app.py

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"))