NeuroSkeptic / app.py
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import transformers
import torch
import tokenizers
import streamlit as st
import re
from PIL import Image
@st.cache(hash_funcs={tokenizers.Tokenizer: lambda _: None, tokenizers.AddedToken: lambda _: None, re.Pattern: lambda _: None}, allow_output_mutation=True, suppress_st_warning=True)
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")