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| 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 TFGPT2LMHeadModel, GPT2Tokenizer | |
| import tensorflow as tf | |
| 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") | |
| # will only run once | |
| def get_model(): | |
| tokenizer = GPT2Tokenizer.from_pretrained("gpt2-medium") | |
| GPT2 = TFGPT2LMHeadModel.from_pretrained("gpt2-medium", pad_token_id=tokenizer.eos_token_id) | |
| return GPT2, tokenizer | |
| GPT2, tokenizer = get_model() | |
| 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: | |
| tf.random.set_seed(12) | |
| input_ids = tokenizer.encode(prompt, return_tensors='tf') | |
| sample_output = GPT2.generate( | |
| input_ids, | |
| do_sample = True, | |
| max_length = c, | |
| top_p = 0.8, | |
| top_k = 0) | |
| st.write(tokenizer.decode(sample_output[0], skip_special_tokens = True), '...') | |