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import json
import requests
from mtranslate import translate
from prompts import PROMPT_LIST
import streamlit as st
import random
headers = {}
MODELS = {
"GPT-2 Base": {
"url": "https://api-inference.huggingface.co/models/flax-community/gpt2-base-thai"
}
}
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="Thai GPT2 Demo")
st.title("🐘 Thai GPT2")
st.sidebar.subheader("Configurable parameters")
max_len = st.sidebar.text_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=0.1,
max_value=100.0,
help="The value used to module the next token probabilities."
)
top_k = st.sidebar.text_input(
"Top k",
value=50,
help="The number of highest probability vocabulary tokens to keep for top-k-filtering."
)
top_p = st.sidebar.text_input(
"Top p",
value=0.95,
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."
)
do_sample = st.sidebar.selectbox(
'Sampling?', (True, False), help="Whether or not to use sampling; use greedy decoding otherwise.")
st.markdown(
"""Thai GPT-2 demo. Part of the [Huggingface JAX/Flax event](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/)."""
)
model_name = st.selectbox('Model', (['GPT-2 Base']))
ALL_PROMPTS = list(PROMPT_LIST.keys())+["Custom"]
prompt = st.selectbox('Prompt', ALL_PROMPTS, index=len(ALL_PROMPTS)-1)
if prompt == "Custom":
prompt_box = "Enter your text here"
else:
prompt_box = random.choice(PROMPT_LIST[prompt])
text = st.text_area("Enter text", prompt_box)
if st.button("Run"):
with st.spinner(text="Getting results..."):
st.subheader("Result")
print(f"maxlen:{max_len}, temp:{temp}, top_k:{top_k}, top_p:{top_p}")
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"]}. 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("Thai 🇹🇭 to English 🇬🇧 translation")
st.write(translate(result, "en", "th").replace("\n", " \n"))
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