import json
import requests
from mtranslate import translate
from prompts import PROMPT_LIST
import streamlit as st
import random
import fasttext
import SessionState
headers = {}
LOGO = "huggingwayang.png"
MODELS = {
"GPT-2 Small": {
"url": "https://api-inference.huggingface.co/models/flax-community/gpt2-small-indonesian"
},
"GPT-2 Medium": {
"url": "https://api-inference.huggingface.co/models/flax-community/gpt2-medium-indonesian"
},
"GPT-2 Small finetuned on Indonesian academic journals": {
"url": "https://api-inference.huggingface.co/models/Galuh/id-journal-gpt2"
},
}
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
def query(payload, model_name):
data = json.dumps(payload)
# print("model url:", MODELS[model_name]["url"])
response = requests.request("POST", MODELS[model_name]["url"], headers=headers, data=data)
return json.loads(response.content.decode("utf-8"))
def process(text: str,
model_name: str,
max_len: int,
temp: float,
top_k: int,
top_p: float):
payload = {
"inputs": text,
"parameters": {
"max_new_tokens": max_len,
"top_k": top_k,
"top_p": top_p,
"temperature": temp,
"repetition_penalty": 2.0,
},
"options": {
"use_cache": True,
}
}
return query(payload, model_name)
st.set_page_config(page_title="Indonesian GPT-2 Demo")
st.title("Indonesian GPT-2")
try:
token = st.secrets["flax_community_token"]
headers = {"Authorization": f"Bearer {token}"}
except FileNotFoundError:
print(f"Token is not found")
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"
session_state = SessionState.get(prompt=None, prompt_box=None, text=None)
ALL_PROMPTS = list(PROMPT_LIST[prompt_group_name].keys())+["Custom"]
prompt = st.selectbox('Prompt', ALL_PROMPTS, index=len(ALL_PROMPTS)-1)
# Update prompt
if session_state.prompt is None:
session_state.prompt = prompt
elif session_state.prompt is not None and (prompt != session_state.prompt):
session_state.prompt = prompt
session_state.prompt_box = None
session_state.text = None
else:
session_state.prompt = prompt
# Update prompt box
if session_state.prompt == "Custom":
session_state.prompt_box = "Enter your text here"
else:
if session_state.prompt is not None and session_state.prompt_box is None:
session_state.prompt_box = random.choice(PROMPT_LIST[prompt_group_name][session_state.prompt])
session_state.text = st.text_area("Enter text", session_state.prompt_box)
if st.button("Run"):
with st.spinner(text="Getting results..."):
lang_predictions, lang_probability = ft_model.predict(session_state.text.replace("\n", " "), k=3)
if "__label__id" in lang_predictions:
lang = "id"
text = session_state.text
else:
lang = lang_predictions[0].replace("__label__", "")
text = translate(session_state.text, "id", lang)
st.subheader("Result")
result = process(text=text,
model_name=model_name,
max_len=int(max_len),
temp=temp,
top_k=int(top_k),
top_p=float(top_p))
# print("result:", result)
if "error" in result:
if type(result["error"]) is str:
st.write(f'{result["error"]}.', end=" ")
if "estimated_time" in result:
st.write(f'Please try it again in about {result["estimated_time"]:.0f} seconds')
else:
if type(result["error"]) is list:
for error in result["error"]:
st.write(f'{error}')
else:
result = result[0]["generated_text"]
st.write(result.replace("\n", " \n"))
st.text("Translation")
translation = translate(result, "en", "id")
if lang == "id":
st.write(translation.replace("\n", " \n"))
else:
st.write(translate(result, 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)
# Reset state
session_state.prompt = None
session_state.prompt_box = None
session_state.text = None