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import transformers | |
import torch | |
import tokenizers | |
import streamlit as st | |
import re | |
from PIL import Image | |
def get_model(model_name, model_path='pytorch_model.bin'): | |
tokenizer = transformers.GPT2Tokenizer.from_pretrained(model_name) | |
model = transformers.OPTForCausalLM.from_pretrained(model_name) | |
model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu'))) | |
model.eval() | |
return model, tokenizer | |
def predict(text, model, tokenizer, n_beams=5, temperature=2.5, top_p=0.8, length_of_generated=300): | |
text += '\n' | |
input_ids = tokenizer.encode(text, return_tensors="pt") | |
length_of_prompt = len(input_ids[0]) | |
with torch.no_grad(): | |
out = model.generate(input_ids, | |
do_sample=True, | |
num_beams=n_beams, | |
temperature=temperature, | |
top_p=top_p, | |
max_length=length_of_prompt + length_of_generated, | |
eos_token_id=tokenizer.eos_token_id | |
) | |
return list(map(tokenizer.decode, out))[0] | |
model, tokenizer = get_model('facebook/opt-13b') | |
# st.title("NeuroKorzh") | |
# image = Image.open('korzh.jpg') | |
# st.image(image, caption='НейроКорж') | |
# option = st.selectbox('Выберите своего Коржа', ('Быстрый', 'Глубокий')) | |
craziness = st.slider(label='Craziness', min_value=0, max_value=100, value=50, step=5) | |
temperature = 2 + craziness / 50. | |
st.markdown("\n") | |
text = st.text_area(label='What are you interested in?', value='Covid - a worldwide conspiracy?', height=80) | |
button = st.button('Go') | |
if button: | |
try: | |
with st.spinner('Finding out the truth'): | |
result = predict(text, model, tokenizer, temperature=temperature) | |
st.text_area(label='', value=result, height=1100) | |
except Exception: | |
st.error("Ooooops, something went wrong. Please try again and report to me, tg: @vladyur") |