import json import requests from mtranslate import translate from prompts import PROMPT_LIST import streamlit as st import random token = st.secrets["flax_community_token"] headers = {"Authorization": f"Bearer {token}"} 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 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") # 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/). """ ) 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 = "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"]}.', 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("English translation") st.write(translate(result, "en", "id").replace("\n", " \n"))