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import pickle
import gradio as gr
import numpy as np
import tensorflow as tf
from utils import clean_text, tokenize_and_pad
# Load pre-trained TensorFlow model
model = tf.keras.models.load_model('model.h5')
# Load tokenizer
with open('tokenizer.pickle', 'rb') as handle:
tokenizer = pickle.load(handle)
print(type(tokenizer))
# Constants
MAX_LEN = 300
def predict_hate_speech(text):
# Clean the text
cleaned_text = clean_text(text)
# Tokenize and pad the text
preprocessed_text = tokenize_and_pad([cleaned_text], tokenizer, MAX_LEN)
# Make a prediction
prediction = model.predict(preprocessed_text)
# Assuming you have two classes: "Hate" and "Not Hate"
if prediction > 0.5:
result = "Hate"
else:
result = "Not Hate"
return result
# Create a Gradio interface
iface = gr.Interface(
fn=predict_hate_speech,
inputs=gr.Textbox(label="Input Text"),
outputs=gr.Textbox(label="Output Prediction"),
title="Hate Speech Classification",
description="A simple hate speech classifier. Enter a text and click submit to make a prediction."
)
# Run the Gradio app
iface.launch()
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