru.predict(vectorized_comment)

    text = ''
    for idx, col in enumerate(data.columns[2:-1]):
        text += '{}: {}\n'.format(col, results[0][idx]>0.5)
    
    return text

interface = gr.Interface(fn=score_comment, inputs=gr.inputs.Textbox(lines=2, placeholder='Toxic Detector by: AezersX'), outputs='text')
interface.launch()