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import streamlit as st
from transformers import pipeline, set_seed
from transformers import AutoTokenizer
from normalizer import Normalizer
import random
import meta
import examples
from utils import (
remote_css,
local_css
)
class TextGeneration:
def __init__(self):
self.debug = False
self.dummy_output = None
self.tokenizer = None
self.generator = None
self.task = "text-generation"
self.model_name_or_path = "HamidRezaAttar/gpt2-product-description-generator"
set_seed(42)
def load(self):
if not self.debug:
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path)
self.generator = pipeline(self.task, model=self.model_name_or_path, tokenizer=self.model_name_or_path)
def generate(self, prompt, generation_kwargs):
if not self.debug:
generation_kwargs["num_return_sequences"] = 1
max_length = len(self.tokenizer(prompt)["input_ids"]) + generation_kwargs["max_length"]
generation_kwargs["max_length"] = max_length
generation_kwargs["return_full_text"] = False
return self.generator(
prompt,
**generation_kwargs,
)[0]["generated_text"]
return self.dummy_output
@st.cache(allow_output_mutation=True)
def load_text_generator():
generator = TextGeneration()
generator.load()
return generator
def main():
st.set_page_config(
page_title="GPT2 - Home",
page_icon="🏡",
layout="wide",
initial_sidebar_state="expanded"
)
remote_css("https://fonts.googleapis.com/css2?family=Roboto:wght@300&display=swap%22%20rel=%22stylesheet%22")
local_css("assets/ltr.css")
generator = load_text_generator()
st.sidebar.markdown(meta.SIDEBAR_INFO)
max_length = st.sidebar.slider(
label='Max Length',
help="The maximum length of the sequence to be generated.",
min_value=1,
max_value=128,
value=50,
step=1
)
top_k = st.sidebar.slider(
label='Top-k',
help="The number of highest probability vocabulary tokens to keep for top-k-filtering",
min_value=40,
max_value=80,
value=50,
step=1
)
top_p = st.sidebar.slider(
label='Top-p',
help="Only the most probable tokens with probabilities that add up to `top_p` or higher are kept for "
"generation.",
min_value=0.0,
max_value=1.0,
value=0.95,
step=0.01
)
temperature = st.sidebar.slider(
label='Temperature',
help="The value used to module the next token probabilities",
min_value=0.1,
max_value=10.0,
value=1.0,
step=0.05
)
do_sample = st.sidebar.selectbox(
label='Sampling ?',
options=(True, False),
help="Whether or not to use sampling; use greedy decoding otherwise.",
)
generation_kwargs = {
"max_length": max_length,
"top_k": top_k,
"top_p": top_p,
"temperature": temperature,
"do_sample": do_sample,
}
st.markdown(meta.HEADER_INFO)
prompts = list(examples.EXAMPLES.keys()) + ["Custom"]
prompt = st.selectbox('Examples', prompts, index=len(prompts) - 1)
if prompt == "Custom":
prompt_box = meta.PROMPT_BOX
else:
prompt_box = random.choice(examples.EXAMPLES[prompt])
text = st.text_area("Enter text", prompt_box)
generation_kwargs_ph = st.empty()
cleaner = Normalizer()
if st.button("Generate !"):
with st.spinner(text="Generating ..."):
generation_kwargs_ph.markdown(", ".join([f"`{k}`: {v}" for k, v in generation_kwargs.items()]))
if text:
generated_text = generator.generate(text, generation_kwargs)
generated_text = cleaner.clean_txt(generated_text)
st.markdown(
f'<p class="ltr ltr-box">'
f'<span class="result-text">{text} <span>'
f'<span class="result-text generated-text">{generated_text}</span>'
f'</p>',
unsafe_allow_html=True
)
if __name__ == '__main__':
main()
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