AnupSarkarDD/SarcasmDetect

1import gradio as gr
2import tensorflow as tf
3from tensorflow.keras.preprocessing.sequence import pad_sequences
4from tensorflow.keras.preprocessing.text import tokenizer_from_json
5import json
6
7# Constants (must match training)
8max_len = 25
9
10# Load saved model and tokenizer
11model = tf.keras.models.load_model("sarcasm_model.keras")
12
13with open("tokenizer.json") as f:
14    tokenizer_data = f.read()
15tokenizer = tokenizer_from_json(tokenizer_data)
16
17def predict_sarcasm(text):
18    # Preprocess input text using the saved tokenizer
19    sequences = tokenizer.texts_to_sequences([text])
20    padded = pad_sequences(sequences, maxlen=max_len, padding='post', truncating='post')
21    pred = model.predict(padded)[0][0]
22    
23    # Interpretation of sarcasm probability
24    if pred > 0.8:
25        label = "Highly Sarcastic"
26    elif pred > 0.6:
27        label = "Moderately Sarcastic"
28    elif pred > 0.4:
29        label = "Neutral"
30    elif pred > 0.2:
31        label = "Mildly Sarcastic"
32    else:
33        label = "Not Sarcastic"
34    
35    return f"Sarcasm Probability: {pred:.2f}", label
36
37iface = gr.Interface(
38    fn=predict_sarcasm,
39    inputs=gr.Textbox(lines=2, placeholder="Enter headline here..."),
40    outputs=[gr.Textbox(label="Probability"), gr.Textbox(label="Interpretation")],
41    title="Sarcasm Detection",
42    description="Enter a headline to check if it is sarcastic."
43)
44
45if __name__ == "__main__":
46    iface.launch()
47