import pandas as pd 
import tensorflow as tf
import gradio as gr
df=pd.read_csv("train.csv")
from tensorflow.keras.layers import TextVectorization
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 = tf.keras.models.load_model('toxicity.h5')
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]*100)>0.5)
    
    return text

interface = gr.Interface(fn=score_comment,  
                         inputs=gr.inputs.Textbox(lines=2, placeholder='Comment to score'),
                        outputs='text')
                     
interface.launch()