import gradio as gr
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import pandas as pd
import torch
from torch.utils.data import Dataset, random_split
from transformers import GPT2Tokenizer, TrainingArguments, Trainer, GPT2LMHeadModel

pretrained_name = "kobkrit/openthaigpt-gpt2-instructgpt-poc-0.0.2"

tokenizer = GPT2Tokenizer.from_pretrained(pretrained_name, bos_token='<|startoftext|>',unk_token='<|unk|>', eos_token='<|endoftext|>', pad_token='<|pad|>')
model = GPT2LMHeadModel.from_pretrained(pretrained_name).cuda()
model.resize_token_embeddings(len(tokenizer))

def gen(input):
    generated = tokenizer("<|startoftext|>"+input, return_tensors="pt").input_ids.cuda()
    output = model.generate(generated, top_k=50, num_beams=5, no_repeat_ngram_size=2, 
        early_stopping=True, max_length=300, top_p=0.95, temperature=1.9)
    return tokenizer.decode(output[0], skip_special_tokens=True)

demo = gr.Interface(fn=gen, inputs=gr.Textbox(lines=3, label="Input Text", value="Q: อยากลดความอ้วน ทำอย่างไร\n\nA:"), outputs="text")
demo.launch()