<filename>infer.py
import torch
from kobart import get_kobart_tokenizer
from transformers.models.bart import BartForConditionalGeneration
from flask import Flask, jsonify, request

app = Flask(__name__)

device = 'cuda' if torch.cuda.is_available() else 'cpu'


def load_model():
    model = BartForConditionalGeneration.from_pretrained('./kobart_summary')
    return model.to(device)


model = load_model()
tokenizer = get_kobart_tokenizer()


@app.route("/health.json")
def health():
    return jsonify({"status": "UP"}), 200


@app.route('/', methods=['POST'])
def summarization():
    request_data = request.get_json()
    # text = request.args('text')
    text = request_data['text']
    text = text.replace('\n', '')
    input_ids = tokenizer.encode(text)
    input_ids = torch.tensor(input_ids)
    input_ids = input_ids.unsqueeze(0).to(device)
    output = model.generate(input_ids, eos_token_id=1,
                            max_length=512, num_beams=5)
    output = tokenizer.decode(output[0], skip_special_tokens=True)
    print(output)
    return jsonify({'output': output})


if __name__ == "__main__":
    app.run(debug=True, host='0.0.0.0')

<filename>infer.py
import torch
from kobart import get_kobart_tokenizer
from transformers.models.bart import BartForConditionalGeneration
from flask import Flask, jsonify, request

app = Flask(__name__)

device = 'cuda' if torch.cuda.is_available() else 'cpu'


def load_model():
    model = BartForConditionalGeneration.from_pretrained('./kobart_summary')
    return model.to(device)


model = load_model()
tokenizer = get_kobart_tokenizer()


@app.route("/health.json")
def health():
    return jsonify({"status": "UP"}), 200


@app.route('/', methods=['POST'])
def summarization():
    request_data = request.get_json()
    # text = request.args('text')
    text = request_data['text']
    text = text.replace('\n', '')
    input_ids = tokenizer.encode(text)
    input_ids = torch.tensor(input_ids)
    input_ids = input_ids.unsqueeze(0).to(device)
    output = model.generate(input_ids, eos_token_id=1,
                            max_length=512, num_beams=5)
    output = tokenizer.decode(output[0], skip_special_tokens=True)
    print(output)
    return jsonify({'output': output})


if __name__ == "__main__":
    app.run(debug=True, host='0.0.0.0')

# text = request.args('text')