| import streamlit as st | |
| from transformers import pipeline | |
| import tensorflow as tf | |
| import numpy as np | |
| import pandas as pd | |
| from tensorflow.keras.layers import TextVectorization | |
| from tensorflow import keras | |
| model = tf.keras.models.load_model('toxicity_model.h5') | |
| df = pd.read_csv('train.csv') | |
| X = df['comment_text'] | |
| y = df[df.columns[2:]].values | |
| MAX_FEATURES = 200000 | |
| vectorizer = TextVectorization(max_tokens=MAX_FEATURES, | |
| output_sequence_length=1800, | |
| output_mode='int') | |
| vectorizer.adapt(X.values) | |
| input_str = vectorizer('I hate you.') | |
| res = model.predict(np.expand_dims(input_str,0)) | |
| classification = res[0].tolist() | |
| st.write(classification) |