app.py

import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("ai4bharat/IndicNER")

model = AutoModelForTokenClassification.from_pretrained("ai4bharat/IndicNER")


def get_ner(sentence):
    tok_sentence = tokenizer(sentence, return_tensors='pt')

    with torch.no_grad():
        logits = model(**tok_sentence).logits.argmax(-1)
        predicted_tokens_classes = [
            model.config.id2label[t.item()] for t in logits[0]]

        predicted_labels = []

        previous_token_id = 0
        word_ids = tok_sentence.word_ids()
        for word_index in range(len(word_ids)):
            if word_ids[word_index] == None:
                previous_token_id = word_ids[word_index]
            elif word_ids[word_index] == previous_token_id:
                previous_token_id = word_ids[word_index]
            else:
                predicted_labels.append(predicted_tokens_classes[word_index])
                previous_token_id = word_ids[word_index]

        ner_output = []
        for index in range(len(sentence.split(' '))):
            ner_output.append(
                (sentence.split(' ')[index], predicted_labels[index]))
        return ner_output


iface = gr.Interface(get_ner,
                     gr.Textbox(placeholder="Enter sentence here..."),
                     ["highlight"], description='The 11 languages covered by IndicNER are: Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, Telugu.',
                     examples=['लगातार हमलावर हो रहे शिवपाल और राजभर को सपा की दो टूक, चिट्ठी जारी कर कहा- जहां जाना चाहें जा सकते हैं', 'ಶರಣ್ ರ ನೀವು ನೋಡಲೇಬೇಕಾದ ಟಾಪ್ 5 ಕಾಮಿಡಿ ಚಲನಚಿತ್ರಗಳು'], title='IndicNER',
                     article='IndicNER is a model trained to complete the task of identifying named entities from sentences in Indian languages. Our model is specifically fine-tuned to the 11 Indian languages mentioned above over millions of sentences. The model is then benchmarked over a human annotated testset and multiple other publicly available Indian NER datasets.'
                     )

iface.launch(enable_queue=True)