Spaces:
Runtime error
Runtime error
File size: 5,571 Bytes
a4af9d2 0f2a50b a4af9d2 7c82098 a4af9d2 3e7562a a4af9d2 3e7562a a4af9d2 3e7562a a4af9d2 d356c04 a4af9d2 7c82098 a4af9d2 7c82098 a4af9d2 7c82098 a4af9d2 d356c04 a4af9d2 3e7562a a4af9d2 d356c04 7be8469 d356c04 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 |
import json
import requests
from mtranslate import translate
from prompts import PROMPT_LIST
import streamlit as st
import random
import transformers
from transformers import GPT2Tokenizer, GPT2LMHeadModel
import fasttext
import SessionState
LOGO = "huggingwayang.png"
MODELS = {
"GPT-2 Small": "flax-community/gpt2-small-indonesian",
"GPT-2 Medium": "flax-community/gpt2-medium-indonesian",
"GPT-2 Small finetuned on Indonesian academic journals": "Galuh/id-journal-gpt2"
}
headers = {}
@st.cache(show_spinner=False, persist=True, hash_funcs={transformers.models.gpt2.modeling_gpt2.GPT2LMHeadModel: lambda _: None})
def load_gpt(model_type):
model = GPT2LMHeadModel.from_pretrained(MODELS[model_type])
return model
@st.cache(show_spinner=False, persist=True, hash_funcs={transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer: lambda _: None})
def load_gpt_tokenizer(model_type):
tokenizer = GPT2Tokenizer.from_pretrained(MODELS[model_type])
return tokenizer
def get_image(text: str):
url = "https://wikisearch.uncool.ai/get_image/"
try:
payload = {
"text": text,
"image_width": 400
}
data = json.dumps(payload)
response = requests.request("POST", url, headers=headers, data=data)
print(response.content)
image = json.loads(response.content.decode("utf-8"))["url"]
except:
image = ""
return image
st.set_page_config(page_title="Indonesian GPT-2 Demo")
st.title("Indonesian GPT-2")
ft_model = fasttext.load_model('lid.176.ftz')
# Sidebar
st.sidebar.image(LOGO)
st.sidebar.subheader("Configurable parameters")
max_len = st.sidebar.number_input(
"Maximum length",
value=100,
help="The maximum length of the sequence to be generated."
)
temp = st.sidebar.slider(
"Temperature",
value=1.0,
min_value=0.0,
max_value=100.0,
help="The value used to module the next token probabilities."
)
top_k = st.sidebar.number_input(
"Top k",
value=50,
help="The number of highest probability vocabulary tokens to keep for top-k-filtering."
)
top_p = st.sidebar.number_input(
"Top p",
value=1.0,
help=" If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation."
)
st.markdown(
"""
This demo uses the [small](https://huggingface.co/flax-community/gpt2-small-indonesian) and
[medium](https://huggingface.co/flax-community/gpt2-medium-indonesian) Indonesian GPT2 model
trained on the Indonesian [Oscar](https://huggingface.co/datasets/oscar), [MC4](https://huggingface.co/datasets/mc4)
and [Wikipedia](https://huggingface.co/datasets/wikipedia) dataset. We created it as part of the
[Huggingface JAX/Flax event](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/).
The demo supports "multi language" ;-), feel free to try a prompt on your language. We are also experimenting with
the sentence based image search using Wikipedia passages encoded with distillbert, and search the encoded sentence
in the encoded passages using Facebook's Faiss.
"""
)
model_name = st.selectbox('Model',(['GPT-2 Small', 'GPT-2 Medium', 'GPT-2 Small finetuned on Indonesian academic journals']))
if model_name in ["GPT-2 Small", "GPT-2 Medium"]:
prompt_group_name = "GPT-2"
elif model_name in ["GPT-2 Small finetuned on Indonesian academic journals"]:
prompt_group_name = "Indonesian Journals"
ALL_PROMPTS = list(PROMPT_LIST[prompt_group_name].keys())+["Custom"]
prompt = st.selectbox('Prompt', ALL_PROMPTS, index=len(ALL_PROMPTS)-1)
session_state = SessionState.get(prompt_box=None)
if prompt == "Custom":
prompt_box = "Enter your text here"
else:
prompt_box = random.choice(PROMPT_LIST[prompt_group_name][prompt])
session_state.prompt_box = prompt_box
text = st.text_area("Enter text", session_state.prompt_box)
if st.button("Run"):
text = st.text_area("Enter text", session_state.prompt_box)
with st.spinner(text="Getting results..."):
lang_predictions, lang_probability = ft_model.predict(text.replace("\n", " "), k=3)
if "__label__id" in lang_predictions:
lang = "id"
else:
lang = lang_predictions[0].replace("__label__", "")
text = translate(text, "id", lang)
st.subheader("Result")
model = load_gpt(model_name)
tokenizer = load_gpt_tokenizer(model_name)
input_ids = tokenizer.encode(text, return_tensors='pt')
output = model.generate(input_ids=input_ids,
max_length=max_len,
temperature=temp,
top_k=top_k,
top_p=top_p,
repetition_penalty=2.0)
text = tokenizer.decode(output[0],
skip_special_tokens=True)
st.write(text.replace("\n", " \n"))
st.text("Translation")
translation = translate(text, "en", "id")
if lang == "id":
st.write(translation.replace("\n", " \n"))
else:
st.write(translate(text, lang, "id").replace("\n", " \n"))
image_cat = "https://media.giphy.com/media/vFKqnCdLPNOKc/giphy.gif"
image = get_image(translation.replace("\"", "'"))
if image is not "":
st.image(image, width=400)
else:
# display cat image if no image found
st.image(image_cat, width=400)
|