import streamlit as st
import numpy as np
import pandas as pd
import os
import torch
import torch.nn as nn
from transformers.activations import get_activation
from transformers import AutoTokenizer, AutoModelWithLMHead, AutoModelForCausalLM

st.title('DeepWords')
st.text('Still under Construction.')
st.text('Tip: Try writing a sentence and making the model predict final word.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

@st.cache(allow_output_mutation=True)
def get_model():
  tokenizer = AutoTokenizer.from_pretrained("ml6team/gpt-2-medium-conditional-quote-generator")
  model = AutoModelForCausalLM.from_pretrained("ml6team/gpt-2-medium-conditional-quote-generator")
  return model, tokenizer
    
 
model, tokenizer = get_model()
#g = 
c = 5
with st.form(key='my_form'):
    prompt = st.text_input('Enter sentence:', '')
    c = st.number_input('Enter Number of words: ', 1)
    submit_button = st.form_submit_button(label='Submit')
    if submit_button:
      with torch.no_grad():
        text = tokenizer.encode(prompt)
        myinput, past_key_values = torch.tensor([text]), None
        myinput = myinput
        myinput= myinput.to(device)
        logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
        logits = logits[0,-1]
        probabilities = torch.nn.functional.softmax(logits)
        best_logits, best_indices = logits.topk(350)
        best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
        text.append(best_indices[0].item())
        best_probabilities = probabilities[best_indices].tolist()
        words = []
        
        best_words = ' '.join(best_words[0:c])
        final_string = prompt + best_words             
        st.write(final_string)