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| import json | |
| import random | |
| import requests | |
| from mtranslate import translate | |
| import streamlit as st | |
| MODEL_URL = "https://api-inference.huggingface.co/models/flax-community/papuGaPT2" | |
| PROMPT_LIST = { | |
| "Najsmaczniejszy owoc to...": ["Najsmaczniejszy owoc to "], | |
| "Cześć, mam na imię...": ["Cześć, mam na imię "], | |
| "Największym polskim poetą był...": ["Największym polskim poetą był "], | |
| } | |
| API_TOKEN = st.secrets['api_token'] | |
| headers = {"Authorization": f"Bearer {API_TOKEN}"} | |
| def query(payload, model_url): | |
| data = json.dumps(payload) | |
| print("model url:", model_url) | |
| response = requests.request( | |
| "POST", model_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": False, | |
| }, | |
| } | |
| return query(payload, model_name) | |
| # Page | |
| st.set_page_config(page_title="papuGaPT2 aka Polish GPT-2 model demo") | |
| st.title("papuGaPT2 aka Polish GPT-2") | |
| # Sidebar | |
| st.sidebar.subheader("Configurable parameters") | |
| max_len = st.sidebar.number_input( | |
| "Maximum length", | |
| value=100, | |
| max_value=250, | |
| help="The maximum length of the sequence to be generated.", | |
| ) | |
| temp = st.sidebar.slider( | |
| "Temperature", | |
| value=1.0, | |
| min_value=0.1, | |
| max_value=2.0, | |
| 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.", | |
| ) | |
| # Body | |
| st.markdown( | |
| """ | |
| This demo showcases the text generation capabilities of the papuGaPT2, a GPT2 model pre-trained from scratch using Polish subset of the multilingual Oscar corpus. | |
| To use it, add your text to 'Enter Text' box, or click one of the examples in 'Prompt' drop-down list to load them and click 'Run' button. The model will generate text based on the entered text (prompt). | |
| For more information including dataset, training and evaluation procedure, intended use, limitations and bias analysis see the [model card](https://huggingface.co/flax-community/papuGaPT2) | |
| In this demo, we used the template from the wonderful demo of [Spanish GPT2 team](https://huggingface.co/spaces/flax-community/spanish-gpt2) especially María Grandury and Manuel Romero. | |
| """ | |
| ) | |
| model_name = MODEL_URL | |
| ALL_PROMPTS = list(PROMPT_LIST.keys()) + ["Custom"] | |
| prompt = st.selectbox("Prompt", 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 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", "pl").replace("\n", " \n")) |