app.py

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"
    },
    "GPT-2 Medium finetuned on Indonesian stories": {
        "url": "https://api-inference.huggingface.co/models/cahya/gpt2-medium-indonesian-story"
    },
}

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")

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=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."
)

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 (disabled temporary).
    """
)

model_name = st.selectbox('Model',([
    'GPT-2 Small',
    'GPT-2 Medium',
    'GPT-2 Small finetuned on Indonesian academic journals',
    'GPT-2 Medium finetuned on Indonesian stories']))

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"
elif model_name in ["GPT-2 Medium finetuned on Indonesian stories"]:
    prompt_group_name = "Indonesian Stories"

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..."):
        if model_name in ["GPT-2 Medium finetuned on Indonesian stories"]:
            lang = "id"
            text = session_state.text
        else:
            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"))


        # Reset state
        session_state.prompt = None
        session_state.prompt_box = None
        session_state.text = None