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| import streamlit as st | |
| print("Streamlit Version: ", st.__version__) | |
| import SessionState | |
| from mtranslate import translate | |
| from prompts import PROMPT_LIST | |
| import random | |
| import time | |
| from transformers import pipeline, set_seed | |
| import psutil | |
| import codecs | |
| import streamlit.components.v1 as stc | |
| import pathlib | |
| import os | |
| # st.set_page_config(page_title="Indonesian GPT-2") | |
| mirror_url = "https://gpt2-app.ai-research.id/" | |
| if "MIRROR_URL" in os.environ: | |
| mirror_url = os.environ["MIRROR_URL"] | |
| MODELS = { | |
| "Indonesian GPT-2 Small": { | |
| "group": "Indonesian GPT-2", | |
| "name": "indonesian-nlp/gpt2", | |
| "description": "The original Indonesian GPT-2 small model.", | |
| "text_generator": None | |
| }, | |
| "Indonesian GPT-2 Medium": { | |
| "group": "Indonesian GPT-2", | |
| "name": "indonesian-nlp/gpt2-medium-indonesian", | |
| "description": "The original Indonesian GPT-2 medium model.", | |
| "text_generator": None | |
| }, | |
| "Indonesian Literature - GPT-2 Small": { | |
| "group": "Indonesian Literature", | |
| "name": "cahya/gpt2-small-indonesian-story", | |
| "description": "The Indonesian Literature Generator using fine-tuned GPT-2 small model.", | |
| "text_generator": None | |
| }, | |
| "Indonesian Literature - GPT-2 Medium": { | |
| "group": "Indonesian Literature", | |
| "name": "cahya/gpt2-medium-indonesian-story", | |
| "description": "The Indonesian Literature Generator using fine-tuned GPT-2 medium model.", | |
| "text_generator": None | |
| }, | |
| "Indonesian Academic Journal - GPT-2 Small": { | |
| "group": "Indonesian Journal", | |
| "name": "Galuh/id-journal-gpt2", | |
| "description": "The Indonesian Journal Generator using fine-tuned GPT-2 small model.", | |
| "text_generator": None | |
| }, | |
| "Indonesian Persona Chatbot - GPT-2 Small": { | |
| "group": "Indonesian Persona Chatbot", | |
| "name": "cahya/gpt2-small-indonesian-personachat", | |
| "description": "The Indonesian Persona Chatbot using fine-tuned GPT-2 small model.", | |
| "text_generator": None | |
| }, | |
| "Multilingual mGPT": { | |
| "group": "Indonesian GPT-2", | |
| "name": "sberbank-ai/mGPT", | |
| "description": "Multilingual GPT model, autoregressive GPT-like models with 1.3 billion parameters.", | |
| "text_generator": None | |
| }, | |
| } | |
| def stc_chatbot(root_dir, width=700, height=900): | |
| html_file = root_dir/"app/chatbot.html" | |
| css_file = root_dir/"app/css/main.css" | |
| js_file = root_dir/"app/js/main.js" | |
| if css_file.exists() and js_file.exists(): | |
| html = codecs.open(html_file, "r").read() | |
| css = codecs.open(css_file, "r").read() | |
| js = codecs.open(js_file, "r").read() | |
| html = html.replace('<link rel="stylesheet" href="css/main.css">', "<style>\n" + css + "\n</style>") | |
| html = html.replace('<script src="js/main.js"></script>', "<script>\n" + js + "\n</script>") | |
| stc.html(html, width=width, height=height, scrolling=True) | |
| st.sidebar.markdown(""" | |
| <style> | |
| .centeralign { | |
| text-align: center; | |
| } | |
| </style> | |
| <p class="centeralign"> | |
| <img src="https://huggingface.co/spaces/flax-community/gpt2-indonesian/resolve/main/huggingwayang.png"/> | |
| </p> | |
| """, unsafe_allow_html=True) | |
| st.sidebar.markdown(f""" | |
| ___ | |
| <p class="centeralign"> | |
| This is a collection of applications that generates sentences using Indonesian GPT-2 models! | |
| </p> | |
| <p class="centeralign"> | |
| Created by <a href="https://huggingface.co/indonesian-nlp">Indonesian NLP</a> team @2021 | |
| <br/> | |
| <a href="https://github.com/indonesian-nlp/gpt2-app" target="_blank">GitHub</a> | <a href="https://github.com/indonesian-nlp/gpt2-app" target="_blank">Project Report</a> | |
| <br/> | |
| A mirror of the application is available <a href="{mirror_url}" target="_blank">here</a> | |
| </p> | |
| """, unsafe_allow_html=True) | |
| st.sidebar.markdown(""" | |
| ___ | |
| """, unsafe_allow_html=True) | |
| model = st.sidebar.selectbox('Model', (MODELS.keys())) | |
| def get_generator(model_name: str): | |
| st.write(f"Loading the GPT2 model {model_name}, please wait...") | |
| text_generator = pipeline('text-generation', model=model_name) | |
| return text_generator | |
| # Disable the st.cache for this function due to issue on newer version of streamlit | |
| # @st.cache(suppress_st_warning=True, hash_funcs={tokenizers.Tokenizer: id}) | |
| def process(text_generator, text: str, max_length: int = 100, do_sample: bool = True, top_k: int = 50, top_p: float = 0.95, | |
| temperature: float = 1.0, max_time: float = 120.0, seed=42, repetition_penalty=1.0): | |
| # st.write("Cache miss: process") | |
| set_seed(seed) | |
| if repetition_penalty == 0.0: | |
| min_penalty = 1.05 | |
| max_penalty = 1.5 | |
| repetition_penalty = max(min_penalty + (1.0-temperature) * (max_penalty-min_penalty), 0.8) | |
| result = text_generator(text, max_length=max_length, do_sample=do_sample, | |
| top_k=top_k, top_p=top_p, temperature=temperature, | |
| max_time=max_time, repetition_penalty=repetition_penalty) | |
| return result | |
| st.title("Indonesian GPT-2 Applications") | |
| prompt_group_name = MODELS[model]["group"] | |
| st.header(prompt_group_name) | |
| description = f"This application is a demo for {MODELS[model]['description']}" | |
| st.markdown(description) | |
| model_name = f"Model name: [{MODELS[model]['name']}](https://huggingface.co/{MODELS[model]['name']})" | |
| st.markdown(model_name) | |
| if prompt_group_name in ["Indonesian GPT-2", "Indonesian Literature", "Indonesian Journal"]: | |
| 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 = "" | |
| else: | |
| print(f"# prompt: {session_state.prompt}") | |
| print(f"# prompt_box: {session_state.prompt_box}") | |
| 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) | |
| max_length = st.sidebar.number_input( | |
| "Maximum length", | |
| value=100, | |
| max_value=512, | |
| help="The maximum length of the sequence to be generated." | |
| ) | |
| temperature = st.sidebar.slider( | |
| "Temperature", | |
| value=0.9, | |
| min_value=0.0, | |
| max_value=2.0 | |
| ) | |
| do_sample = st.sidebar.checkbox( | |
| "Use sampling", | |
| value=True | |
| ) | |
| top_k = 30 | |
| top_p = 0.95 | |
| if do_sample: | |
| top_k = st.sidebar.number_input( | |
| "Top k", | |
| value=top_k, | |
| help="The number of highest probability vocabulary tokens to keep for top-k-filtering." | |
| ) | |
| top_p = st.sidebar.number_input( | |
| "Top p", | |
| value=top_p, | |
| 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." | |
| ) | |
| seed = st.sidebar.number_input( | |
| "Random Seed", | |
| value=25, | |
| help="The number used to initialize a pseudorandom number generator" | |
| ) | |
| repetition_penalty = 0.0 | |
| automatic_repetition_penalty = st.sidebar.checkbox( | |
| "Automatic Repetition Penalty", | |
| value=True | |
| ) | |
| if not automatic_repetition_penalty: | |
| repetition_penalty = st.sidebar.slider( | |
| "Repetition Penalty", | |
| value=1.0, | |
| min_value=1.0, | |
| max_value=2.0 | |
| ) | |
| for group_name in MODELS: | |
| if MODELS[group_name]["group"] in ["Indonesian GPT-2", "Indonesian Literature", "Indonesian Journal"]: | |
| MODELS[group_name]["text_generator"] = get_generator(MODELS[group_name]["name"]) | |
| # text_generator = get_generator() | |
| if st.button("Run"): | |
| with st.spinner(text="Getting results..."): | |
| memory = psutil.virtual_memory() | |
| st.subheader("Result") | |
| time_start = time.time() | |
| # text_generator = MODELS[model]["text_generator"] | |
| result = process(MODELS[model]["text_generator"], text=session_state.text, max_length=int(max_length), | |
| temperature=temperature, do_sample=do_sample, | |
| top_k=int(top_k), top_p=float(top_p), seed=seed, repetition_penalty=repetition_penalty) | |
| time_end = time.time() | |
| time_diff = time_end-time_start | |
| result = result[0]["generated_text"] | |
| st.write(result.replace("\n", " \n")) | |
| st.text("Translation") | |
| translation = translate(result, "en", "id") | |
| st.write(translation.replace("\n", " \n")) | |
| # st.write(f"*do_sample: {do_sample}, top_k: {top_k}, top_p: {top_p}, seed: {seed}*") | |
| info = f""" | |
| *Memory: {memory.total/(1024*1024*1024):.2f}GB, used: {memory.percent}%, available: {memory.available/(1024*1024*1024):.2f}GB* | |
| *Text generated in {time_diff:.5} seconds* | |
| """ | |
| st.write(info) | |
| # Reset state | |
| session_state.prompt = None | |
| session_state.prompt_box = None | |
| session_state.text = None | |
| elif model.startswith("Indonesian Persona Chatbot"): | |
| root_dir = pathlib.Path(".") | |
| stc_chatbot(root_dir) | |