Update app.py

app.py

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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()