app.py

from huggingface_hub import from_pretrained_keras
import tensorflow as tf 
import gradio as gr
import pandas as pd
from tensorflow.keras.layers import TextVectorization
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)
model = from_pretrained_keras('yasinbastug/comment_toxicity_model')
def score_comment(comment):
    vectorized_comment = vectorizer([comment])
    results = model.predict(vectorized_comment)
    
    text = ''
    for idx, col in enumerate(df.columns[2:]):
        text += '{}: {}\n'.format(col, results[0][idx]>0.5)
    
    return text
interface = gr.Interface(fn=score_comment, 
                         inputs=gr.inputs.Textbox(lines=2, placeholder='Comment to score'),
                        outputs='text')

interface.launch()