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import tensorflow as tf
import gradio as gr
import pandas as pd
from transformers import AutoTokenizer
model_save_path = "Multilingual_toxic_comment_classifier/"
### Loading the fine-tuned model ###
loaded_model = tf.keras.models.load_model(model_save_path)
### Initializing the tokenizer ###
tokenizer_ = AutoTokenizer.from_pretrained("xlm-roberta-large")
examples_list = [
[example]
for example in pd.read_csv("examples/sample_comments.csv")["comment_text"].tolist()
]
def prep_data(text, tokenizer, max_len=192):
tokens = tokenizer(
text,
max_length=max_len,
truncation=True,
padding="max_length",
add_special_tokens=True,
return_tensors="tf",
)
return {
"input_ids": tokens["input_ids"],
"attention_mask": tokens["attention_mask"],
}
def predict(text):
prob_of_toxic_comment = loaded_model.predict(
prep_data(text=text, tokenizer=tokenizer_, max_len=192)
)[0][0]
prob_of_non_toxic_comment = 1 - prob_of_toxic_comment
prob_of_toxic_comment, prob_of_non_toxic_comment
probs = {
"prob_of_toxic_comment": float(prob_of_toxic_comment),
"prob_of_non_toxic_comment": float(prob_of_non_toxic_comment),
}
return probs
interface = gr.Interface(
fn=predict,
inputs=gr.components.Textbox(lines=4, label="Comment"),
outputs=[gr.Label(label="Probabilities")],
examples=examples_list,
title="Multi-Lingual Toxic Comment Classification.",
description="XLM-Roberta Large model",
)
interface.launch(debug=False, share=True)