Spaces:
Runtime error
Runtime error
File size: 4,174 Bytes
d7acda5 3cbcd97 d7acda5 aeaac81 d7acda5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 |
import streamlit as st
from transformers import pipeline, set_seed
from transformers import AutoTokenizer
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/rtl.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()
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)
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()
|