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| import json | |
| import requests | |
| from mtranslate import translate | |
| from prompts import PROMPT_LIST | |
| import streamlit as st | |
| import random | |
| from transformers import GPT2Tokenizer, GPT2LMHeadModel | |
| import fasttext | |
| import SessionState | |
| LOGO = "huggingwayang.png" | |
| MODELS = { | |
| "GPT-2 Small": "flax-community/gpt2-small-indonesian", | |
| "GPT-2 Medium": "flax-community/gpt2-medium-indonesian", | |
| "GPT-2 Small Finetuned on Indonesian Journals": "Galuh/id-journal-gpt2" | |
| } | |
| headers = {} | |
| def load_gpt(model_type, text): | |
| print("Loading model...") | |
| model = GPT2LMHeadModel.from_pretrained(MODELS[model_type]) | |
| tokenizer = GPT2Tokenizer.from_pretrained(MODELS[model_type]) | |
| return model, tokenizer | |
| 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 | |
| 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=1.0, | |
| 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. | |
| """ | |
| ) | |
| model_name = st.selectbox('Model',(['GPT-2 Small', 'GPT-2 Medium', 'GPT-2 Small Finetuned on Indonesian Journals'])) | |
| 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 Journals"]: | |
| prompt_group_name = "Indonesian Journals" | |
| ALL_PROMPTS = list(PROMPT_LIST[prompt_group_name].keys())+["Custom"] | |
| prompt = st.selectbox('Prompt', ALL_PROMPTS, index=len(ALL_PROMPTS)-1) | |
| session_state = SessionState.get(prompt_box=None) | |
| if prompt == "Custom": | |
| prompt_box = "Enter your text here" | |
| else: | |
| prompt_box = random.choice(PROMPT_LIST[prompt_group_name][prompt]) | |
| session_state.prompt_box = prompt_box | |
| text = st.text_area("Enter text", session_state.prompt_box) | |
| if st.button("Run"): | |
| with st.spinner(text="Getting results..."): | |
| st.subheader("Result") | |
| model, tokenizer = load_gpt(model_name, text) | |
| input_ids = tokenizer.encode(text, return_tensors='pt') | |
| output = model.generate(input_ids=input_ids, | |
| max_length=max_len, | |
| temperature=temp, | |
| top_k=top_k, | |
| top_p=top_p, | |
| repetition_penalty=2.0) | |
| text = tokenizer.decode(output[0], | |
| skip_special_tokens=True) | |
| st.write(text.replace("\n", " \n")) | |
| st.text("Translation") | |
| translation = translate(text, "en", "id") | |