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import streamlit as st
import torch
import numpy as np
import transformers
import random
import textwrap
@st.cache
def load_model():
model_finetuned = transformers.AutoModelWithLMHead.from_pretrained(
'tinkoff-ai/ruDialoGPT-small',
output_attentions = False,
output_hidden_states = False
)
model_finetuned.load_state_dict(torch.load('GPT_sonnik_only.pt', map_location=torch.device('cpu')))
tokenizer = transformers.AutoTokenizer.from_pretrained('tinkoff-ai/ruDialoGPT-small')
return model_finetuned, tokenizer
def preprocess_text(text_input, tokenizer):
prompt = tokenizer.encode(text_input, return_tensors='pt')
return prompt
def predict_sentiment(model, prompt, temp, num_generate):
with torch.inference_mode():
result = model.generate(
input_ids=prompt,
max_length=100,
num_beams=5,
do_sample=True,
temperature=float(temp),
top_k=50,
top_p=0.6,
no_repeat_ngram_size=3,
num_return_sequences=num_generate,
).cpu().numpy()
print(result)
return result
st.title('Text generation with dreambook')
model, tokenizer = load_model()
text_input = st.text_input("Enter some text about movie")
print(text_input)
max_len = st.slider('Length of sequence', 0, 500, 250)
print(max_len)
temp = st.slider('Temperature', 1, 30, 1)
print(temp)
if st.button('Generate a random number of sequences'):
num_generate = random.randint(1,5)
st.write(f'Number of sequences: {num_generate}')
else:
num_generate = st.text_input("Enter number of sequences")
if text_input and num_generate:
prompt = preprocess_text(text_input, tokenizer)
result = predict_sentiment(model, prompt, temp, int(num_generate))
for i in result:
st.write(textwrap.fill(tokenizer.decode(i), max_len)) |