from googletrans import Translator
import torch
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification

def translate_to_english(text):
    translator = Translator()
    translated_text = translator.translate(text, src='kn', dest='en')
    return translated_text.text

def main():
    # Input text in Kannada
    kannada_text = input("Enter the text you want to translate to English: ")

    # Translate Kannada text to English
    english_text = translate_to_english(kannada_text)
    print("Translated text in English:", english_text)

    # Load DistilBERT model and tokenizer
    tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
    model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")

    # Tokenize the English text
    inputs = tokenizer(english_text, return_tensors="pt")

    # Classify the text
    with torch.no_grad():
        logits = model(**inputs).logits

    # Get the predicted class
    predicted_class_id = logits.argmax().item()
    predicted_label = model.config.id2label[predicted_class_id]
    print("Predicted class:", predicted_label)

if __name__ == "__main__":
    main()