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import transformers
import streamlit as st
from transformers import AutoTokenizer, AutoModelWithLMHead
###################
# global variables
###################
# page configs and functions
st.set_page_config(
page_title="Romanian Text Generator",
page_icon="🇷🇴",
layout="wide"
)
model_list = ["dumitrescustefan/gpt-neo-romanian-780m"]
st.sidebar.header("Select Model")
model_checkpoint = st.sidebar.radio("", model_list)
text_element = st.text_input('Text:', 'Acesta este un exemplu,')
st.sidebar.header("Select type of PERSON detection")
max_length = st.sidebar.slider("Max Length", value=20, min_value=10, max_value=200)
temperature = st.sidebar.slider("Temperature", value=1.0, min_value=0.0, max_value=1.0, step=0.05)
top_k = st.sidebar.slider("Top-k", min_value=0, max_value=15, step=1, value=0)
top_p = st.sidebar.slider("Top-p", min_value=0.0, max_value=1.0, step=0.05, value=0.9)
@st.cache(allow_output_mutation=True)
def setModel(model_name):
model = AutoModelWithLMHead.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
return model, tokenizer
def infer(model, tokenizer, text, input_ids, max_length, temperature, top_k, top_p):
encoded_prompt = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt")
output_sequences = model.generate(
input_ids=encoded_prompt.input_ids,
max_length=max_length,
temperature=temperature,
top_k=top_k,
top_p=top_p,
do_sample=True,
num_return_sequences=1
)
return output_sequences
output_sequences = infer(model, tokenizer, text_element, input_ids, max_length, temperature, top_k, top_p)
for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===")
generated_sequences = generated_sequence.tolist()
# Decode text
text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
# Remove all text after the stop token
# text = text[: text.find(args.stop_token) if args.stop_token else None]
# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
total_sequence = (
sent + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)):]
)
generated_sequences.append(total_sequence)
print(total_sequence)
st.write(generated_sequences[-1], text_element)
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