app.py

import json
import requests
from mtranslate import translate
from prompts import PROMPT_LIST
import streamlit as st
import random
import fasttext

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"
    },
}


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=0.1,
    max_value=100.0,
    help="The value used to module the next token probabilities."
)

top_k = st.sidebar.number_input(
    "Top k",
    value=10,
    help="The number of highest probability vocabulary tokens to keep for top-k-filtering."
)

top_p = st.sidebar.number_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(
    """
    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']))

ALL_PROMPTS = list(PROMPT_LIST.keys())+["Custom"]
prompt = st.selectbox('Please choose a predefined prompt or create your custom text.', ALL_PROMPTS, index=len(ALL_PROMPTS)-1)

if prompt == "Custom":
    prompt_box = "Feel free to write text in any language"
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..."):
        lang_predictions, lang_probability = ft_model.predict(text.replace("\n", " "), k=3)
        # print(f"lang: {lang_predictions}, {lang_probability}")
        if "__label__id" in lang_predictions:
            lang = "id"
        else:
            lang = lang_predictions[0].replace("__label__", "")
            text = translate(text, "id", lang)
        # print(f"{lang}: {text}")
        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"]}.', 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)