sarcasm_prediction.py

x = np.random.randint(0, Xval.shape[0] - 1)

headline = df['headline'].values[x]

print("Headline: ", headline)

cleaned_text = []

sentence = removeURL(headline) 
sentence = removeHTML(sentence)
sentence = onlyAlphabets(sentence)
sentence = sentence.lower()   

for word in sentence.split():
    #if word not in stop:
        stemmed = sno.stem(word)
        cleaned_text.append(stemmed)

cleaned_text = [' '.join(cleaned_text)]

print("Cleaned text: ", cleaned_text[0])

cleaned_text = tokenizer.texts_to_sequences(cleaned_text)
cleaned_text = pad_sequences(cleaned_text, maxlen=mlen)

category = df['is_sarcastic'].values[x]  

print("\nTrue category: ", class_names[category])

output = model.predict(cleaned_text)[0][0]

pred = (output>0.5).astype('int64')

print("\nPredicted category: ", class_names[pred], "(", output, "-->", pred, ")")