app.py

# -*- coding: utf-8 -*-
"""gradio_app.py

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1OQvi3I_q3WfavYBpjovCYfv2SPYt__pF
"""

"""gradio_app.py
Automatically generated by Colaboratory.
Original file is located at
    https://colab.research.google.com/drive/1OQvi3I_q3WfavYBpjovCYfv2SPYt__pF
"""

import json
import gradio as gr
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.text import tokenizer_from_json
import tensorflow_addons as tfa

# Load the pre-trained model and tokenizer
model = tf.keras.models.load_model('baseline.h5')

# Assuming you have already loaded the tokenizer configuration from the JSON file.
# Replace 'path' with the actual path to the directory where 'tokenizer.json' is saved.
with open('tokenizer.json', 'r', encoding='utf-8') as f:
    tokenizer_config = json.load(f)

tokenizer = tf.keras.preprocessing.text.tokenizer_from_json(tokenizer_config)

# Define the labels for classification
labels = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']

def classify_comment(comment):
    # Tokenize the comment and convert it into sequences
    comment_sequence = tokenizer.texts_to_sequences([comment])
    comment_sequence = tf.keras.preprocessing.sequence.pad_sequences(comment_sequence, maxlen=200)

    # Make predictions
    predictions = model.predict(comment_sequence)[0]
    results = dict(zip(labels, predictions))
    
    max_value = max(results.values())

    max_keys = [key for key, value in results.items() if value == max_value]

    return max_keys[0].capitalize()

# Create the Gradio interface
comment_input = gr.inputs.Textbox(label="Enter your comment here")
output_text = gr.outputs.Textbox(label="Classification Results")

iface = gr.Interface(
    fn=classify_comment,
    inputs=comment_input,
    outputs=output_text,
    live=True  # Set to True for live updates without needing to restart the server
)

# Launch the Gradio app
iface.launch()