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import transformers
import torch
import tokenizers
import streamlit as st
import re
@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):
tokenizer = transformers.GPT2Tokenizer.from_pretrained(model_name)
model = transformers.GPT2LMHeadModel.from_pretrained(model_name)
model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
model.eval()
return model, tokenizer
#@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 predict(text, model, tokenizer, n_beams=5, temperature=2.5, top_p=0.8, max_length=200):
text += '\n'
input_ids = tokenizer.encode(text, return_tensors="pt")
with torch.no_grad():
out = model.generate(input_ids,
do_sample=True,
num_beams=n_beams,
temperature=temperature,
top_p=top_p,
max_length=max_length,
)
return list(map(tokenizer.decode, out))[0]
model, tokenizer = get_model('sberbank-ai/rugpt3medium_based_on_gpt2', 'korzh-medium_30epochs_1bs.bin')
st.title("NeuroKorzh")
st.markdown("<img width=400px src='https://the-flow.ru/uploads/images/resize/830x0/adaptiveResize/05/06/06/42/25/8c7405840cd7.jpg'>",
unsafe_allow_html=True)
st.markdown("\n")
text = st.text_area(label='Starting point for text generation', height=100)
button = st.button('Go')
if button:
#try:
result = predict(text, model, tokenizer)
#st.subheader('Max Korzh:')
#lines = result.split('\n')
#for line in lines:
# st.write(line)
lines = result.replace('\n', '\n\n')
st.write(lines)
#except Exception:
# st.error("Ooooops, something went wrong. Try again please and report to me, tg: @vladyur")
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