import transformers

from datasets import Dataset
import pdb
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

import nltk

model_name = "pytorch_model.bin"
model_dir = f"model/checkpoint-2672/"

tokenizer_name = "google/flan-t5-base"

tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_dir).to('cuda:1')

max_input_length = 512
import sys
text = ['it is too cold in here']
for line in sys.stdin:
    
    inputs = line.rstrip().split('\t')[-1]

    inputs = tokenizer(inputs, max_length=max_input_length, truncation=True, return_tensors="pt").to('cuda:1')
    output = model.generate(**inputs, num_beams=8, do_sample=True, min_length=10, max_length=64)
    decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
    predicted_title = nltk.sent_tokenize(decoded_output.strip())[0]

    print(predicted_title)