import streamlit as st
import torch
from time import perf_counter
from transformers import AutoTokenizer, AutoModelForCausalLM

MODEL = 'nan-dre/maneleGPT-medium'
TOKENIZER = 'nan-dre/maneleGPT-medium'
MAX_LENGTH = 256

st.set_page_config(
    page_title="ManeleGPT",
    page_icon="🇷🇴",
    layout="centered"
)

def typical_sampling(model, input_ids, attention_mask, no_repeat_ngram_size, max_length, temperature, typical_p):
    return model.generate(
        input_ids=input_ids,
        attention_mask=attention_mask,
        no_repeat_ngram_size=no_repeat_ngram_size,
        max_length=max_length,
        do_sample=True,
        temperature=temperature,
        typical_p=typical_p,
        top_k=0
    )


@st.cache(allow_output_mutation=True)
def setModel():
    model = AutoModelForCausalLM.from_pretrained(MODEL)
    tokenizer = AutoTokenizer.from_pretrained(TOKENIZER)
    return model, tokenizer

st.header("ManeleGPT")
temperature = st.slider(label="Temperatura", min_value=0.01, max_value=1.0, value=0.5, step=0.01)
input = st.text_input(label="Cu ce vers sa inceapa maneaua?", value="", key="seed")

if input:
    model, tokenizer = setModel()

    tokenized_text = tokenizer(input, add_special_tokens=False, return_tensors="pt")

    if len(tokenized_text.input_ids[0]) + MAX_LENGTH > 512:  # need to keep less words
        keep_last = 512 - MAX_LENGTH
        print(f"keep last: {keep_last}")
        input_ids, attention_mask = tokenized_text.input_ids[0][-keep_last:], tokenized_text.attention_mask[0][-keep_last:]
        previous_ids = tokenized_text.input_ids[0][:keep_last]
        st.warning(f"kept last {keep_last}")
    else:
        input_ids, attention_mask = tokenized_text.input_ids[0], tokenized_text.attention_mask[0]
        previous_ids = None

    length = min(512, len(input_ids) + MAX_LENGTH)
    timer_mark = perf_counter()
    output = typical_sampling(model, input_ids.unsqueeze(dim=0), attention_mask.unsqueeze(dim=0), no_repeat_ngram_size=2, max_length=MAX_LENGTH, temperature=temperature, typical_p=1)
    details = f"Text generated in {perf_counter()-timer_mark:.2f}s"


    if previous_ids is not None:
        print(f"\nConcat prev id: "+tokenizer.decode(previous_ids, skip_special_tokens=True))
        print(f"\nWith current decode: " + tokenizer.decode(output[0], skip_special_tokens=True))
        new_text = tokenizer.decode(torch.cat([previous_ids, output[0]], dim=-1), skip_special_tokens=True)
    else:
        new_text = tokenizer.decode(output[0], skip_special_tokens=True)

    st.text(new_text)