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| import streamlit as st | |
| import time | |
| import tokenizers | |
| from transformers import pipeline | |
| import torch | |
| #from transformers import AutoModelForCausalLM, AutoTokenizer | |
| #@st.cache(allow_output_mutation=True) | |
| #def define_model(): | |
| # model = AutoModelForCausalLM.from_pretrained("facebook/opt-1.3b", torch_dtype=torch.float16).cuda() | |
| # tokenizer = AutoTokenizer.from_pretrained("facebook/opt-1.3b", use_fast=False) | |
| # return model, tokenizer | |
| with st.spinner('Loading OPT-1.3b Model...'): | |
| generator = pipeline('text-generation', model="facebook/opt-1.3b", skip_special_tokens=True) | |
| st.success('Model loaded correctly!') | |
| #@st.cache(allow_output_mutation=True) | |
| #def opt_model(prompt, model, tokenizer, num_sequences = 1, max_length = 50): | |
| # input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda() | |
| # generated_ids = model.generate(input_ids, num_return_sequences=num_sequences, max_length=max_length) | |
| # answer = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) | |
| # return answer | |
| #model, tokenizer = define_model() | |
| prompt= st.text_area('Your prompt here', | |
| '''Hello, I'm am conscious and''') | |
| answer = generator(prompt, max_length=100,no_repeat_ngram_size=3, early_stopping=True, num_beams=10) | |
| #answer = opt_model(prompt, model, tokenizer,) | |
| #lst = ['ciao come stai sjfsbd dfhsdf fuahfuf feuhfu wefwu '] | |
| #answer = define_model(prompt) | |
| lst = answer[0]['generated_text'] | |
| t = st.empty() | |
| for i in range(len(lst)): | |
| t.markdown(" %s..." % lst[0:i]) | |
| time.sleep(0.04) |