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import pandas as pd
import numpy as np
import gradio as gr
import tensorflow as tf
from tensorflow.keras.layers import TextVectorization, LSTM
from tensorflow.keras.models import load_model
df = pd.read_csv('https://raw.githubusercontent.com/whitehatjr1001/comment-toxicity-detecttion-/main/jigsaw-toxic-comment-classification-challenge/train.csv/train.csv')
X = df['comment_text']
y = df[df.columns[2:]].values
max_features = 2000000
vecterizor = TextVectorization(max_tokens=max_features, output_sequence_length=1800, output_mode='int')
vecterizor.adapt(X.values)
model_path = 'commenttoxicity (1).h5'
# Load the model with custom_objects
model = load_model(model_path)
def score_comment(comment):
vectorized_comment = vecterizor([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.Textbox(lines=2, placeholder='Comment to score'),
outputs='text')
interface.launch(share=True)
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