import os
os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'

import tensorflow as tf
import gradio as gr
import pandas as pd
import pickle
from keras.utils import pad_sequences

max_len = 200

# Load the tokenizer
with open('tokenizer.pickle', 'rb') as handle:
    tokenizer = pickle.load(handle)

# Load the pre-trained model
model = tf.keras.models.load_model('toxic.h5')

arr = ["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]

def score_comment(comment):
    sequences = tokenizer.texts_to_sequences([comment])
    inp = pad_sequences(sequences, maxlen=max_len)
    results = model.predict(inp)
    text = ''
    for i in range(len(arr)):
        text += '{}: {}\n'.format(arr[i], results[0][i] > 0.5)
    return text

# Update the Gradio interface to use the latest syntax
inputs = gr.Textbox(lines=2, placeholder='Comment to score')
outputs = gr.Textbox()

interface = gr.Interface(fn=score_comment, inputs=inputs, outputs=outputs)

# Launch the interface
interface.launch(share=True)