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import gradio as gr
import tensorflow as tf
import numpy as np
import pickle
from tensorflow.keras.preprocessing.sequence import pad_sequences
from sklearn.preprocessing import LabelEncoder
# Load the trained model
model = tf.keras.models.load_model("cyberbullying_hybrid_model.h5")
# Load the tokenizer
with open("tokenizer.pkl", "rb") as f:
tokenizer = pickle.load(f)
# Load the label encoder
with open("label_encoder.pkl", "rb") as f:
label_encoder = pickle.load(f)
# Function to preprocess text and make predictions
def predict_cyberbullying(text):
if not text.strip():
return "Please enter a valid text."
# Convert text to sequences
seq = tokenizer.texts_to_sequences([text])
padded_seq = pad_sequences(seq, maxlen=100) # Ensure same max_length used during training
# Make prediction
prediction = model.predict(padded_seq)
predicted_label = np.argmax(prediction, axis=1) # Get index of highest probability
# Convert index back to class label
predicted_class = label_encoder.inverse_transform(predicted_label)[0]
return f"Predicted Cyberbullying Type: {predicted_class}"
# Create Gradio interface
ui = gr.Interface(
fn=predict_cyberbullying,
inputs=gr.Textbox(lines=2, placeholder="Enter a text..."),
outputs="text",
title="Cyberbullying Detection",
description="Enter a text and the model will predict the type of cyberbullying."
)
# Launch the UI
ui.launch()
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